The present disclosure relates generally to network monitoring, and particularly to methods and systems for combined network-side and off-air monitoring of wireless networks.
Wireless communication networks typically support encryption of traffic, and require wireless terminals to authenticate vis-à-vis the network before they can communicate. Encryption and authentication processes are specified, for example, in Global System for Mobile communications (GSM), Universal Mobile Telecommunication System (UMTS), Long Term Evolution (LTE) and other cellular communication protocols. Both traffic encryption and authentication use cryptographic keys that are stored in the network and in the terminals.
An embodiment that is described herein provides a method for network monitoring. The method includes obtaining first sets of authentication parameters exchanged between wireless communication terminals and a wireless network, by monitoring an air interface between the terminals and the wireless network, and obtaining second sets of authentication parameters exchanged between the terminals and the network, by monitoring at least one wired interface between network-side elements of the wireless network. One or more correlations are established between the first sets and the second sets, and the established correlations are acted upon.
In some embodiments, obtaining the first and second sets includes monitoring authentication sessions conducted between the terminals and a Home Location Register (HLR) of a wireless network. In an embodiment, establishing the correlations includes identifying one or more authentication parameters that appear in one of the first sets and in one of the second sets.
In some embodiments, establishing the correlations includes identifying a first set of authentication parameters obtained from the air interface and a second set of authentication parameters obtained from the wired interface that both pertain to a given terminal. In an embodiment, acting upon the correlations includes decrypting encrypted traffic exchanged with the given terminal using one or more parameters extracted from the correlated first set and second set. Decrypting the encrypted traffic may include obtaining an initial key from the parameters extracted from the correlated first set and second set, deriving one or more subsequent keys from the initial key, and decrypting the encrypted traffic using the subsequent keys.
In a disclosed embodiment, establishing the correlation includes concluding that a Temporary Mobile Station Identity (TMSI) in the first set and an International Mobile Station Identity (IMSI) in the second set both pertain to the given terminal. Acting upon the correlations may include monitoring the given terminal using the TMSI. In another embodiment, obtaining the first sets includes storing the first sets in a database, and establishing the correlations includes, for a given second set obtained from the air interface, querying the database for a first set that matches the given second set.
In yet another embodiment, establishing and acting upon the correlations include buffering traffic received over the air interface so as to produce a delayed replica and a non-delayed replica of the traffic, establishing the correlations using the non-delayed replica, and acting upon the correlations in the delayed replica. In some embodiments, the method includes decrypting encrypted traffic exchanged in a session for which a first set of authentication parameters is unavailable, by searching over at least some of the second sets and attempting to decrypt the encrypted traffic using the searched second sets.
There is additionally provided, in accordance with an embodiment that is described herein, a system for network monitoring including a first interface, a second interface and a processor. The first interface is configured to monitor an air interface between wireless communication terminals and a wireless network. The second interface is configured to monitor at least one wired interface between network-side elements of the wireless network. The processor is configured to obtain using the first interface first sets of authentication parameters exchanged between the terminals and the network, to obtain using the second interface second sets of authentication parameters exchanged between the terminals and the network, and to establish one or more correlations between the first sets and the second sets.
The present disclosure will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which:
Embodiments that are described herein provide improved methods and systems for monitoring communication in wireless networks. The disclosed techniques use a combination of wired network-side monitoring and wireless off-air monitoring to decode encrypted traffic exchanged between wireless terminals and the network. Such techniques can be used, for example, by cellular service providers to evaluate network performance and provide selective Quality-of-Service (QoS) to users.
The reason for combining network-side and off-air monitoring is that some of the parameters needed for decryption are not transmitted over the air interface. In some embodiments, a monitoring system monitors authentication sessions both on the air interface between the terminals and the network, and on at least one wired network-side interface between network-side elements of the network. The system extracts first sets of authentication parameters (referred to herein as “network-side authentication parameters”) from the authentication sessions monitored on the network-side interface, and second sets of authentication parameters (referred to herein as “air-interface authentication parameters”) from the authentication sessions monitored on the air interface.
A processor in the monitoring system establishes correlations between sets of network-side authentication parameters and corresponding sets of air-interface authentication parameters. Based on these correlations, the processor constructs full sets of parameters needed for decrypting encrypted traffic exchanged between the terminals and the network.
In a typical flow, the monitoring system constructs a database of sets of network-side authentication parameters using network-side monitoring. Each set of network-side authentication parameters originates from a respective authentication session and is associated with the International Mobile Station Identity (IMSI) of the terminal involved in the session. (A terminal may have two sets of authentication keys—One for packet traffic and the other for voice and short messaging.) In order to start decrypting the traffic of a given terminal, the system obtains the off-air authentication parameters of that terminal using off-air monitoring, and finds an entry in the database that matches the air-interface authentication parameters. From the combination of correlated network-side and off-air authentication parameters, the processor is able to extract the parameters needed for decryption.
Several examples of combined network-side and off-air monitoring schemes are described in detail below. The methods and systems described herein are entirely passive, and can be implemented either in real-time or near-real-time monitoring, or in off-line analysis. The disclosed techniques, however, are not limited to passive monitoring and can also be used in active monitoring systems, as well. In some embodiments, the system can use the above-described correlation scheme to map the currently-active terminal IMSIs, without necessarily decrypting or decoding traffic content.
In the example of
Wireless network 28 comprises various network-side elements. In the present example the network-side elements comprise one or more base stations 32 (also referred to as NodeB or NB), one or more Radio Network Controllers (RNC) 36, one or more Mobile Switching Centers (MSC) 40, one or more Serving GPRS Support Nodes (SGSN) 44, and a Home Location Register (HLR) 48. In alternative embodiments, network 28 may have any other suitable configuration and any other suitable types and numbers of network-side elements.
In the embodiment of
Interface 60 monitors the air interface between UEs 24 and NBs 32, using an antenna 64. Interface 60 typically comprises suitable Radio Frequency (RF) circuitry and modem circuitry for receiving and demodulating traffic from the air interface.
In some embodiments, system 20 further comprises a processor 68 that carries out the methods described herein. Among other tasks, processor 68 uses interface 52 to monitor authentication sessions exchanged over wired interfaces 56 of network 28, and uses interface 60 to monitor authentication sessions exchanged over the air interface between UEs 24 and network 28. Processor 68 stores authentication parameters that are extracted from the monitored authentication sessions, as well as other relevant information, in a database (DB) 72.
In some embodiments, processor 68 correlates authentication parameters obtained using the two types of interfaces (wired and off-air), so as to reconstruct parameter sets that enable encryption of decrypted traffic exchanged with UEs 24. Such correlation methods are explained in detail below. Processor 68 may use the reconstructed parameters for decrypting communication sessions of UEs 24, e.g., sessions monitored using interface 60.
The configuration of system 20 shown in
Certain elements of system can be implemented using hardware, such as using one or more Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or other device types. Additionally or alternatively, certain elements of system can be implemented using software, or using a combination of hardware and software elements.
Database 72 may be implemented using any suitable memory or storage device, e.g., HDD, SSD or other non-volatile storage medium, and/or a suitable volatile memory such as Random Access Memory (RAM). In a typical implementation, database 72 is implemented in-memory, in order to support high rates of UPDATE operations that involve authentication key generation.
Typically, processor 68 comprises one or more general-purpose processors, which are programmed in software to carry out the functions described herein. The software may be downloaded to the processors in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.
Typically, at least some of the communication traffic exchanged between UEs 24 and network 28 is encrypted. Encryption keys for encrypting and decrypting traffic are typically derived from a seed (sometimes referred to as secret key) stored only in HLR 48 and in the Subscriber Identity Module (SIM) of the UE. Nevertheless, system 20 uses the combined network-side and off-air monitoring to reconstruct the set of parameters (typically the encryption and integrity keys) needed for decryption. These techniques use the fact that these parameters are also used in the authentication process between the UEs and the network.
In network 28, the authentication mechanism uses a permanent Secret key denoted Ki. Ki is stored only in the SIM of UE 24 and in HLR 48, and is not transferred elsewhere. Other keys in the authentication process are temporary keys that typically change from one session to another. When a certain UE 24 registers with network 28, the network and the UE carry out a mutual challenge-and-response process in which network 28 verifies the authenticity of UE 24, and vice versa. The endpoints of this process are UE 24 and HLR 48.
An authentication session begins with UE 24 sending a network authentication request 80, which indicates the International Mobile Station Identity (IMSI) of the UE, to network 28. The UE sends request 80 to its serving NB 32, and the request is forwarded via RNC 36 to HLR 48.
In response to request 80, HLR 48 generates a set of authentication parameters based on the master key Ki of the UE (stored in advance in the HLR) and the IMSI of the UE (provided in request 80). The set of parameters is referred to as a 5-tuple, and comprises the following parameters:
HLR 48 responds to request 80 by sending the 5-tuple to RNC 36 in a response 84. RNC 36 sends a subset of the 5-tuple, namely the RAND and AUTN parameters, to UE 24 in a user authentication request 88. Note that the temporary keys CK and IK are not transmitted over the air interface. The IMSI of UE 24 is also omitted from request 88. Instead, request 88 comprises a Temporary Mobile Station Identity (TMSI) that is assigned for the specific session.
Upon receiving request 88, terminal 24 uses the received RAND, together with the Ki stored in its SIM, to compute a respective AUTN. The UE verifies the authenticity of the network by comparing the AUTN received in request 88 with the AUTN derived locally at the UE. If the two AUTN values are the same, the UE may conclude that network 28 is trustworthy.
Assuming network authentication was successful, UE 24 responds to request 88 by sending an authentication response 92 to RNC 36. In response 92 the UE sends a response parameter denoted SRES. SRES is generated in the UE from RAND and Ki using the same function that HLR 48 used for generating XRES.
RNC 36 verifies the authenticity of UE 24 by comparing SRES (sent by the UE in response 92) with XRES (sent by the HLR in response 84). If the two values match, the RNC may conclude that the UE is trustworthy, and the mutual authentication process ends successfully.
The authentication process described above is depicted purely by way of example. In alternative embodiments, the methods and systems described herein can be used with any other suitable authentication process. In GSM networks, for example, the authentication parameters form a triplet (RAND, SRES and Kc) rather than a 5-tuple. In the context of the present patent application and in the claims, the term “authentication parameters” is used to describe any suitable set of parameters whose knowledge enables successful authentication. Parameters such as IMSI and TMSI are also regarded as authentication parameters in this context.
In some embodiments, monitoring system 20 monitors authentication sessions conducted between UEs 24 and wireless network 28. In particular, network-side interface 52 monitors responses 84 in which the HLR sends [RAND, XRES, CK, IK, AUTN] 5-tuples, and off-air interface 60 monitors UE authentication requests 88 in which network 28 transmits [RAND, AUTN] pairs to UE 24.
The two types of monitoring actions (network-side and off-air) are typically performed independently of one another. In other words, there is usually no a-priori correlation indicating that a certain [RAND, XRES, CK, IK, AUTN] 5-tuple and a certain [RAND, AUTN] pair were sent as part of the same authentication session. For example, core network interface 52 and radio network interface 60 may be geographically separate.
In some embodiments, processor 68 of system 20 establishes correlations between the authentication parameter sets obtained via network-side and off-air monitoring. Processor 68 typically establishes the correlations using the RAND and AUTN parameters, which appear both in the 5-tuples obtained on the network side and in the [RAND, AUTN] pairs obtained from the air interface.
Processor 68 (or interface 52 directly) stores the collected [RAND, XRES, CK, IK, AUTN, IMSI] records in database 72, at a database construction step 104. Database 72 gradually develops to contain a large number of [RAND, XRES, CK, IK, AUTN, IMSI] records pertaining to multiple authentication sessions conducted with various UEs 24.
At a certain point in time, system 20 may be required to decrypt the encrypted traffic of a certain UE 24. The requirement may originate, for example, from the need to recognize the type of application used by the UE in order to provide it with the appropriate QoS, or for any other reason.
In order to decrypt the traffic of the UE in question, system 20 monitors the authentication session between the UE and the network on the air interface using interface 108, at an off-air monitoring step 108. Processor 68 extracts the [RAND, AUTN] pair and the corresponding TMSI value from the monitored session.
At a correlation step 112, processor 68 queries database 72 for a [RAND, XRES, CK, IK, AUTN, IMSI] record, which has RAND and AUTN values that match the [RAND, AUTN] pair obtained from the air interface at step 108 above. Assuming that a matching record is found in the database, processor 68 now possesses a correlation between the CK, IK and IMSI of the UE, and between the TMSI assigned to this UE for the current session.
Using this correlation, processor 68 decrypts the downlink and/or uplink traffic exchanged with the UE, at a decryption step 116. All traffic associated with the UE in the current session is addressed with the TMSI value, and, since processor 68 possesses the correct CK and IK values for this TMSI, it is able to decrypt the traffic.
The flow of operations shown in
In various embodiments, the method of
In an example near-real-time implementation, system 20 comprises suitable buffer storage for buffering traffic obtained from the air interface. Typically, an entire UMTS carrier is buffered, including both authentication sessions and user traffic of various UEs. The buffering operation provides processor 68 with a non-delayed replica and a delayed replica of the UMTS carrier traffic. Processor 68 uses the non-delayed replica of the traffic to obtain the parameters needed for decryption (steps 100-112), and then uses these parameters to decrypt the user traffic in the delayed (buffered) replica. This sort of implementation enables system 20 to perform near-real-time decryption of traffic, and also to compensate for possible geographical separation between core network interface 52 and radio network interface 60.
In an alternative embodiment, system 20 does not decrypt the traffic, but rather uses the correlations obtained at step 112 to monitor one or more UEs 24 using their respective TMSIs. For example, system 20 may use the correlations to map the IMSIs (and thus the UE identities) that are active at a given time at a given geographical area. The correlation is needed because, after authentication, subsequent traffic carries only the TMSI and not the IMSI value. Step 112 obtains a correlation between the unique permanent IMSI of each UE (used during authentication) and the TMSI that was assigned temporarily and used in subsequent traffic.
In some practical scenarios, system 20 is required to decrypt the encrypted traffic of a certain UE 24 even though no off-air authentication parameters are available. For example, in a certain scenario, authentication sessions are rare, and system 20 needs to decrypt the encrypted traffic before encountering the next authentication session. In such cases, processor 68 of system 20 may search exhaustively over the keys ([RAND, XRES, CK, IK, AUTN, IMSI] records) stored in database 72 in an attempt to find a key that successfully decrypts the data.
In an embodiment, processor 68 may remove irrelevant keys from the exhaustive search. For example, processor 68 may remove keys that were issued after system 20 started monitoring. In an embodiment, system 20 may obtain side information that assists in removing irrelevant keys from the exhaustive search. For example, by monitoring additional network-side interfaces, system 20 may enrich the ([RAND, XRES, CK, IK, AUTN, IMSI] records with geographical information, e.g., the Visitor Location Register (VLR), Location Area Code (LAC) and/or cell in which the terminal holding the corresponding session was located. This geographical information enables processor 68 to narrow-down the list of keys that should be searched exhaustively.
In some embodiments, the keys used for decryption by system 20 are not the initial keys formed by correlating the off-air and network-side authentication parameters, but rather subsequent keys that are derived from the initial keys using known key-derivation functions. In LTE, for example, the UE and the network derive multiple types of keys, for use in different network elements on the network side, from the same [CK, IK] pair. In system 20, once CK and IK are discovered using the disclosed techniques, processor 68 may derive the subsequent keys by applying the same key-derivation functions used by the UE and the network.
Although the embodiments described herein mainly address network monitoring, the principles of the present disclosure can also be used for other applications such as monitoring test equipment and communication interception systems.
It will thus be appreciated that the embodiments described above are cited by way of example, and that the present disclosure is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present disclosure includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art. Documents incorporated by reference in the present patent application are to be considered an integral part of the application except that to the extent any terms are defined in these incorporated documents in a manner that conflicts with the definitions made explicitly or implicitly in the present specification, only the definitions in the present specification should be considered.
Number | Date | Country | Kind |
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236968 | Jan 2015 | IL | national |
This application is a continuation of, and claims the benefit of priority to, U.S. patent application Ser. No. 15/008,375, filed on Jan. 27, 2016, which claims the benefit of priority to Israel Patent Application No. 236968, filed on Jan. 28, 2015, the disclosure of both are incorporated herein by reference in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
5689442 | Swanson et al. | Nov 1997 | A |
6404857 | Blair et al. | Jun 2002 | B1 |
6718023 | Zolotov | Apr 2004 | B1 |
6741992 | McFadden | May 2004 | B1 |
6757361 | Blair et al. | Jun 2004 | B2 |
6950521 | Marcovici et al. | Sep 2005 | B1 |
7134141 | Crosbie | Nov 2006 | B2 |
7216162 | Amit et al. | May 2007 | B2 |
7225343 | Honig et al. | May 2007 | B1 |
7269157 | Klinker et al. | Sep 2007 | B2 |
7287278 | Liang | Oct 2007 | B2 |
7374096 | Overhultz et al. | May 2008 | B2 |
7466816 | Blair | Dec 2008 | B2 |
RE40634 | Blair et al. | Feb 2009 | E |
7587041 | Blair | Sep 2009 | B2 |
7769875 | Moisand et al. | Aug 2010 | B1 |
RE43103 | Rozman et al. | Jan 2012 | E |
8176527 | Njemanze et al. | May 2012 | B1 |
8201245 | Dewey et al. | Jun 2012 | B2 |
RE43528 | Rozman et al. | Jul 2012 | E |
RE43529 | Rozman et al. | Jul 2012 | E |
8215546 | Lin et al. | Jul 2012 | B2 |
8224761 | Rockwood | Jul 2012 | B1 |
8351900 | Lotvonen et al. | Jan 2013 | B2 |
RE43987 | Rozman et al. | Feb 2013 | E |
8402543 | Ranjan et al. | Mar 2013 | B1 |
8413244 | Nachenberg | Apr 2013 | B1 |
8496169 | Christofferson | Jul 2013 | B2 |
8499348 | Rubin | Jul 2013 | B1 |
8578493 | McFadden | Nov 2013 | B1 |
8682812 | Ranjan | Mar 2014 | B1 |
8762948 | Zaitsev | Jun 2014 | B1 |
8838951 | Hicks et al. | Sep 2014 | B1 |
8839417 | Jordan | Sep 2014 | B1 |
8850579 | Kalinichenko | Sep 2014 | B1 |
8869268 | Barger | Oct 2014 | B1 |
9385865 | Wix et al. | Jul 2016 | B2 |
10609635 | Ravishankar | Mar 2020 | B2 |
11284288 | Yavuz | Mar 2022 | B2 |
20020129140 | Peled et al. | Sep 2002 | A1 |
20030031322 | Beckmann et al. | Feb 2003 | A1 |
20030097439 | Strayer et al. | May 2003 | A1 |
20050018618 | Mualem et al. | Jan 2005 | A1 |
20050108377 | Lee et al. | May 2005 | A1 |
20050202815 | Verma et al. | Sep 2005 | A1 |
20060026680 | Zakas | Feb 2006 | A1 |
20060253703 | Eronen et al. | Nov 2006 | A1 |
20060262742 | Dommaraju | Nov 2006 | A1 |
20070180509 | Swartz et al. | Aug 2007 | A1 |
20070186284 | McConnell | Aug 2007 | A1 |
20070192863 | Kapoor et al. | Aug 2007 | A1 |
20070294768 | Moskovitch et al. | Dec 2007 | A1 |
20080014873 | Krayer et al. | Jan 2008 | A1 |
20080028463 | Dagon et al. | Jan 2008 | A1 |
20080141376 | Clausen et al. | Jun 2008 | A1 |
20080150698 | Smith et al. | Jun 2008 | A1 |
20080184371 | Moskovitch et al. | Jul 2008 | A1 |
20080196104 | Tuvell et al. | Aug 2008 | A1 |
20080261192 | Huang et al. | Oct 2008 | A1 |
20080285464 | Katzir | Nov 2008 | A1 |
20090036143 | Martin et al. | Feb 2009 | A1 |
20090106842 | Durie | Apr 2009 | A1 |
20090122762 | Kitazoe et al. | May 2009 | A1 |
20090150999 | Dewey et al. | Jun 2009 | A1 |
20090158430 | Borders | Jun 2009 | A1 |
20090172397 | Kim | Jul 2009 | A1 |
20090216760 | Bennett | Aug 2009 | A1 |
20090249484 | Howard et al. | Oct 2009 | A1 |
20090267730 | Zhang | Oct 2009 | A1 |
20090282476 | Nachenberg et al. | Nov 2009 | A1 |
20100037314 | Perdisci | Feb 2010 | A1 |
20100071065 | Khan et al. | Mar 2010 | A1 |
20100100949 | Sonwane | Apr 2010 | A1 |
20100124196 | Bonar et al. | May 2010 | A1 |
20110080267 | Clare et al. | Apr 2011 | A1 |
20110099620 | Stavrou et al. | Apr 2011 | A1 |
20110150211 | Anderson | Jun 2011 | A1 |
20110154497 | Bailey | Jun 2011 | A1 |
20110167494 | Bowen et al. | Jul 2011 | A1 |
20110271341 | Satish et al. | Nov 2011 | A1 |
20110302653 | Frantz et al. | Dec 2011 | A1 |
20110320816 | Yao et al. | Dec 2011 | A1 |
20120017281 | Banerjee | Jan 2012 | A1 |
20120167221 | Kang et al. | Jun 2012 | A1 |
20120174225 | Shyamsunder et al. | Jul 2012 | A1 |
20120222117 | Wong et al. | Aug 2012 | A1 |
20120256730 | Scott et al. | Oct 2012 | A1 |
20120304244 | Xie et al. | Nov 2012 | A1 |
20120308009 | Venkatsuresh et al. | Dec 2012 | A1 |
20120311708 | Agarwal et al. | Dec 2012 | A1 |
20120327956 | Vasudevan | Dec 2012 | A1 |
20130014253 | Neou | Jan 2013 | A1 |
20130333038 | Chien | Dec 2013 | A1 |
20140075557 | Balabine et al. | Mar 2014 | A1 |
20140098797 | Kanamarlapudi et al. | Apr 2014 | A1 |
20140148196 | Bassan-Eskenazi et al. | May 2014 | A1 |
20140160955 | Lum et al. | Jun 2014 | A1 |
20140207917 | Tock et al. | Jul 2014 | A1 |
20140298469 | Marion et al. | Oct 2014 | A1 |
20150019746 | Shatzkamer | Jan 2015 | A1 |
20150023504 | Wix et al. | Jan 2015 | A1 |
20150135265 | Bagrin | May 2015 | A1 |
20150135326 | Bailey, Jr. | May 2015 | A1 |
20150140097 | Goldfarb | May 2015 | A1 |
20150161518 | McCann | Jun 2015 | A1 |
20150180658 | Korenfeld et al. | Jun 2015 | A1 |
20160050562 | Pudney et al. | Feb 2016 | A1 |
20160182460 | Alexander et al. | Jun 2016 | A1 |
20170013121 | Baeder et al. | Jan 2017 | A1 |
20170310486 | Goldfarb | Oct 2017 | A1 |
Number | Date | Country |
---|---|---|
2010116292 | Oct 2010 | WO |
Entry |
---|
3GPP TS 24.008 v3.8.0, “3rd Generation Partnership Project; Technical Specification Group Core Network; Mobile radio interface layer 3 specification; Core Network Protocols—Stage 3,” Release 1999, (Jun. 2001), 442 pages. |
Aho, Alfred V., et al., “Efficient String Matching: An Aid to Bibliographic Search,” Communication of the ACM, Jun. 1975, vol. 18, No. 6, pp. 333-340. |
Altshuler, Y., et al., “How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage,” LNCS 7227, 2012, pp. 43-52. |
Altshuler, Y., et al., “Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data,” IEEE, 2011, 10 pages. |
Altshuler, Y., et al., “Trade-Offs in Social and Behavioral Modeling in Mobile Networks,” LNCS 7812, 2013, pp. 412-423. |
Argamon, S., et al., “Automatically Profiling the Author of an Anonymous Text,” Communication of the ACM, vol. 52, No. 2, Feb. 2009, pp. 119-123. |
Argamon, S., et al., “Gender, Genre, and Writing Style in Formal Written Texts,” Text & Talk, vol. 23, Issue 3, 2003, 32 pages. |
Asokan, N., et al., “Man-in-the-Middle in Tunneled Authentication Protocols,” Draft version 1.3 (latest public version: http://eprint.iacr.org/2002/163/, Nov. 11, 2002, 15 pages. |
Atkinson, M., et al., “Near Real Time Information Mining in Multilingual News,” World Wide Web Conference, Apr. 20-24, 2009, 2 pages. |
Bailey, M., et al., “Automated Classification and Analysis of Internet Malware,” RAID, 2007, pp. 178-197. |
Barkan, E.P., “Cryptanalysis of Ciphers and Protocols,” Research Thesis submitted to the Senate of the Technion, Israel Institute of Technology, 2006, 200 pages. |
Bayer, U., et al., Scalable, Behavior-Based Malware Clustering, Secure Systems Lab, Technical University, Vienna, 2009, 18 pages. |
Bilge, Leyla, et al., “Exposure: Finding Malicious Domains Using Passive DNS Analysis,” Feb. 2011, 17 pages. |
“Cell Scanning and Catcher Detection in unnoticeable pocket size,” NetHawk C2, Data sheet, version 1.4, EXFO, 2010, 4 pages. |
Cellusys, “SS7 Vulnerabilities,” e-book, 2016, 53 pages. |
Cloudshield, Inc., “Lawful Intercept Next-Generation Platform,” 2009, 6 pages. |
Coffman, T., et al., “Graph-Based Technologies for Intelligence Analysis,” CACM, Mar. 2004, 12 pages. |
Corney, M., et al. “Gender-Preferential Text Mining of E-mail Discourse,” Proceedings of the 18the Annual Computer Security Applications Conference, 2002, 8 pages. |
De Vel, O., et al., “Language and Gender Author Cohort Analysis of E-mail for Computer Forensics,” Defence Science and Technology Organisation, Australia, 2002, 16 pages. |
Dharmapurikar, Sarang, et al., “Fast and Scalable Pattern Matching for Network Intrusion Detection Systems,” IEEE Journal on Selected Areas in Communications, Oct. 2006, vol. 24, Issue 10, pp. 1781-1792. |
Dietrich, C.J., et al., “CoCoSpot: Clustering and recognizing botnet command and control channels using traffic analysis,” 2012, pp. 475-486. |
Eagle, N., et al., “Inferring friendship network structure by using mobile phone data,” PNAS, vol. 106, No. 36, 2009, pp. 15274-15278. |
Engel, T., “SS7: Locate. Track. Manipulate.” 2014, 55 pages. |
Eslahi, M., “botAnalytics: Improving HTTP-Based Botnet Detection by Using Network Behavior Analysis system,” Dissertation, Faculty of Computer Science and Information Technology, University of Malaya, 2010, 124 pages. |
Estival, D., et al., “Author Profiling for English Emails,” Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics, 2007, pp. 263-272. |
Fisk, Mike, et al., “Applying Fast String Matching to Intrusion Detection,” Los Alamos National Laboratory and University of California San Diego, Jun. 1975, 22 pages. |
FoxReplay Analyst, Fox Replay BV, http//www.foxreplay.com, Revision 1.0, Nov. 2007, 5 pages. |
FoxReplay Analyst Product Brochure, Fox-IT BV, http//www.foxreplay.com, 2006, 2 pages. |
Girardin, F., et al., “Detecting air travel to survey passengers on a worldwide scale,” Journal of Location Based Services, 2010, 26 pages. |
Goldfarb, Eithan, “Mass Link Analysis: Conceptual Analysis,” Jun. 24, 2007, Version 1.1, 21 pages. |
Goswami, S., et al., “Stylometric Analysis of Bloggers' Age and Gender,” Proceedings of the Third International ICWSM Conference, 2009, pp. 214-217. |
Gu, G., et al., “BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection,” USENIX Security Symposium, vol. 5, No. 2, XP61009228, 2008, 16 pages. |
Gu, G., et al., “BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic,” Proceedings of the 15th Annual Network and Distributed System Security Symposium (NDSS'08), San Diego, California, 2008, 18 pages. |
Kostrzewa, A., “Development of a man in the middle attack on the GSM Um-Interface,” Master Thesis, 2011, 88 pages. |
Jacob, G., et al., “Jackstraws: Picking Command and Control Connections from Bot Traffic,” Proceedings of the 20th Usenix Security Symposium, San Francisco, 2011, 16 pages. |
Lakhina, A., et al., “Mining Anomalies Using Traffic Feature Distributions,” SIGCOMM, 2005, pp. 217-228. |
Livadas, C., et al., “Using Machine Learning Techniques to Identify Botnet Traffic,” In 2nd IEEE LCN Workshop on Network Security (WoNS'2006), 2006, pp. 967-974. |
Meyer, U., et al., “On the Impact of GSM Encryption and Man-in-the-Middle Attacks on the Security of Interoperating GSM/UMTS Networks,” proceedings of the 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2004, pp. 2876-2883. |
Mohrehkesh, S., et al., “Demographic Prediction of Mobile User from Phone Usage,” Proceedings Mobile Data Challenge by Nokia Workshop, Newcastle, United Kingdom, 2012, 4 pages. |
Mourad, H., “The Fall of SS7—How Can the Critical Security Controls Help?” The SANS Institute, 2015, 29 pages. |
Navarro, Gonzalo, et al., “Flexible Pattern Matching in Strings: Practical On-Line Search Algorithms for Texts and Biological Sequences,” Cambridge University Press, 2002, 166 pages. |
Netronome SSL Inspector Solution Overview White Paper, “Examining SSL-encrypted Communications,” 2010, 8 pages. |
Nohl, K., “Mobile self-defense,” Security Research Labs, 2015, 24 pages. |
Pan, Long, “Effective and Efficient Methodologies for Social Network Analysis,” Dissertation submitted to faculty of Virginia Polytechnic Institute and State University, Blacksburg, Virginia, Dec. 11, 2007, 148 pages. |
Radiator, EAP-SIM, EAP-AKA and EAP-AKA' Support, White Paper, Open System Consultants Pty. Ltd., 2014, 17 pages. |
Rangel, F., et al., “Overview of the Author Profiling Task at PAN 2013,” CLEF 2013 Evaluation Labs, 2013, 13 pages. |
Rieck, K., et al., “Botzilla: Detecting the ‘Phoning Home’ of Malicious Software,” Proceedings of the ACM Symposium on Applied Computing (SAC), Sierre, Switzerland, 2010, 7 pages. |
Rohde & Schwarz GmbH & Co. KG, “Accessnet-T, DMX-500 R2, Digital Mobile eXchange,” Product Brochure, Secure Communications, Mar. 2000, 4 pages. |
Rohde & Schwarz GmbH & Co. KG, “Accessnet-T IP,” Product Brochure, Secure Communications, Jan. 2000, 4 pages. |
Rohde & Schwarz GmbH & Co. KG, “Digital Standards for R&S SMU200A, R&S SMATE200A, R&S SMJ100A, R&S SMBV100A and R&S AMU200A,” Data Sheet, Test & Measurement, May 2000, 68 pages. |
Rohde & Schwarz GmbH & Co. KG, “GAPM Passive GSM Test System,” Technical Information, 2009, 37 pages. |
Rohde & Schwarz GmbH & Co. KG, “Integrated Digital Audio Software R&S AllAudio,” Specifications, 2000, 8 pages. |
Rohde & Schwarz GmbH & Co. KG, “R&S AllAudio Integrated Digital Audio Software,” Product Brochure, Radiomonitoring & Radiolocation, Feb. 2000, 12 pages. |
Rohde & Schwarz GmbH & Co. KG, “R&S AllAudio Integrierte digitale Audio-Software,” Product Brochure, Feb. 2002, 12 pages. |
Rohde & Schwarz GmbH & Co. KG, “R&S Ammos GX425 Software,” http://www2.rohde-schwarz.com/en/products/radiomonitoring/Signal_Analysis/GX425, Jul. 30, 2010, 1 page. |
Rohde & Schwarz GmbH & Co. KG, “R&S Ammos GX430 PC-Based Signal Analysis and Signal Processing Standalone software solution,” http://www2.rohde-schwarz.com/en/products/radiomonitoring/Signal_Analysis/GX430, Jul. 30, 2010, 1 page. |
Rohde & Schwarz GmbH & Co. KG, “R&S RA-CM Continuous Monitoring Software,” Product Brochure, Radiomonitoring & Radiolocation, Jan. 2001, 16 pages. |
Rohde & Schwarz GmbH & Co. KG, “R&S Ramon COMINT/CESM Software,” Product Brochure, Radiomonitoring & Radiolocation, Jan. 2000, 22 pages. |
Rohde & Schwarz GmbH & Co. KG, “R&S TMSR200 Lightweight Interception and Direction Finding System,” Technical Information, Aug. 14, 2009, 8SPM-ko/hn, Version 3.0, 10 pages. |
Schulzrinne, H., et al., “RTP: A Transport Protocol for Real-Time Applications,” Standards Track, Jul. 2003, 89 pages. |
Sheng, Lei, et al., “A Graph Query Language and Its Query Processing,” IEEE, Apr. 1999, pp. 572-581. |
Signaling System 7 (SS7) Security Report, Positive Technologies, 2014, 15 pages. |
Soghoian, Christopher, et al., “Certified Lies: Detecting and Defeating Government Interception Attacks Against SSL,” 2010, 19 pages. |
Stamatatos, E., “Author identification: Using text sampling to handle the class imbalance problem,” Science Direct, Information Processing and Management, vol. 44, 2008, pp. 790-799. |
Strobel, D., “IMSI Catcher,” Seminararbeit, Ruhr-Universität Bochum, 2007, pp. 13-24. |
Svenson, Pontus, et al., “Social network analysis and information fusion for anti-terrorism,” CIMI, 2006, 8 pages. |
“TEMS™ Pocket—A Complete Measurement Smartphone System in Your Hand,” Ascom Network Testing, 2013, 2 pages. |
Thonnard, O., et al., “Actionable Knowledge Discovery for Threats Intelligence Support Using a Multi-Dimensional Data Mining Methodolgy,” 2008 IEEE International Conference on Data Mining Workshops, 2008, pp. 154-163. |
Tongaonkar, Alok S., “Fast Pattern-Matching Techniques for Packet Filtering,” Stony Brook University, May 2004, 44 pages. |
Vedaldi, A., “An implementation of SIFT detector and descriptor,” University of California at Los Angeles, 2007, 7 pages. |
Verint Systems Inc., “Mass Link Analysis: Solution Description,” Dec. 2008, 16 pages. |
Wang, H., et al., “NetSpy: Automatic Generation of Spyware Signatures for NIDS,” Proceedings of the 22nd Annual Computer Security Applications Conference, Miami Beach, Florida, Dec. 2006, ten pages. |
“Wireless Network Optimization Platform—LTE,” E6474A Drive Test, JDSU, Communications Test & Measurement Solutions, 2012, 4 pages. |
Yu, Fang, et al., “Fast and Memory-Efficient Regular Expression Matching for Deep Packet Inspection,” ANCS'06, San Jose, California, Dec. 3-5, 2006, 10 pages. |
Yu, Fang, et al., “Gigabit Rate Packet Pattern-Matching Using TCAM,” Proceedings of the 12th IEEE International Conference on Network Protocols (ICNP'04), 2004, 10 pages. |
Number | Date | Country | |
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20200107195 A1 | Apr 2020 | US |
Number | Date | Country | |
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Parent | 15008375 | Jan 2016 | US |
Child | 16703241 | US |