Patent Application
20160219066
The present disclosure relates generally to computer networks, and, more particularly, to correlating network events by correlating local graph models from distributed nodes in the network.
One type of network attack that is of particular concern in the context of computer networks is a Denial of Service (DoS) attack. In general, the goal of a DoS attack is to prevent legitimate use of the services available on the network. For example, a DoS jamming attack may artificially introduce interference into the network, thereby causing collisions with legitimate traffic and preventing message decoding. In another example, a DoS attack may attempt to overwhelm the network's resources by flooding the network with requests, to prevent legitimate requests from being processed. A DoS attack may also be distributed, to conceal the presence of the attack. For example, a distributed DoS (DDoS) attack may involve multiple attackers sending malicious requests, making it more difficult to distinguish when an attack is underway. When viewed in isolation, a particular one of such a request may not appear to be malicious. However, in the aggregate, the requests may overload a resource, thereby impacting legitimate requests sent to the resource.
Botnets represent one way in which a DDoS attack may be launched against a network. In a botnet, a subset of the network devices may be infected with malicious software, thereby allowing the devices in the botnet to be controlled by a single master. Using this control, the master can then coordinate the attack against a given network resource.
The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:
According to one or more embodiments of the disclosure, a device in a network receives an indication of a network anomaly detected by a first graph-based anomaly detection model hosted by a first node in the network. The device identifies one or more additional graph-based anomaly detection models based on the network anomaly detected by the first graph-based anomaly detection model. The device correlates one or more network events from the one or more additional graph-based anomaly detection models with the network anomaly detected by the first graph-based anomaly detection model. The device identifies a cause of the network anomaly using the one or more network events from the one or more additional graph-based anomaly detection models that are correlated with the network anomaly detected by the first graph-based anomaly detection model.
In further embodiments, a first device in a network maintains a graph-based anomaly detection model for a set of nodes in the network. The first device detects a network anomaly using the graph-based anomaly detection model. The first device reports the detected network anomaly to a second device. The first device provides data regarding the graph-based anomaly detection model for the set of nodes to the second device.
A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network.
Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or PLC networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port such as PLC, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.
In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN thanks to a carrier network, via one or more links exhibiting very different network and service level agreement (SLA) characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:
1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/LTE backup connection). For example, a particular CE router 110 shown in network 100 may support a given customer site, potentially also with a backup link, such as a wireless connection.
2.) Site Type B: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/LTE connection). A site of type B may itself be of different types:
2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different Service Providers), with potentially a backup link (e.g., a 3G/4G/LTE connection).
2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/LTE connection). For example, a particular customer site may be connected to network 100 via PE-3 and via a separate Internet connection, potentially also with a wireless backup link.
2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potential a backup link (e.g., a 3G/4G/LTE connection).
Notably, MPLS VPN links are usually tied to a committed SLA, whereas Internet links may either have no SLA at all or a loose SLA (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site).
3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/LTE backup link). For example, a particular customer site may include a first CE router 110 connected to PE-2 and a second CE router 110 connected to PE-3.
In some embodiments, network 100 may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.
Notably, shared-media mesh networks, such as wireless or PLC networks, etc., are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network 160 may be an LLN in which CE-2 operates as a root node for nodes/devices 10-16 in the local mesh, in some embodiments.
In contrast to traditional networks, LLNs face a number of communication challenges. First, LLNs communicate over a physical medium that is strongly affected by environmental conditions that change over time. Some examples include temporal changes in interference (e.g., other wireless networks or electrical appliances), physical obstructions (e.g., doors opening/closing, seasonal changes such as the foliage density of trees, etc.), and propagation characteristics of the physical media (e.g., temperature or humidity changes, etc.). The time scales of such temporal changes can range between milliseconds (e.g., transmissions from other transceivers) to months (e.g., seasonal changes of an outdoor environment). In addition, LLN devices typically use low-cost and low-power designs that limit the capabilities of their transceivers. In particular, LLN transceivers typically provide low throughput. Furthermore, LLN transceivers typically support limited link margin, making the effects of interference and environmental changes visible to link and network protocols. The high number of nodes in LLNs in comparison to traditional networks also makes routing, quality of service (QoS), security, network management, and traffic engineering extremely challenging, to mention a few.
The network interfaces 210 include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface 210 may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.
The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242 (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory 240 and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise routing process 244 (e.g., routing services) and illustratively, a dynamic learning agent (DLA) process 248 and/or an event correlator process 249, as described herein, any of which may alternatively be located within individual network interfaces.
It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.
Routing process/services 244 include computer executable instructions executed by processor 220 to perform functions provided by one or more routing protocols, such as the Interior Gateway Protocol (IGP) (e.g., Open Shortest Path First, “OSPF,” and Intermediate-System-to-Intermediate-System, “IS-IS”), the Border Gateway Protocol (BGP), etc., as will be understood by those skilled in the art. These functions may be configured to manage a forwarding information database including, e.g., data used to make forwarding decisions. In particular, changes in the network topology may be communicated among routers 200 using routing protocols, such as the conventional OSPF and IS-IS link-state protocols (e.g., to “converge” to an identical view of the network topology).
Notably, routing process 244 may also perform functions related to virtual routing protocols, such as maintaining VRF instance, or tunneling protocols, such as for MPLS, generalized MPLS (GMPLS), etc., each as will be understood by those skilled in the art. Also, EVPN, e.g., as described in the IETF Internet Draft entitled “BGP MPLS Based Ethernet VPN”<draft-ietf-12vpn-evpn>, introduce a solution for multipoint L2VPN services, with advanced multi-homing capabilities, using BGP for distributing customer/client media access control (MAC) address reach-ability information over the core MPLS/IP network.
DLA process 248 and/or event correlator process 249 include computer executable instructions that, when executed by processor(s) 220, cause device 200 to perform anomaly detection functions as part of an anomaly detection infrastructure within the network. In general, anomaly detection attempts to identify patterns that do not conform to an expected behavior. For example, in one embodiment, the anomaly detection infrastructure of the network may be operable to detect network attacks (e.g., DDoS attacks, the use of malware such as viruses, rootkits, etc.). However, anomaly detection in the context of computer networking typically presents a number of challenges: 1.) a lack of a ground truth (e.g., examples of normal vs. abnormal network behavior), 2.) being able to define a “normal” region in a highly dimensional space can be challenging, 3.) the dynamic nature of the problem due to changing network behaviors/anomalies, 4.) malicious behaviors such as malware, viruses, rootkits, etc. may adapt in order to appear “normal,” and 5.) differentiating between noise and relevant anomalies is not necessarily possible from a statistical standpoint, but typically also requires domain knowledge.
Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points).
In various embodiments, processes 248-249 may utilize machine learning techniques, to perform anomaly detection in the network. In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators), and recognize complex patterns in these data. One very common pattern among machine learning techniques is the use of an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function would be the number of misclassified points. The learning process then operates by adjusting the parameters a, b, c such that the number of misclassified points is minimal. After this optimization phase (or learning phase), the model M can be used very easily to classify new data points. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data.
Computational entities that rely on one or more machine learning techniques to perform a task for which they have not been explicitly programmed to perform are typically referred to as learning machines. In particular, learning machines are capable of adjusting their behavior to their environment. For example, a learning machine may dynamically make future predictions based on current or prior network measurements, may make control decisions based on the effects of prior control commands, etc.
For purposes of anomaly detection in a network, a learning machine may construct a model of normal network behavior, to detect data points that deviate from this model. For example, a given model (e.g., a supervised, un-supervised, or semi-supervised model) may be used to generate and report anomaly scores to another device. Example machine learning techniques that may be used to construct and analyze such a model may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), or the like.
One class of machine learning techniques that is of particular use in the context of anomaly detection is clustering. Generally speaking, clustering is a family of techniques that seek to group data according to some typically predefined notion of similarity. For instance, clustering is a very popular technique used in recommender systems for grouping objects that are similar in terms of people's taste (e.g., because you watched X, you may be interested in Y, etc.). Typical clustering algorithms are k-means, density based spatial clustering of applications with noise (DBSCAN) and mean-shift, where a distance to a cluster is computed with the hope of reflecting a degree of anomaly (e.g., using a Euclidian distance and a cluster based local outlier factor that takes into account the cluster density).
Replicator techniques may also be used for purposes of anomaly detection. Such techniques generally attempt to replicate an input in an unsupervised manner by projecting the data into a smaller space (e.g., compressing the space, thus performing some dimensionality reduction) and then reconstructing the original input, with the objective of keeping the “normal” pattern in the low dimensional space. Example techniques that fall into this category include principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs (e.g., for non-linear models), and replicating reservoir networks (e.g., for non-linear models, typically for time series).
According to various embodiments, processes 248-249 may use graph-based models for purposes of anomaly detection. Generally speaking, a graph-based model attempts to represent the relationships between different entities. For example, ego-centric graphs have been used to represent the relationship between a particular social networking profile and the other profiles connected to it (e.g., the connected “friends” of a user, etc.). The patterns of these connections can then be analyzed for purposes of anomaly detection. For example, in the social networking context, it may be considered anomalous for the connections of a particular profile not to share connections, as well. In other words, a person's social connections are typically also interconnected. If no such interconnections exist, this may be deemed anomalous.
In general, a graph is a mathematical structure that connects nodes using links that may or may not be weighted and/or directed. A typical graph used in networking is the directed graph generated by linking nodes that have performed a data communication. Thus, a graph-based anomaly detection model may construct such a graph and model and or all of the graph's parameters, to detect anomalies. For instance, the graph of communications between nodes in a network can be constructed and the amount of data transferred between two nodes may be used as the weight of its links. On this graph, a mixture of Gaussians model can be used, for instance, for modeling the amounts of transferred data between different nodes. This can then be used to detect anomalies in terms of transferred data, e.g., for detecting pairs of nodes that suddenly change their data communication behavior.
As shown in
In various embodiments, events may be correlated across different graph-based models. For instance, the sequence of detected anomalies can be considered as a function of time and searched for anomalies that appear simultaneously on the network (e.g., those that have a high correlation in time). Notably, such anomalies may or may not occur in the same regions of the network. For example, a DDoS attack may involve a myriad of geographically-diverse devices launching an attack at a coordinated point in time. Another example of correlation can be a topological correlation using the graph information. For example, assume that two or more overlapping graph-based models (e.g., graphs having common nodes) each models the communications between their respective nodes. In such a case, an anomaly score may be computed for each link in terms of transferred data, etc., from the models. These anomaly scores may be topologically correlated, using the topological information provided by the different graph-based models, in order to correlate/associate anomalies that are related (e.g., an anomaly between nodes A and B detected on graph 1, between nodes B and C detected on graph 2, between C and D on graph 3, etc.). Note that combinations of different graph parameters may also be analyzed for purposes of anomaly detection. For example, temporal and topological correlations may be performed at the same time, in some embodiments.
In various embodiments, events/anomalies between different graph-based models may be correlated (e.g., by event correlator process 249). For example, continuing the example of
As noted previously, other anomaly detection techniques have been preferred in computer networks (e.g., to detect DDoS attacks, non-malicious code that is performing incorrectly, etc.) over that of graph-based anomaly detection models. Some approaches try to analyze changes in the overall statistical behavior of the network traffic (e.g., the traffic distribution among flows tends to flatten when a DDoS attack occurs). Other approaches aim at statistically characterizing the normal behaviors of network flows or TCP connections, in order to detect significant deviations. Classification approaches try to extract features of network flows and traffic that are characteristic of normal traffic or malicious traffic, constructing from these features a classifier that is able to differentiate between the two classes (normal and malicious).
Notably, graph-based anomaly detection models typically require that all information be available to the model. In the case of social networking, for example, this is not usually an issue, as all of the social networking information is available on the same system. However, in the context of modeling network behavior via distributed learning agents, it may be impractical for any particular device to have access to all information regarding the entire network.
Event Correlation Merging Local Graph Models from Distributed Nodes
The techniques herein provide an infrastructure that performs anomaly correlation by inspecting similarities between graph-based models computed by remote distributed learning agents. In some aspects, each remote distributed learning agent may be responsible for computing its own graph-based model that models a given subset of the network (e.g., the portion of the network of which a particular learning agent is able to observe). In further aspects, a supervisory (e.g., central) device may be operable to confirm an anomaly detected by a distributed agent (e.g., by seeking user input, based on defined rules, etc.), compute similarities between attacking flows detected by different graph-based models, determine the source/cause of an anomaly (e.g., how an attack is distributed), assess the degree of severity of an anomaly, and/or trigger a mitigation action to mitigate the effects of the detected anomaly or attack.
Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with processes 248-249, which may include computer executable instructions executed by the processor 220 to perform functions relating to the techniques described herein. For example, the techniques herein may be treated as extensions to machine learning processes and protocols, and as such, may be processed by similar components understood in the art that execute those processes and protocols, accordingly.
Specifically, according to various embodiments, a device in a network receives an indication of a network anomaly detected by a first graph-based anomaly detection model hosted by a first node in the network. The device identifies one or more additional graph-based anomaly detection models based on the network anomaly detected by the first graph-based anomaly detection model. The device correlates one or more network events from the one or more additional graph-based anomaly detection models with the network anomaly detected by the first graph-based anomaly detection model. The device identifies a cause of the network anomaly using the one or more network events from the one or more additional graph-based anomaly detection models that are correlated with the network anomaly detected by the first graph-based anomaly detection model.
Operationally, the techniques herein introduce an infrastructure in which distributed devices in the network act as dynamic learning agents (DLAs) (e.g., by executing DLA process 248). Each DLA may build a partial view of the node-to-node relationships in the network. These relationships may be characterized by the amount of traffic exchanged by the nodes, the type of applications that communicate between these nodes, the geographical/topological proximity of the nodes, or any other information that may be used to characterize the nodes/devices. In many situations, such a partial view of the overall network may be sufficient for purposes of detecting network anomalies. For example, CE-2 and CE-3 shown in
A particular device hosting DLA process 248 that detects an anomaly in the network (e.g., a possible attack, etc.) may provide an indication of the detected anomaly to a supervisory/central network device. For example, assume that DLAi shown detects an anomaly. In such a case, it may provide indication 402 to a supervisory device (e.g., a self learning network central agent in servers 152-154, etc.) that executes event correlator process 249. Indication 402 may include, in various embodiments, information regarding the type of flow(s) that were deemed anomalous (e.g., the flow source, the flow destination, the application ID, etc.), the type of detected anomaly, an anomaly score, a duration, or any other information regarding the detected anomaly. Indication 402 may provide such information in compressed form (e.g., as mathematical models) or as raw data, in various embodiments. In addition, indication 402 may be provided to the supervisory device on a push basis (e.g., in response to detecting the anomaly) or on a pull basis (e.g., in response to receiving a request for information regarding any anomalies detected by the DLA).
DLA process 248 may detect an anomaly either directly from a graph-based anomaly detection model or, alternatively, construct such a model in response to detecting an anomaly using another detection technique (e.g., statistical methods, etc.). Generally, anomalies may be detected from a graph-based model based on an assumption that the features of the graph (e.g., the degree of a node, the number of edges in the graph, the total edge weights in the graph, the principal eigenvalue of the weighted adjacency matrix for the graph, etc.) follow a power law and/or an expected pattern.
In some aspects, the supervisory device may confirm whether the detected anomaly is a true positive. For example, as shown in
If a detected anomaly is confirmed (e.g., by a user/administrator, based on predefined rules, etc.), event correlator process 249 may retrieve the graph-based models of the DLAs, as shown in
Should the graph-based models stored by event correlator process 249 not be recent, the supervisory device may request an update from the corresponding DLAs. For example, DLA1 may provide model data 404 to the supervisory device, DLAn may provide model data 406 to the supervisory device, etc. In yet further embodiments, the supervisory device may request the graph-based models from all DLAs belonging a specific domain using a well-know multicast IPv4/v6 address, specifying the model of interest (e.g., graph vs. host models, . . . ), etc. Note that in one embodiment, graph correlation may also be performed in batch-mode (e.g., during quiet periods) or in response to detecting the anomaly (e.g., as specified by policy, explicitly required by the administrator, etc.), according to the severity of the detected anomaly.
As shown in
In a further aspect, the supervisory device executing event correlator process 249 may correlate multiple graph-based models retrieved from the various DLAs. In other words, event correlator process 249 may analyze the different graph-based models, to determine how similar the models are to the model associated with the abnormality (e.g., by measuring the degree of similarity between a set of graphs, using sub-graph matching, etc.). In various embodiments, event correlator process 249 may use different techniques to determine the degree of similarity/correlation between the different graph-based models, due to varying degrees of complexity. For example, if event correlator process 249 analyzes two graph-based models for non-overlapping networks, the search may be limited to finding the set of common IP addresses in the flows (e.g., as sources or destinations) marked as offending/anomalous flows. However, if event correlator process 249 analyzes two or more graph-based models hosted by DLAs on the same network, process 249 may find the set of anomalous/offending flows that overlap across the models, as shown in
In further embodiments, event correlator process 249 may expand its search of the graph-based models from the DLAs to search for similar flows to those identified as anomalous (e.g., in addition to finding the anomalous/offending flows). Indeed, the control of a particular malware, for instance, will generate a particular structure of connections in a graph-based model, regardless of the C&C source. Therefore, once a C&C has been located, event correlator process 249 can use the information about the graph structure generated by this C&C to locate other C&Cs observed by other DLAs, but not labeled as abnormal yet. In one embodiment, these similar graph structures can be located using community-finding techniques. For instance, event correlator process 249 may extract graph-based features such as degrees of nodes, orders of sub-graphs, etc. and use a clustering technique such as k-means or DBSCAN on the feature space to locate similar graph structures. In other embodiments, event correlator process 249 may use pure graph structure techniques such as the clique percolation method, etc., to locate communities of similar graph structures. In yet another embodiment, event correlator process 249 may use a statistical inference technique to determine the similarity between graph-based models, such as those based on stochastic block models.
Once the cause of the detected anomaly has been identified, mitigation actions may be triggered in the network. For example, as shown in
In one embodiment, event correlator process 249 may initiate mitigation by sending a custom unicast message to the DLA “hosting” the offending traffic that indicates that the flow is part of a generalized attack. Such a message may either specify the mitigation operation to be performed (e.g., by instructing the receiving device to shape traffic, to re-marking the traffic as low priority by re-coloring the DSCP fields, to drop the traffic, etc.) or the policy may be hosted on the DLA itself.
In another embodiment, event correlator process 249 may trigger a mitigation operation at a non-DLA device (e.g., a device that does not host DLA process 248). For example, assume that a network attack includes a number of attacking flows that traverse a particular non-DLA router (e.g., a C&C server outside of the network, etc.). In such a case, event correlator process 249 may send a custom message to the device, or to an NMS in charge of the configuration of the device, that includes an instruction to perform a specified mitigation operation. For example, event correlator process 249 may determine that a series of hosts present in a set of graph-based models are involved in similar anomalies (e.g., a set of hosts that are compromised by malware trying to exfiltrate information from a series of servers outside of the network, such as in a site interconnected to their respective site via BGP). In such a case, event correlator process 249 may identify the set of paths followed by all offending flows, to determine the BGP routers where the mitigation operation should be performed. For the sake of illustration, suppose that n hosts in domains D1 and D2 (e.g., domains interconnected via different routing domains such as OSPF areas) are attempting to exfiltrate information from a server S in a remote domain D3 interconnected via BGP. Thus, event correlator process 249 may determine that all attack flows transit through a common BGP peer, determine that the common peer should perform a mitigation operation, and instruct the peer to do so, accordingly.
In various embodiments, the indication received by the device in step 510 may be provided by a first node in the network that hosts a graph-based anomaly detection model. Such a model may, for example, model the traffic or other relationships between a subset of nodes in the network that the first node is able to monitor. In other words, the graph-based model used by the first node may only model a subset of the network. In one embodiment, multiple nodes in the network may host similar graph-based models that each models a different subset of the network.
In response to receiving the indication of a detected network anomaly, the device may, in some embodiments, attempt to confirm that the detected anomaly is indeed anomalous and/or requires further action. In one embodiment, the device may provide data regarding the detected anomaly to a user interface device, to receive a confirmation or denial from a human operator. In another embodiment, the device may use a predefined set of rules to determine whether the detected anomaly is indeed anomalous and requires further action.
At step 515, the device identifies one or more additional graph-based anomaly detection models, as described in greater detail above. In various embodiments, the additional graph-based anomaly detection models may be hosted on the same node that detected the network anomaly or on different nodes (e.g., on different DLAs). The one or more additional graph-based anomaly detection models may be identified as the full set of deployed detection models (e.g., all models in the network) or a subset of the deployed models selected based on the detected network anomaly. For example, assume that the detected anomaly corresponds to a potential attack on a proxy-HTTP server. In such a case, the device may determine that all locations/sites hosting a DLA may also potentially host similar malware and identify the corresponding models from the DLAs.
At step 520, the device correlates network events associated with the graph-based anomaly detection models with the detected anomaly, as described in greater detail above. Said differently, the device may analyze the graph-based models from multiple distributed nodes in the network, to identify any similarities between the models. For example, the device may attempt to identify any common anomalies, traffic flows, sources, or destinations between the models. Correlation may be temporal (e.g., anomalies/events occurring within a certain amount of time of one another) and/or topographical (e.g., anomalies/events associated with the same device or set of devices). For example, assume that the detected anomaly in step 510 is an anomalous traffic flow directed to a particular device in the network. In such a case, one or more other traffic flows/anomalies from the other graph-based model(s) may be correlated to the detected anomaly. In addition to identifying any offending flows (e.g., anomalies) that are correlated to the anomaly, the device may, in some embodiments, be configured to also search for any traffic flows that are similar to the offending flows (e.g., flows to the same destination, flows containing similar traffic, etc.). In doing so, the device may expand its search to also identify any potentially offending traffic that was not previously identified as such by the individual models.
At step 525, the device identifies a cause of the anomaly, as described in greater detail above. Notably, the device may identify a set of one or more nodes and/or traffic flows that are potential causes of the anomaly based on the correlated events/anomalies from step 520. For example, in the case of a distributed network attack, the device may identify the nodes that are potentially infected with malware. In some embodiments, the device may also initiate mitigation actions based on the cause of the anomaly. For example, the device may cause one or more of the DLA-hosting nodes to perform an anomaly mitigation operation (e.g., dropping certain traffic, lowering the priority of certain traffic, etc.) and/or one or more non-DLA nodes to perform the mitigation operation. Procedure 500 then ends at step 530.
At step 615, as detailed above, the device may detect an anomaly. In some cases, the device may detect the anomaly using the graph-based model itself. In these cases, the interrelationships between nodes, the edge weights, or other properties of a graph-based model may be indicative of an anomalous condition. For example, an excessively-weighted graph edge between nodes may indicate that the source node is attempting to maliciously overload the network and/or destination. In another example, the pattern of connections between nodes may be anomalous. In other embodiments, the device may detect the anomaly using another technique (e.g., statistical techniques, etc.) and generate/update the graph-based model, in response to detecting the anomaly.
At step 620, the first device reports the detected anomaly to a second device, as detailed above. In particular, the first device may provide an indication of the detected anomaly to a supervisory device responsible for determining whether any corrective measures should be taken in the network. For example, the supervisory device may validate the detected anomaly (e.g., by seeking validation by a human operator, according to predefined rules, etc.), prior to initiating any corrective measures. Such a report may, in some cases, identify any anomalous traffic flows, the source of the flow(s), the destination(s) of the flow(s), etc.
At step 625, as detailed above, the first device provides the graph-based model to the second device. In some embodiments, the first device may provide the graph-based model to the second device, in response to receiving a request for the information from the second device. For example, the second device may request the graph-based model after confirming the detected anomaly. In other embodiments, the first device may provide the graph-based model to the second device at predetermined times (e.g., periodically, at times when network usage is at a low, etc.). In these cases, the second device may still request the model if, for example, the second device determines that its stored model is potentially out of date. In some embodiments, the second device may use the reported anomaly and the model data to identify a cause of the anomaly and initiate an anomaly mitigation operation in the network. For example, the second device may instruct the first device to perform a mitigation operation (e.g., by blocking traffic, etc.), to alleviate the anomalous condition. Procedure 600 then ends at step 630.
It should be noted that while certain steps within procedures 500-600 may be optional as described above, the steps shown in
The techniques described herein, therefore, provide for a self-learning network architecture that allows for distributed, graph-based models to detect anomalies in isolation, but do not have a global view of the network. Notably, the techniques herein allow for the correlation of detected anomalies/events at very low cost, thereby preserving the distributed nature of the anomaly detection architecture and allowing for anomaly mitigation to be initiated.
While there have been shown and described illustrative embodiments that provide for the correlation of distributed, anomaly detection models, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, the techniques herein may be adapted for use within any kind of anomaly detection, in addition to that of detecting potential network attacks and/or the presence of malicious software. Additionally, the protocols discussed herein are exemplary only and other protocols may be used within the scope of the teachings herein.
The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.
