@article {20, title = {Network risk management using attacker profiling}, journal = {Security and Communication Networks}, volume = {2}, year = {2009}, pages = {83{\textendash}96}, abstract = {

Risk management refers to the process of making decisions that minimize the effects of vulnerabilities on the network hosts. This can be a difficult task in the context of high-exploit probability and the difficult to identify new exploits and vulnerabilities. For many years, security engineers have performed risk analysis using economic models for the design and operation of risk-prone, technological systems using attack profiles. Based on the type of attacker identified, security administrators can formulate effective risk management policies for a network. We hypothesize that sequence of network actions by an attacker depends on the social behavior (e.g., skill level, tenacity, financial ability). We extended this and formulated a mechanism to estimate the risk level of critical resources that may be compromised based on attacker behavior. This estimation is accomplished using behavior based attack graphs representing all the possible attack paths to all the critical resources. The risk level is computed based on these graphs and are used as a measure of the vulnerability of the resource and forming an effective basis for a system administrator to perform suitable changes to network configuration. Copyright {\textcopyright} 2008 John Wiley \& Sons, Ltd.

}, keywords = {attack graphs, behavior, risk management}, issn = {1939-0122}, doi = {10.1002/sec.58}, url = {http://dx.doi.org/10.1002/sec.58}, author = {Ram Dantu and Kolan, Prakash and Cangussu, Jo{\~a}o} } @conference {99, title = {Call Algebra}, booktitle = {2008 5th IEEE Consumer Communications and Networking Conference}, year = {2008}, month = {Jan}, abstract = {

In every day life, people communicate through a voice(e.g., VoIP) network with different social groups that range from known people such as family members, friends, and distant relatives to unknown people such as spammers, telemarketers, and phishers. We believe that there exists a human/social dynamics between individuals by the way calls are generated, handled and received. In this paper we present how this dynamics can be used for detecting and filtering unwanted calls. In this paper, we first enumerate the communication patterns between the called party (callee) and the calling parties (callers). Next, we discuss operations on caller-callee matrices constructed based on their communication patterns, and derive call-constructs that can be used for determining the legitimacy of the calls and the callers. Finally, we discuss how these communication patterns and operations can be grouped for solutions to few of the existing IP telephony problems. These solutions can complement the existing no-call-lists in voice networks.

}, keywords = {Algebra, call algebra, caller-callee matrices, communication pattern, Computer science, Context modeling, Filtering, Humans, Internet telephony, IP networks, IP telephony problem, matrix algebra, Mood, Peer to peer computing, social aspects of automation, social group, Social network services, voice-over-IP, VoIP network}, doi = {10.1109/ccnc08.2007.199}, author = {Kolan, Prakash and Ram Dantu} } @article {37, title = {Nuisance Level of a Voice Call}, journal = {ACM Trans. Multimedia Comput. Commun. Appl.}, volume = {5}, year = {2008}, pages = {6:1{\textendash}6:22}, abstract = {

In our everyday life, we communicate with many people such as family, friends, neighbors, and colleagues. We communicate with them using different communication media such as email, telephone calls, and face-to-face interactions. While email is not real-time and face-to-face communications require geographic proximity, voice and video communications are preferred over other modes of communication. However, real-time voice/video calls may create nuisance to the receiver. In this article, we describe a mathematical model for computing nuisance level of incoming voice/video calls. We computed the closeness and nuisance level using the calling patterns between the caller and the callee. To validate the nuisance model, we collected cell phone call records of real-life people at our university and computed the nuisance value for all voice calls. We validated the nuisance levels using the feedback from those real-life people. Such a nuisance model is useful for predicting unwanted voice and video sessions in an IP communication network.

}, keywords = {behavior, Multimedia communications, nuisance, presence, Security, tolerance, unwantedness}, issn = {1551-6857}, doi = {10.1145/1404880.1404886}, url = {http://doi.acm.org/10.1145/1404880.1404886}, author = {Kolan, Prakash and Ram Dantu and Cangussu, Jo{\~a}o} } @conference {58, title = {Automatic Calibration Using Receiver Operating Characteristics Curves}, booktitle = {2007 2nd International Conference on Communication Systems Software and Middleware}, year = {2007}, month = {Jan}, abstract = {

Application-level filters, such as e-mail and VoIP spam filters, that analyze dynamic behavior changes are replacing static signature-recognition filters. These application-level filters learn behavior and use that knowledge to filter unwanted requests. Because behavior of a service request{\textquoteright}s participating entities changes rapidly, filters must adapt quickly by using end user{\textquoteright}s preferences about receiving that service request message. Many adaptive filters learn from the participating entities{\textquoteright} behavior; however, none configure themselves automatically to an end user{\textquoteright}s changing tolerance levels. Also, filter administrators cannot manually change the threshold for each service request in real time. Traditional adaptive filters fail when administrators must optimize multiple filter thresholds manually and often. Thus, to improve a filter{\textquoteright}s learning, we must automate its threshold-update process. We propose an automatic threshold-calibration mechanism using Receiver Operating Characteristics (ROC) curves that updates the threshold based on an end user{\textquoteright}s feedback. To demonstrate the mechanism{\textquoteright}s real-time applicability, we integrated it in a Voice over IP (VoIP) spam filter that analyzes incoming Spam over IP Telephony (SPIT) calls. Using this mechanism, we observed good improvement in the VoIP spam filter{\textquoteright}s accuracy. Further, computing and updating the optimum threshold in realtime does not impede the filter{\textquoteright}s temporal performance because we update thresholds after each call{\textquoteright}s completion. Because we reach an optimum threshold for any initial setting, this mechanism works efficiently when we cannot predict end-user behavior. Furthermore, automatic calibration proves efficient when using multiple threshold values.

}, keywords = {adaptive filters, application-level filter learning, automatic threshold-calibration mechanism, behavior, calibration, Computer science, Computer worms, Electronic mail, end user feedback, Filtering, information filtering, information filters, Internet telephony, learning (artificial intelligence), Protocols, receiver operating characteristics curves, sensitivity analysis, service request message, telecommunication traffic, Threshold, tolerance, Viruses (medical)}, doi = {10.1109/COMSWA.2007.382484}, author = {Kolan, Prakash and Vaithilingam, Ram and Ram Dantu} } @conference {34, title = {Classification of Attributes and Behavior in Risk Management Using Bayesian Networks}, booktitle = {2007 IEEE International Conference on Intelligence and Security Informatics (ISI) }, year = {2007}, month = {05/2007}, address = {New Brunswick, NJ}, abstract = {

Security administration is an uphill task to implement in an enterprise network providing secured corporate services. With the slew of patches being released by network component vendors, system administrators require a barrage of tools for analyzing the risk due to vulnerabilities in those components. In addition, criticalities in patching some end hosts raises serious security issues about the network to which the end hosts are connected. In this context, it would be imperative to know the risk level of all critical resources keeping in view the everyday emerging new vulnerabilities. We hypothesize that sequence of network actions by attackers depends on their social and attack profile (behavioral resources such as skill level, time, and attitude). To estimate the types of attack behavior, we surveyed individuals for their ability and attack intent. Using the individuals{\textquoteright} responses, we determined their behavioral resources and classified them as having opportunist, hacker, or explorer behavior. The profile behavioral resources can be used for determining risk by an attacker having that profile. Thus, suitable vulnerability analysis and risk management strategies can be formulated to efficiently curtail the risk from different types of attackers.

}, keywords = {attack behaviour, attack graphs, attack intent, Bayesian network, behavior, belief networks, explorer behavior, hacker behavior, opportunist behavior, profile behavioral resource classification, risk management, Security, security administration, vulnerability analysis}, doi = {10.1109/ISI.2007.379536}, author = {Ram Dantu and Kolan, Prakash and Akl, Robert and Loper, Kall} } @article {47, title = {HIPAA: Securing Medical Networks}, journal = {Network Security}, volume = {2007}, year = {2007}, pages = {13{\textendash}16}, abstract = {

The Health Information Portability and Accountability Act of 1996 (HIPAA) imposes strict regulations on healthcare institutions and commercial vendors to indemnify clinical data against unscrupulous users. Security vulnerabilities concerning hospital information systems not only negatively impact patient healthcare, but may also represent a potential federal violation. For a comprehensive understanding of the security of a radiology communication network, a detailed survey of the Picture Archiving and Communication Systems (PACS) was compiled. In this paper, we present survey results and a set of recommendations for implementing PACS security.

}, issn = {1353-4858}, doi = {10.1016/S1353-4858(07)70055-7}, url = {http://dx.doi.org/10.1016/S1353-4858(07)70055-7}, author = {Ram Dantu and Oosterwijk, Herman and Kolan, Prakash and Husna, Husain} } @article {10, title = {Socio-technical Defense Against Voice Spamming}, journal = {ACM Trans. Auton. Adapt. Syst.}, volume = {2}, year = {2007}, abstract = {

Voice over IP (VoIP) is a key enabling technology for migration of circuit-switched PSTN (Public Switched Telephone Network) architectures to packet-based networks. One problem of the present VoIP networks is filtering spam calls referred to as SPIT (Spam over Internet Telephony). Unlike spam in e-mail systems, VoIP spam calls have to be identified in real time. Many of the techniques devised for e-mail spam detection rely upon content analysis, and in the case of VoIP, it is too late to analyze the content (voice) as the user would have already attended the call. Therefore, the real challenge is to block a spam call before the telephone rings. In addition, we believe it is imperative that spam filters integrate human behavioral aspects to gauge the legitimacy of voice calls. We know that, when it comes to receiving or rejecting a voice call, people use the social meaning of trust, reputation, friendship of the calling party and their own mood. In this article, we describe a multi-stage, adaptive spam filter based on presence (location, mood, time), trust, and reputation to detect spam in voice calls. In particular, we describe a closed-loop feedback control between different stages to decide whether an incoming call is spam. We further propose formalism for voice-specific trust and reputation analysis. We base this formal model on a human intuitive behavior for detecting spam based on the called party{\textquoteright}s direct and indirect relationships with the calling party. No VoIP corpus is available for testing the detection mechanism. Therefore, for verifying the detection accuracy, we used a laboratory setup of several soft-phones, real IP phones and a commercial-grade proxy server that receives and processes incoming calls. We experimentally validated the proposed filtering mechanisms by simulating spam calls and measured the filter{\textquoteright}s accuracy by applying the trust and reputation formalism. We observed that, while the filter blocks a second spam call from a spammer calling from the same end IP host and domain, the filter needs only a maximum of three calls{\textemdash}even in the case when spammer moves to a new host and domain. Finally, we present a detailed sensitivity analysis for examining the influence of parameters such as spam volume and network size on the filter{\textquoteright}s accuracy

}, keywords = {behavior, reputation, SIP (Session Initiation Protocol), SPIT (Spam over IP Telephony), tolerance, Trust}, issn = {1556-4665}, doi = {10.1145/1216895.1216897}, url = {http://doi.acm.org/10.1145/1216895.1216897}, author = {Kolan, Prakash and Ram Dantu} } @conference {7, title = {Detecting Spam in VoIP Networks}, booktitle = {Proceedings of the Steps to Reducing Unwanted Traffic on the Internet Workshop}, year = {2005}, publisher = {USENIX Association}, organization = {USENIX Association}, address = {Berkeley, CA, USA}, abstract = {

Voice over IP (VoIP) is a key enabling technology for the migration of circuit-switched PSTN architectures to packetbased networks. The problem of spam in VoIP networks has to be solved in real time compared to e-mail systems. Many of the techniques devised for e-mail spam detection rely upon content analysis and in the case of VoIP it is too late to analyze the media after picking up the receiver. So we need to stop the spam calls before the telephone rings. From our observation, when it comes to receiving or rejecting a voice call people use social meaning of trust and reputation of the calling party. In this paper, we describe a multi-stage spam filter based on trust, and reputation for detecting the spam. In particular we used closed loop feedback between different stages in deciding if the incoming call is a spam or not. For verifying the concepts, we used a laboratory setup of several thousand soft-phones and a commercial grade proxy server. We verified our filtering mechanisms by simulating the spam calls and measured the accuracy of the filter. Results show that multistage feedback loop fares better than any single stage. Also, the larger the network size, the harder to detect a spam call. Further work includes understanding the behavior of different controlling parameters in trust and reputation calculations and deriving meaningful relationships between them.

}, url = {http://dl.acm.org/citation.cfm?id=1251282.1251287}, author = {Ram Dantu and Kolan, Prakash} } @conference {24, title = {Risk Management Using Behavior Based Bayesian Networks}, booktitle = {Proceedings of the 2005 IEEE International Conference on Intelligence and Security Informatics}, year = {2005}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Atlanta, GA}, abstract = {

Security administration is an uphill task to implement in an enterprise network providing secured corporate services. With the slew of patches being released by Microsoft, HP and other vendors, system administrators require a barrage of tools for analyzing the risk due to these vulnerabilities. In addition to this, criticalities in patching some end hosts (eg., in hospitals) raises serious security issues about the network to which the end hosts are connected. In this context, it would be imperative to know the risk level of all critical resources (e.g., Oracle Server in HR department) keeping in view the everyday emerging new vulnerabilities. We hypothesize that sequence of network actions by an attacker depends on the social behavior (e.g., skill level, tenacity, financial ability). We extended this and formulated a mechanism to estimate the risk level of critical resources that may be compromised based on attacker behavior. This estimation is accomplished using behavior based attack graphs. These graphs represent all the possible attack paths to all the critical resources. Based on these graphs, we calculate the risk level of a critical resource using Bayesian methodology and periodically update the subjective beliefs about the occurrence of an attack. Such a calculated risk level would be a measure of the vulnerability of the resource and it forms an effective basis for a system administrator to perform suitable changes to network configuration. Thus suitable vulnerability analysis and risk management strategies can be formulated to efficiently curtail the risk from different types of attackers (script kiddies, hackers, criminals and insiders).

}, isbn = {3-540-25999-6, 978-3-540-25999-2}, doi = {10.1007/11427995_10}, url = {http://dx.doi.org/10.1007/11427995_10}, author = {Ram Dantu and Kolan, Prakash} } @conference {11, title = {Risk Management Using Behavior Based Attack Graphs}, booktitle = {Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC{\textquoteright}04) Volume 2 - Volume 2}, year = {2004}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Washington, DC, USA}, abstract = {

Security administration is an uphill task to implement inan enterprise network providing secured corporateservices. With the slew of patches being released byMicrosoft, HP and other vendors, system administratorsrequire a barrage of tools for analyzing the risk due tothese vulnerabilities. In addition to this, criticalities inpatching some end hosts (eg., in hospitals) raises serioussecurity issues about the network to which the end hostsare connected. In this context, it would be imperative toknow the risk level of all critical resources (e.g., OracleServer in HR department) keeping in view the everydayemerging new vulnerabilities. We hypothesize thatsequence of network actions by an attacker depends onthe social behavior (e.g., skill level, tenacity, financialability). By verifying our hypothesis on hacker emailcommunications, we extended this methodology andcalculated risk level for a small network. Towards thisgoal, we formulated a mechanism to estimate the risklevel of critical resources that may be compromisedbased on attacker behavior. This estimation isaccomplished using behavior based attack graphs. Thesegraphs represent all the possible attack paths to all thecritical resources. Based on these graphs, we calculatethe risk level of a critical resource using Bayesianmethodology and periodically update the subjectivebeliefs about the occurrence of an attack Such acalculated risk level would be a measure of thevulnerability of the resource and it forms an effectivebasis for a system administrator to perform suitablechanges to network configuration. Thus suitablevulnerability analysis and risk management strategiescan be formulated to efficiently curtail the risk fromdifferent types of attacker (script kiddies, hackers,criminals and insiders).

}, isbn = {0-7695-2108-8}, url = {http://dl.acm.org/citation.cfm?id=977403.978389}, author = {Ram Dantu and Loper, Kall and Kolan, Prakash} }