Patent Application
20170364794
Embodiments of the invention relate to the field of traffic classification; and more specifically, to a method and apparatus for classifying traffic that has been encrypted.
Encryption is utilized to protect the content of data traffic as it traverses a wide area network, such as the Internet. Where encryption is performed at the endpoints of communication, i.e., the user device originating the data traffic and the destination device that receives the data traffic, intermediate devices have only minimum information about the data traffic that traverses them, usually little more than the destination address for a data packet, i.e., the payloads of the data packets are typically encrypted. The amount of such encrypted traffic is increasing due to increases in security concerns and ease at which robust encryption can be implemented at endpoint devices.
However, currently, intermediate devices often perform some level of packet inspection, i.e., examining the content of data packets received at an intermediate device, to facilitate efficient data traffic handling such as implementing quality of service processes and similar processes that can prioritize data traffic based on the type or classification of the data traffic (e.g., video streams, email, voice over Internet protocol). High levels of encrypted traffic can cause an issue for traffic management schemes that rely on traffic classification information. This is because most of the traffic classification schemes (e.g., Deep Packet Inspection (DPI)) rely on payload inspection to classify the traffic.
DPI is a technology used to inspect packets sent over the network by examining both the headers, referred to as shallow packet inspection (SPI), and payload (e.g. layer 7 information). DPI is used in real time to identify and analyze traffic flows based on their application type, content type, and other measurable parameters. More generally, traffic classification relies on seeing the payload of the packets. Therefore, payload encryption renders most of existing classification mechanisms inefficient at the least and completely ineffective in the worst case. SPI may still be possible but is limited. Thus, with the increase in encrypted traffic the efficiency of handling this information and the data traffic flows is diminished leading to overall traffic management performance degradation in the networks carrying the encrypted data traffic.
The embodiments include a method implemented by a network device to classify encrypted data traffic. The method identifies characteristics of the encrypted data traffic that have been modeled where network anomalies have been injected into the encrypted data traffic to provide additional traffic characteristics that enable categorization. The method includes receiving the encrypted data traffic, applying an encrypted traffic categorization model to the received encrypted traffic to determine a first categorization identification, and injecting an anomaly into the encrypted data traffic where the first categorization identification is not within a precision threshold. The method then applies the encrypted traffic categorization model to monitored encrypted traffic after injection of the anomaly to determine a second categorization identification, and applies the second categorization identification where the second categorization identification is within the precision threshold.
In a further embodiment a network device is configured to execute the method to classify encrypted data traffic. The network device includes a non-transitory computer-readable storage medium having stored therein an encrypted traffic categorizer, and a processor coupled to the non-transitory computer-readable storage medium. The processor is configured to execute the encrypted traffic categorizer. The encrypted traffic categorizer receives the encrypted data traffic, applies an encrypted traffic categorization model to the received encrypted traffic to determine a first categorization identification, injects an anomaly into the encrypted data traffic where the first categorization identification is not within a precision threshold, applies the encrypted traffic categorization model to monitored encrypted traffic after injection of the anomaly to determine a second categorization identification, and applies the second categorization identification where the second categorization identification is within the precision threshold.
In one embodiment, a computing device executes a plurality of virtual machines for implementing network function virtualization (NFV), wherein a virtual machine from the plurality of virtual machines is configured to execute the method to classify encrypted data traffic. The computing device including a non-transitory computer-readable storage medium having stored therein an encrypted traffic categorizer, and a processor coupled to the non-transitory computer-readable storage medium. The processor is configured to execute one of the plurality of virtual machines. The virtual machine executes the encrypted traffic categorizer. The encrypted traffic categorizer receives the encrypted data traffic, applies an encrypted traffic categorization model to the received encrypted traffic to determine a first categorization identification, injects an anomaly into the encrypted data traffic where the first categorization identification is not within a precision threshold, applies the encrypted traffic categorization model to monitored encrypted traffic after injection of the anomaly to determine a second categorization identification, and applies the second categorization identification where the second categorization identification is within the precision threshold.
In a further embodiment, a control plane device is configured to implement at least one centralized control plane for a software defined network (SDN). The centralized control plane is configured to execute the method to classify encrypted data traffic. The control plane device includes a non-transitory computer-readable storage medium having stored therein an encrypted traffic categorizer, and a processor coupled to the non-transitory computer-readable storage medium. The processor is configured to execute the encrypted traffic categorizer. The encrypted traffic categorizer receives the encrypted data traffic, applies an encrypted traffic categorization model to the received encrypted traffic to determine a first categorization identification, injects an anomaly into the encrypted data traffic where the first categorization identification is not within a precision threshold, applies the encrypted traffic categorization model to monitored encrypted traffic after injection of the anomaly to determine a second categorization identification, and applies the second categorization identification where the second categorization identification is within the precision threshold.
The invention may best be understood by referring to the following description and accompanying drawings that are used to illustrate embodiments of the invention. In the drawings:
The following description describes methods and apparatus for encrypted data traffic categorization to improve the handling of encrypted data traffic over a network by intermediate devices where the encryption is end-to-end encryption. The online process models the visible characteristics of known data traffic types and compares the visible characteristics of the incoming encrypted data traffic to a categorization model. To improve the accuracy of the categorization, the training system can inject anomalies into the encrypted data traffic and monitor the resulting encrypted data traffic flow characteristics and utilize these additional characteristics and the encrypted data traffic categorization models in the training system to determine the category of the encrypted traffic. This information is then used to update the categorization models in the online process when the latter is unable to categorize the encrypted data traffic accurately. The training system may generate encrypted data traffic categorization models offline by a process that injects anomalies into known data traffic and the encrypted data traffic and monitors the resulting characteristics of the encrypted data traffic flow before and after anomaly injection.
In the following description, numerous specific details such as logic implementations, opcodes, means to specify operands, resource partitioning/sharing/duplication implementations, types and interrelationships of system components, and logic partitioning/integration choices are set forth in order to provide a more thorough understanding of the present invention. It will be appreciated, however, by one skilled in the art that the invention may be practiced without such specific details. In other instances, control structures, gate level circuits and full software instruction sequences have not been shown in detail in order not to obscure the invention. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.
References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
Bracketed text and blocks with dashed borders (e.g., large dashes, small dashes, dot-dash, and dots) may be used herein to illustrate optional operations that add additional features to embodiments of the invention. However, such notation should not be taken to mean that these are the only options or optional operations, and/or that blocks with solid borders are not optional in certain embodiments of the invention.
In the following description and claims, the terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms are not intended as synonyms for each other. “Coupled” is used to indicate that two or more elements, which may or may not be in direct physical or electrical contact with each other, co-operate or interact with each other. “Connected” is used to indicate the establishment of communication between two or more elements that are coupled with each other.
The operations in the flow diagrams will be described with reference to the exemplary embodiments of the other figures. However, it should be understood that the operations of the flow diagrams can be performed by embodiments of the invention other than those discussed with reference to the other figures, and the embodiments of the invention discussed with reference to these other figures can perform operations different than those discussed with reference to the flow diagrams.
An electronic device stores and transmits (internally and/or with other electronic devices over a network) code (which is composed of software instructions and which is sometimes referred to as computer program code or a computer program) and/or data using machine-readable media (also called computer-readable media), such as machine-readable storage media (e.g., magnetic disks, optical disks, read only memory (ROM), flash memory devices, phase change memory) and machine-readable transmission media (also called a carrier) (e.g., electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals). Thus, an electronic device (e.g., a computer) includes hardware and software, such as a set of one or more processors coupled to one or more machine-readable storage media to store code for execution on the set of processors and/or to store data. For instance, an electronic device may include non-volatile memory containing the code since the non-volatile memory can persist code/data even when the electronic device is turned off (when power is removed), and while the electronic device is turned on that part of the code that is to be executed by the processor(s) of that electronic device is typically copied from the slower non-volatile memory into volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM)) of that electronic device. Typical electronic devices also include a set or one or more physical network interface(s) to establish network connections (to transmit and/or receive code and/or data using propagating signals) with other electronic devices. One or more parts of an embodiment of the invention may be implemented using different combinations of software, firmware, and/or hardware.
A network device (ND) is an electronic device that communicatively interconnects other electronic devices on the network (e.g., other network devices, end-user devices). Some network devices are “multiple services network devices” that provide support for multiple networking functions (e.g., routing, bridging, switching, Layer 2 aggregation, session border control, Quality of Service, and/or subscriber management), and/or provide support for multiple application services (e.g., data, voice, and video).
Overview
As discussed herein above, the increasing amount of encrypted data traffic causes an issue for traffic management schemes that rely on traffic classification information. Traffic classification schemes rely on information in the payload of data packets. When the data packets are encrypted this information is not available. Technologies that rely on payload information for data packets are often referred to as Deep Packet Inspection (DPI)) technologies, whereas technologies that only rely on header information are referred to as shallow packet inspection (SPI).
DPI is a technology used to inspect packets sent over the network by examining both the headers via SPI and payload information (e.g., layer 7 information). DPI is used in real time to identify and analyze unencrypted data traffic flows based on their application type, content type, and other measurable parameters. Payload encryption renders most of the existing classification mechanisms inefficient as they are based on DPI. SPI may still be possible to utilize for data packet classification, but the information available is limited.
The telecommunication industry and research community have an interest in solving this problem and finding a process whereby packet classification and the associated services and traffic management processes can be utilized with encrypted data traffic. Traffic management includes enforcements of the type called content-aware actions that do not work over encrypted traffic as they are realized today. Examples are video optimization or caching, redirect or parental control. For these enforcements, either network operators get access to the ‘clear’ unencrypted data traffic, which in many cases is not feasible, or these enforcements must be “reinvented.” The embodiments provide an alternative that enables relatively accurate encrypted data traffic categorization. The process relies on identifying characteristics of various types of data traffic that are unaffected by encryption such that data traffic that exhibits these characteristics can be reasonably expected to reliably identify respective encrypted data flow content type. In some embodiments, these content-aware enforcements are mostly, but not completely implemented in non-evolved packet core (EPC) products and with other embodiments the process may be adapted for other collaboration forms, for example, with content delivery networks (CDNs).
The telecommunication industry and researchers are exploring a wide variety of initiatives to address this problem that revolve around collaboration (i.e., collaboration between the network operators, network providers or user equipment manufacturers). This issue is one of the trends related to encryption and not only a challenge but an opportunity for network operators to add value. There are several possible routes for addressing the issue including offline and also online collaboration based on the definition of a number of application programming interfaces (APIs) addressing different types of use cases (UCs). The APIs can address getting assistance for service differentiation related to sponsored data, zero rating, and similar conditions. There is also another possible method for exchanging keys, or use digital certificate inspection to allow the network operator to access the content (or parts of it). This allows service aware content-aware actions. At the moment, these possible approaches seem difficult to argue and justify, and are likely to encounter public opposition and so these approaches are very unlikely to succeed.
Many network management and value added features that could be impacted by encryption, in particular end-to-end (e2e) encryption, are not yet commonly deployed, i.e. the problems with full e2e encryption are yet to be widely seen in networks. For many of the currently deployed features that could be impacted by encryption, there are varying levels of impairment from minimal impairment to complete impairment. Services that rely on DPI are significantly impacted.
The Internet Engineering Task Force (IETF) may review current third generation partnership project (3GPP) architectural use of content-based classification for radio resource allocation and may discuss potential solutions to the classification of encrypted traffic such as zero-bit active queue management (AQM) alternative, 1-bit alternative signal, modern differentiated services code point (DSCP marking). Having end-point over the tops (OTTs) expose classification of their payload may not be fully viable as end point applications could have various incentives to misguide the network. Similar problems have been seen with SPI mechanisms that for example used knowledge of transport control protocol (TCP)/user datagram (UDP) ports to identify an application. Therefore, any solution relying on end-points (OTTs) to send classification information should also have means to verify that information, the embodiments could be applied to assist in such verification.
Traffic classification has also been studied in academic work and some of this work either does not rely on payload data or is designed to circumvent the problem caused by encryption. Below some of this work is discussed, however, none of these techniques are used in production as most of them rely on changes in current network architecture or in current applications, which makes their adoption non-trivial. The embodiments offer an alternative to these techniques which is more easily implemented in existing architectures, but which can be used in combination with some of these techniques.
Machine learning (e.g., via use of or in combination with fingerprinting) is one such technique. Machine learning (ML) can use decision trees to identify data packets by their size distribution, TCP window sizes, TCP flag bits, packet directions from IP packet headers and similar information to classify unencrypted or encrypted data traffic. Other possible solutions posit that data traffic categorization either allow for middlebox DPI functionality or payload encryption, but the processes cannot accommodate both. To address this, the processes provide a new encryption algorithm which allows keeping the encryption on the payload but also allows middleboxes to carry out their DPI functionalities. The approach is for the DPI to perform the inspection directly on the encrypted traffic. The processes propose a new searchable encryption scheme and the detection algorithm which allows for fast packet inspection. Some processes have been based on secure socket layer (SSL) interception to proxy and decrypt the traffic at the middlebox, but these processes have not yet been worked out to be secure. Thus, end users seeking e2e encryption are not likely to be willing to utilize such searchable encryption schemes.
The embodiments overcome the limitations of the prior art. The prior art processes and schemes as set forth above are either unsecure (i.e., they decrypt the traffic at the middleboxes) or cannot classify encrypted data traffic accurately. A certificate inspection method is limited too as different services can share the same certificate. Thus, there is no technique or process in the prior art that is able to validate if information provided by OTTs about the encrypted traffic type is accurate.
The embodiments of the invention overcome these limitations of the prior art. The embodiments provide a process that is able to determine or validate the content information of encrypted traffic flows. The process may utilize quality of service (QoS) management functionality that may be implemented in common network nodes (e.g., traffic management nodes for throttling etc.) to inject for example latency, and/or jitter, and/or packet loss and/or packet shuffling and/or connection (e.g., transport control protocol (TCP)) reset and/or throttling or similar anomaly and then, monitor the reaction of the traffic flow in response to the anomaly. The monitored reaction could consist in numerous changes in the data traffic flow including changes in throughput, packet size, packet inter-arrival time, connection was reset, statistical values of the above, and similar changes to data traffic flow.
The monitored reaction (i.e., monitored traffic characteristics after anomaly injection) is used to determine the traffic type of the encrypted flow. This identification is based on a training a categorization model (e.g., using machine learning such as neural networks or complex multi machine language algorithm systems) and using test traffic to train it to be able to match the encrypted traffic's characteristics to an application type. The encrypted data traffic's characteristics could be based on what is visible in packet headers and measured traffic characteristics before the anomaly injection, the injected anomaly (e.g. latency or similar anomaly) and what is visible in packet headers and the measured characteristics of the encrypted traffic after the injected anomaly. In some embodiments, test traffic may be generated offline by collecting data packets from known applications and transmitting them over a closed or simulated network where the reaction of the data traffic to various network anomalies can be monitored.
Once this training system is created and its models are built, the resulting models can be used by the online system in the following manner. The online system is used to map encrypted traffic's basic characteristics (e.g., the visible characteristics) to an application. If the online system cannot do this mapping within given accuracy limits, it triggers anomaly injection on the encrypted data traffic to be identified and uses the categorization models from the training system to determine the traffic type. This information is used again to update the mapping model of the online system. This utilization of anomalies is for a short period of time on a small portion of the overall data traffic being categorized.
The embodiments provide advantages over the prior art. The embodiments enable the categorization and/or identification of the application type of e2e encrypted traffic, without having to break the encryption, and without requiring any protocol changes (e.g. carrying application info on packet headers which could lead to dishonest marking of packets by end points). In some embodiments, such as the latter case, the embodiments can be used to verify, i.e., detect dishonest end-point markings. The embodiments provide a process that can learn new applications mappings as they are discovered or computed. The embodiments can also be mixed with other techniques to form more accurate traffic classification techniques.
A UE 101 may be a smartphone, laptop, personal computer, handheld device, console device or similar computing device. The UE 101 can be in communication with another computing device 113 via the network, where the network can be a wide area network, such as the Internet or similar network. The network can include any number of intermediate devices that include network devices of a cellular network or similar network devices. In the example embodiment, the UE 101 is a cellular device such as a smartphone that communicates with the network via a base station such as via an eNodeB 103 of an evolved packet core (EPC) 107 or similar network device. The eNodeB 103 can be in communication with the EPC 107 via a mobile backhaul network 105, which is a set of intermediate network devices between the eNodeB 103 and the EPC 107 including a set of gateway network devices and similar network devices.
Traffic management may be implemented by network devices in the EPC 107. The traffic management can include DPI based services 109 and encrypted traffic categorization services 117. DPI and similar services 109 can be employed to manage services that operate over data traffic that is unencrypted. Whereas, encrypted data traffic has inaccessible payloads that rely on the encrypted traffic categorization services 117 of the embodiments presented herein.
The EPC 107 can connect with a WAN such as the Internet or similar additional set of networking devices that enable the end to end path 115 to reach from the UE 101 to the other endpoint computing device 113. In other embodiments, the other endpoint computing device 113 may be part of the same network including the EPC 107. The end to end (e2e) path 115 is in this example an e2e encrypted data flow that is managed by encryption communication software at each endpoint that renders the payload of the data traffic exchanged by the two endpoints inaccessible to all intermediate network devices thereby making it impossible to utilize DPI to categorize the data traffic and apply traffic management enforcements for the EPC 107 or other aspects of the network.
The embodiments are described with the example of the use of machine learning (ML), but can be implemented with other techniques as well. The principle and the focus of the embodiments is in the input data set fed into the ‘black box’ system (e.g., a ML based). The embodiments enable the feeding of the encrypted traffic characteristics into an ML system and based on clustering and similar information a determination of its classification. The embodiments augment those techniques and involve generating extra input by first injecting network anomaly on the data traffic flow for a given duration and measuring the end to end behavior of the data traffic flow after the anomaly. This e2e behavior is to differ depending on the application and hence will be a method or means to determine the application type (e.g. VoLTE, Skype, Whatsapp call, Viber call, Netflix Video streaming, Hulu video streaming, Amazon video streaming, web browsing traffic, file download, and similar application data traffic types).
The offline system referred to herein as the training system generates a categorization model that is trained initially with test traffic as shown in Step [a] (Block 201) of
The training system process and the online system processes are broken down and described in further description in relation to
The training system (301) implements the phase [a] described above, where the required steps to implement training of a model are described with relation to
The machine learning implemented by the training system (i.e., the black box view) determines the traffic type and characteristics based on its behavior (i.e. traffic characteristics) before and after network anomaly injection. Inputs (303) into the process include traffic characteristics (e.g., at time windows T and T+ Delta), these traffic characteristics may include L0-L4 header information, throughput measurement, packet size, packet inter-arrival time, whether a connection was reset (e.g., at time T+Delta), and similar characteristics. Delta is the time during which the anomaly is injected and can vary depending on the anomaly type and application type. In some embodiments, for a given anomaly time different Deltas may be tried as different applications may demonstrate a reaction at varying Delta times.
Similarly, the inputs into the training of the model by the black box (e.g., the ML process) for the anomaly injections may include monitored reactions to anyone or more of latency, jitter, packet loss, packet shuffling, connection types (e.g. TCP), throttling (i.e. using active queue management (AQM), e.g. via a leaky bucket algorithm) and similar traffic modifications and monitored characteristics.
The output (305) from the black box process can be an identification or mapping of the input traffic (303) to traffic or application type. The output can more specifically include traffic types such as a voice call (e.g., VoLTE, Skype, Whatsapp call, Viber call), a video call (e.g., FaceTime, Skype, Whatsapp call, Viber call), video streaming (e.g., Netflix, Hulu, Amazon), web browsing traffic (e.g., browser type), file download, or similar application types. The output can also include traffic characteristics information, such as traffic characteristics other than those measurable (application parameters hidden by encryption, and similar information (e.g. accuracy estimate of the mapping, how close it is to data used for training etc.). This output can be a mapping, matrix or similar format that serves as a categorization model to be provided to the online system.
Further, the training system can be fed errors (after filtering special cases, e.g. anomalous traffic that is not to be learned) from the input/output and monitored traffic data (309). The output can be compared or combined with expected output information (307). Known output from unencrypted traffic types or application data can be used to make the mappings of the input and output information to specific traffic and application types. Feedback may be used in embodiments where backpropagation NN type learning processes are employed in the training system. Once a categorization model is used by the online system the feedback about errors in its predictions may be refined.
The process selects one of the sets of test traffic to process (Block 403). The selected test traffic can then be input into a test network, which may be a real controlled test network or a simulated test network (Block 405). The result of injecting the test traffic into the test network is then observed and measured (Block 407). The results can be measured in terms of any type of traffic characteristics as set forth above. The observed traffic characteristics are associated with the traffic or application type. Then the process selects a set of anomalies to test (Block 409). Anomalies can be injected by type one at a time or in any combination into the test network along with the test traffic (Block 411). The anomalies could be of different durations (i.e. same anomaly could be tested for different durations). The process then monitors and measures the traffic characteristics of the data traffic that result from the injected anomaly (Block 413). A check may be made whether all anomalies to be tested have been exhausted (Block 415). If all of the anomalies have not been exhausted, then the process selects the next set of anomalies to test (Block 409). If all of the anomalies have been tested, then a check is made whether all the different traffic types to be tested have been tested (Block 417).
If all traffic types have not been tested, then the next traffic type is selected (Block 403). If all of the traffic types have been tested, then the process records the mapping or correlations as part of the training of the encrypted traffic categorization model (Block 419). The encrypted traffic categorization model can then be forwarded to the online system for use in operation of identifying encrypted data traffic.
In some embodiments, the online system takes the following input into its process, which can include ML processes. The inputs may include the traffic characteristics of the received data traffic including L0-L4 header information, throughput, packet size, packet inter-arrival time, and similar information (503). The output of this process can be a categorization of the received data traffic (505), for example an identification of a traffic type such as a voice call (e.g., VoLTE, Skype, Whatsapp call, Viber call), video call (e.g., FaceTime, Skype, Whatsapp call, Viber call), video streaming (e.g., Netflix, Hulu, Amazon), web browsing traffic (e.g., by browser type), file download, and similar traffic or application type. Additional output can include traffic characteristics, where the traffic characteristics may be other than those measurable (e.g., application parameters hidden by encryption and similar characteristics), estimate error (e.g., the estimated error on the output (to be used by Step [c]), or other traffic categorization information such as other statistical information that could be application specific and used for other means. In addition, the process may be fed back information on errors (507) after filtering out special cases such as non-representation cases. The output categorization can be compared to expected output to verify accuracy or for similar purposes (509).
Further processing can include checking if the encrypted traffic categorization models could identify the traffic type based on its behavior to the network anomaly that was injected. If not (i.e. where step [e]: no), then the data traffic flow and associated information is sent to Step [a] to trigger the offline process of retraining by the training system (e.g., by human operator or an automated process).
If the first categorization identification is not within a precision threshold, then the process injects a set of anomalies into the encrypted data traffic (Block 609). The encrypted data traffic with the anomalies is then monitored and then a second categorization identification is made based on the encrypted traffic categorization model (Block 611). A check is made whether the second categorization identification is within a precision threshold y (Block 613). If the second categorization identification is within the precision threshold, then this categorization is applied for traffic management purposes (Block 615). If the categorization can be utilized to update the encrypted traffic categorization model, then the encrypted traffic categorization model is updated before the process completes (Block 617). If the categorization is not within the precision threshold, then the encrypted data traffic information can be provided to the training system to update the encrypted traffic categorization model and improve the categorization precision (Step [e]:no) (Block 619). Once the training system has further processed the traffic information, then an updated encrypted traffic categorization model may be returned for use in the online system (Block 621).
The embodiments have advantages over the prior art. The processes of the embodiments are not performance hindering since the network anomaly injection can be controlled such that it does not cause service performance deterioration for end users. During training most of the test traffic is generated with fake end points or collected traces. Moreover, for live production traffic, anomaly injection is required only when the characteristics of the encrypted data traffic flow does not map to past learnings. Therefore, not all data traffic is constantly subject to network anomaly injection. Further, the embodiments minimize the problem of a malicious end point. Obfuscation (hiding or disguising information to prevent detection) can be limited to cases where the application changes its behavior constantly to prevent the processes from learning the data traffic behavior. This is considered as a rare case, and if the same application switches between a few known behavior models, a well-defined machine learning system should be able to learn them all. For encrypted data flows of very short duration that could not be identified by online system, the process can redirect the traffic information to the training system. This may require capture of much of the data flow as if there is too much delay then the training system may not have enough time because the flow does not exist anymore.
Architecture
Two of the exemplary ND implementations in
The special-purpose network device 702 includes networking hardware 710 comprising compute resource(s) 712 (which typically include a set of one or more processors), forwarding resource(s) 714 (which typically include one or more ASICs and/or network processors), and physical network interfaces (NIs) 716 (sometimes called physical ports), as well as non-transitory machine readable storage media 718 having stored therein networking software 720. A physical NI is hardware in a ND through which a network connection (e.g., wirelessly through a wireless network interface controller (WNIC) or through plugging in a cable to a physical port connected to a network interface controller (NIC)) is made, such as those shown by the connectivity between NDs 700A-H. During operation, the networking software 720 may be executed by the networking hardware 710 to instantiate a set of one or more networking software instance(s) 722. Each of the networking software instance(s) 722, and that part of the networking hardware 710 that executes that network software instance (be it hardware dedicated to that networking software instance and/or time slices of hardware temporally shared by that networking software instance with others of the networking software instance(s) 722), form a separate virtual network element 730A-R. Each of the virtual network element(s) (VNEs) 730A-R includes a control communication and configuration module 732A-R (sometimes referred to as a local control module or control communication module) and forwarding table(s) 734A-R, such that a given virtual network element (e.g., 730A) includes the control communication and configuration module (e.g., 732A), a set of one or more forwarding table(s) (e.g., 734A), and that portion of the networking hardware 710 that executes the virtual network element (e.g., 730A).
The special-purpose network device 702 is often physically and/or logically considered to include: 1) a ND control plane 724 (sometimes referred to as a control plane) comprising the compute resource(s) 712 that execute the control communication and configuration module(s) 732A-R; and 2) a ND forwarding plane 726 (sometimes referred to as a forwarding plane, a data plane, or a media plane) comprising the forwarding resource(s) 714 that utilize the forwarding table(s) 734A-R and the physical NIs 716. By way of example, where the ND is a router (or is implementing routing functionality), the ND control plane 724 (the compute resource(s) 712 executing the control communication and configuration module(s) 732A-R) is typically responsible for participating in controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) and storing that routing information in the forwarding table(s) 734A-R, and the ND forwarding plane 726 is responsible for receiving that data on the physical NIs 716 and forwarding that data out the appropriate ones of the physical NIs 716 based on the forwarding table(s) 734A-R.
Returning to
The instantiation of the one or more sets of one or more applications 764A-R and 766A-R, as well as virtualization if implemented, are collectively referred to as software instance(s) 752. Each set of applications 764A-R and 766A-R, corresponding virtualization construct (e.g., instance 762A-R) if implemented, and that part of the hardware 740 that executes them (be it hardware dedicated to that execution and/or time slices of hardware temporally shared), forms a separate virtual network element(s) 760A-R.
The virtual network element(s) 760A-R perform similar functionality to the virtual network element(s) 730A-R—e.g., similar to the control communication and configuration module(s) 732A and forwarding table(s) 734A (this virtualization of the hardware 740 is sometimes referred to as network function virtualization (NFV)). Thus, NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which could be located in Data centers, NDs, and customer premise equipment (CPE). While embodiments of the invention are illustrated with each instance 762A-R corresponding to one VNE 760A-R, alternative embodiments may implement this correspondence at a finer level granularity (e.g., line card virtual machines virtualize line cards, control card virtual machine virtualize control cards, etc.); it should be understood that the techniques described herein with reference to a correspondence of instances 762A-R to VNEs also apply to embodiments where such a finer level of granularity and/or unikernels are used.
In certain embodiments, the virtualization layer 754 includes a virtual switch that provides similar forwarding services as a physical Ethernet switch. Specifically, this virtual switch forwards traffic between instances 762A-R and the NIC(s) 744, as well as optionally between the instances 762A-R; in addition, this virtual switch may enforce network isolation between the VNEs 760A-R that by policy are not permitted to communicate with each other (e.g., by honoring virtual local area networks (VLANs)).
The third exemplary ND implementation in
Regardless of the above exemplary implementations of an ND, when a single one of multiple VNEs implemented by an ND is being considered (e.g., only one of the VNEs is part of a given virtual network) or where only a single VNE is currently being implemented by an ND, the shortened term network element (NE) is sometimes used to refer to that VNE. Also in all of the above exemplary implementations, each of the VNEs (e.g., VNE(s) 730A-R, VNEs 760A-R, and those in the hybrid network device 706) receives data on the physical NIs (e.g., 716, 746) and forwards that data out the appropriate ones of the physical NIs (e.g., 716, 746). For example, a VNE implementing IP router functionality forwards IP packets on the basis of some of the IP header information in the IP packet; where IP header information includes source IP address, destination IP address, source port, destination port (where “source port” and “destination port” refer herein to protocol ports, as opposed to physical ports of a ND), transport protocol (e.g., user datagram protocol (UDP), Transmission Control Protocol (TCP), and differentiated services code point (DSCP) values.
The NDs of
A virtual network is a logical abstraction of a physical network (such as that in
A network virtualization edge (NVE) sits at the edge of the underlay network and participates in implementing the network virtualization; the network-facing side of the NVE uses the underlay network to tunnel frames to and from other NVEs; the outward-facing side of the NVE sends and receives data to and from systems outside the network. A virtual network instance (VNI) is a specific instance of a virtual network on a NVE (e.g., a NE/VNE on an ND, a part of a NE/VNE on a ND where that NE/VNE is divided into multiple VNEs through emulation); one or more VNIs can be instantiated on an NVE (e.g., as different VNEs on an ND). A virtual access point (VAP) is a logical connection point on the NVE for connecting external systems to a virtual network; a VAP can be physical or virtual ports identified through logical interface identifiers (e.g., a VLAN ID).
Examples of network services include: 1) an Ethernet LAN emulation service (an Ethernet-based multipoint service similar to an Internet Engineering Task Force (IETF) Multiprotocol Label Switching (MPLS) or Ethernet VPN (EVPN) service) in which external systems are interconnected across the network by a LAN environment over the underlay network (e.g., an NVE provides separate L2 VNIs (virtual switching instances) for different such virtual networks, and L3 (e.g., IP/MPLS) tunneling encapsulation across the underlay network); and 2) a virtualized IP forwarding service (similar to IETF IP VPN (e.g., Border Gateway Protocol (BGP)/MPLS IPVPN) from a service definition perspective) in which external systems are interconnected across the network by an L3 environment over the underlay network (e.g., an NVE provides separate L3 VNIs (forwarding and routing instances) for different such virtual networks, and L3 (e.g., IP/MPLS) tunneling encapsulation across the underlay network)). Network services may also include quality of service capabilities (e.g., traffic classification marking, traffic conditioning and scheduling), security capabilities (e.g., filters to protect customer premises from network—originated attacks, to avoid malformed route announcements), and management capabilities (e.g., full detection and processing).
For example, where the special-purpose network device 702 is used, the control communication and configuration module(s) 732A-R of the ND control plane 724 typically include a reachability and forwarding information module to implement one or more routing protocols (e.g., an exterior gateway protocol such as Border Gateway Protocol (BGP), Interior Gateway Protocol(s) (IGP) (e.g., Open Shortest Path First (OSPF), Intermediate System to Intermediate System (IS-IS), Routing Information Protocol (RIP), Label Distribution Protocol (LDP), Resource Reservation Protocol (RSVP) (including RSVP-Traffic Engineering (TE): Extensions to RSVP for LSP Tunnels and Generalized Multi-Protocol Label Switching (GMPLS) Signaling RSVP-TE)) that communicate with other NEs to exchange routes, and then selects those routes based on one or more routing metrics. Thus, the NEs 770A-H (e.g., the compute resource(s) 712 executing the control communication and configuration module(s) 732A-R) perform their responsibility for participating in controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) by distributively determining the reachability within the network and calculating their respective forwarding information. Routes and adjacencies are stored in one or more routing structures (e.g., Routing Information Base (RIB), Label Information Base (LIB), one or more adjacency structures) on the ND control plane 724. The ND control plane 724 programs the ND forwarding plane 726 with information (e.g., adjacency and route information) based on the routing structure(s). For example, the ND control plane 724 programs the adjacency and route information into one or more forwarding table(s) 734A-R (e.g., Forwarding Information Base (FIB), Label Forwarding Information Base (LFIB), and one or more adjacency structures) on the ND forwarding plane 726. For layer 2 forwarding, the ND can store one or more bridging tables that are used to forward data based on the layer 2 information in that data. While the above example uses the special-purpose network device 702, the same distributed approach 772 can be implemented on the general purpose network device 704 and the hybrid network device 706.
For example, where the special-purpose network device 702 is used in the data plane 780, each of the control communication and configuration module(s) 732A-R of the ND control plane 724 typically include a control agent that provides the VNE side of the south bound interface 782. In this case, the ND control plane 724 (the compute resource(s) 712 executing the control communication and configuration module(s) 732A-R) performs its responsibility for participating in controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) through the control agent communicating with the centralized control plane 776 to receive the forwarding information (and in some cases, the reachability information) from the centralized reachability and forwarding information module 779 (it should be understood that in some embodiments of the invention, the control communication and configuration module(s) 732A-R, in addition to communicating with the centralized control plane 776, may also play some role in determining reachability and/or calculating forwarding information—albeit less so than in the case of a distributed approach; such embodiments are generally considered to fall under the centralized approach 774, but may also be considered a hybrid approach).
While the above example uses the special-purpose network device 702, the same centralized approach 774 can be implemented with the general purpose network device 704 (e.g., each of the VNE 760A-R performs its responsibility for controlling how data (e.g., packets) is to be routed (e.g., the next hop for the data and the outgoing physical NI for that data) by communicating with the centralized control plane 776 to receive the forwarding information (and in some cases, the reachability information) from the centralized reachability and forwarding information module 779; it should be understood that in some embodiments of the invention, the VNEs 760A-R, in addition to communicating with the centralized control plane 776, may also play some role in determining reachability and/or calculating forwarding information—albeit less so than in the case of a distributed approach) and the hybrid network device 706. In fact, the use of SDN techniques can enhance the NFV techniques typically used in the general purpose network device 704 or hybrid network device 706 implementations as NFV is able to support SDN by providing an infrastructure upon which the SDN software can be run, and NFV and SDN both aim to make use of commodity server hardware and physical switches.
While
While
On the other hand,
While some embodiments of the invention implement the centralized control plane 776 as a single entity (e.g., a single instance of software running on a single electronic device), alternative embodiments may spread the functionality across multiple entities for redundancy and/or scalability purposes (e.g., multiple instances of software running on different electronic devices).
Similar to the network device implementations, the electronic device(s) running the centralized control plane 776, and thus the network controller 778 including the centralized reachability and forwarding information module 779, may be implemented a variety of ways (e.g., a special purpose device, a general-purpose (e.g., COTS) device, or hybrid device). These electronic device(s) would similarly include compute resource(s), a set or one or more physical NICs, and a non-transitory machine-readable storage medium having stored thereon the centralized control plane software. For instance,
In embodiments that use compute virtualization, the processor(s) 842 typically execute software to instantiate a virtualization layer 854 (e.g., in one embodiment the virtualization layer 854 represents the kernel of an operating system (or a shim executing on a base operating system) that allows for the creation of multiple instances 862A-R called software containers (representing separate user spaces and also called virtualization engines, virtual private servers, or jails) that may each be used to execute a set of one or more applications; in another embodiment the virtualization layer 854 represents a hypervisor (sometimes referred to as a virtual machine monitor (VMM)) or a hypervisor executing on top of a host operating system, and an application is run on top of a guest operating system within an instance 862A-R called a virtual machine (which in some cases may be considered a tightly isolated form of software container) that is run by the hypervisor; in another embodiment, an application is implemented as a unikernel, which can be generated by compiling directly with an application only a limited set of libraries (e.g., from a library operating system (LibOS) including drivers/libraries of OS services) that provide the particular OS services needed by the application, and the unikernel can run directly on hardware 840, directly on a hypervisor represented by virtualization layer 854 (in which case the unikernel is sometimes described as running within a LibOS virtual machine), or in a software container represented by one of instances 862A-R). Again, in embodiments where compute virtualization is used, during operation an instance of the CCP software 850 (illustrated as CCP instance 876A) is executed (e.g., within the instance 862A) on the virtualization layer 854. In embodiments where compute virtualization is not used, the CCP instance 876A is executed, as a unikernel or on top of a host operating system, on the “bare metal” general purpose control plane device 804. The instantiation of the CCP instance 876A, as well as the virtualization layer 854 and instances 862A-R if implemented, are collectively referred to as software instance(s) 852.
In some embodiments, the CCP instance 876A includes a network controller instance 878. The network controller instance 878 includes a centralized reachability and forwarding information module instance 879 (which is a middleware layer providing the context of the network controller 778 to the operating system and communicating with the various NEs), and an CCP application layer 880 (sometimes referred to as an application layer) over the middleware layer (providing the intelligence required for various network operations such as protocols, network situational awareness, and user-interfaces). At a more abstract level, this CCP application layer 880 within the centralized control plane 776 works with virtual network view(s) (logical view(s) of the network) and the middleware layer provides the conversion from the virtual networks to the physical view. Applications can in some embodiments be executed by the network controller instance in the CCP application layer 880 or in a similar manner. The applications can include the encrypted traffic categorizer 881 and/or the encrypted traffic categorization model trainer 883. In other embodiments, these components may be implemented in the centralized control plane 876.
The centralized control plane 776 transmits relevant messages to the data plane 780 based on CCP application layer 880 calculations and middleware layer mapping for each flow. A flow may be defined as a set of packets whose headers match a given pattern of bits; in this sense, traditional IP forwarding is also flow-based forwarding where the flows are defined by the destination IP address for example; however, in other implementations, the given pattern of bits used for a flow definition may include more fields (e.g., 10 or more) in the packet headers. Different NDs/NEs/VNEs of the data plane 780 may receive different messages, and thus different forwarding information. The data plane 780 processes these messages and programs the appropriate flow information and corresponding actions in the forwarding tables (sometime referred to as flow tables) of the appropriate NE/VNEs, and then the NEs/VNEs map incoming packets to flows represented in the forwarding tables and forward packets based on the matches in the forwarding tables.
Standards such as OpenFlow define the protocols used for the messages, as well as a model for processing the packets. The model for processing packets includes header parsing, packet classification, and making forwarding decisions. Header parsing describes how to interpret a packet based upon a well-known set of protocols. Some protocol fields are used to build a match structure (or key) that will be used in packet classification (e.g., a first key field could be a source media access control (MAC) address, and a second key field could be a destination MAC address).
Packet classification involves executing a lookup in memory to classify the packet by determining which entry (also referred to as a forwarding table entry or flow entry) in the forwarding tables best matches the packet based upon the match structure, or key, of the forwarding table entries. It is possible that many flows represented in the forwarding table entries can correspond/match to a packet; in this case the system is typically configured to determine one forwarding table entry from the many according to a defined scheme (e.g., selecting a first forwarding table entry that is matched). Forwarding table entries include both a specific set of match criteria (a set of values or wildcards, or an indication of what portions of a packet should be compared to a particular value/values/wildcards, as defined by the matching capabilities—for specific fields in the packet header, or for some other packet content), and a set of one or more actions for the data plane to take on receiving a matching packet. For example, an action may be to push a header onto the packet, for the packet using a particular port, flood the packet, or simply drop the packet. Thus, a forwarding table entry for IPv4/IPv6 packets with a particular transmission control protocol (TCP) destination port could contain an action specifying that these packets should be dropped.
Making forwarding decisions and performing actions occurs, based upon the forwarding table entry identified during packet classification, by executing the set of actions identified in the matched forwarding table entry on the packet.
However, when an unknown packet (for example, a “missed packet” or a “match-miss” as used in OpenFlow parlance) arrives at the data plane 780, the packet (or a subset of the packet header and content) is typically forwarded to the centralized control plane 776. The centralized control plane 776 will then program forwarding table entries into the data plane 780 to accommodate packets belonging to the flow of the unknown packet. Once a specific forwarding table entry has been programmed into the data plane 780 by the centralized control plane 776, the next packet with matching credentials will match that forwarding table entry and take the set of actions associated with that matched entry.
While the invention has been described in terms of several embodiments, those skilled in the art will recognize that the invention is not limited to the embodiments described, can be practiced with modification and alteration within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting.
