Abstract. At present, alert correlation techniques do not make full use of the information that is available. We propose a data model for IDS alert correlation called M2D2. It supplies four information types: information related to the characteristics of the monitored information system, information about the vulnerabilities, information about the security tools used for the monitoring, and information about the events observed. M2D2 is formally defined. As far as we know, no other formal model includes the vulnerability and alert parts of M2D2. Three examples of correlations are given. They are rigorously specified using the formal definition of M2D2. As opposed to already published correlation methods, these examples use more than the events generated by security tools; they make use of many concepts formalized in M2D2. 1

When performing packet-level analysis in intrusion detection, analysts often lose sight of the "big picture" while examining these low-level details. In order to prevent this loss of context and augment the available tools for intrusion detection analysis tasks, we developed an information visualization tool, the Time-based Network traffic Visualizer (TNV). TNV is grounded in an understanding of the work practices of intrusion detection analysts, particularly foregrounding the overarching importance of context and time in the process of intrusion detection analysis. The main visual component of TNV is a matrix showing network activity of hosts over time, with connections between hosts superimposed on the matrix, complemented by multiple, linked views showing port activity and the details of the raw packets. Providing low-level textual data in the context of a high-level, aggregated graphical display enables analysts to examine packetlevel details within the larger context of activity. This combination has the potential to facilitate the intrusion detection analysis tasks and help novice analysts learn what constitutes "normal" on a particular network.

Although the ability to model and infer Attacker Intent, Objectives and Strategies (AIOS) may dramatically advance the literature of risk assessment, harm prediction, and predictive or proactive cyber defense, existing AIOS inference techniques are ad hoc and system or application specific. In this paper, we present a general incentive-based method to model AIOS and a game theoretic approach to infer AIOS. On one hand, we found that the concept of incentives can unify a large variety of attacker intents; the concept of utilities can integrate incentives and costs in such a way that attacker objectives can be practically modeled. On the other hand, we developed a game theoretic AIOS formalization which can capture the inherent inter-dependency between AIOS and defender objectives and strategies in such a way that AIOS can be automatically inferred. Finally, we use a specific case study to show how AIOS can be inferred in real world attack-defense scenarios.

Malware detection is a crucial aspect of software security. Current malware detectors work by checking for “signatures,” which attempt to capture (syntactic) characteristics of the machine-level byte sequence of the malware. This reliance on a syntactic approach makes such detectors vulnerable to code obfuscations, increasingly used by malware writers, that alter syntactic properties of the malware byte sequence without significantly affecting their execution behavior. This paper takes the position that the key to malware identification lies in their semantics. It proposes a semantics-based framework for reasoning about malware detectors and proving properties such as soundness and completeness of these detectors. Our approach uses a trace semantics to characterize the behaviors of malware as well as the program being checked for infection, and uses abstract interpretation to “hide” irrelevant aspects of these behaviors. As a concrete application of our approach, we show that the semantics-aware malware detector proposed by Christodorescu et al. is complete with respect to a number of common obfuscations used by malware writers.

This paper describes a technique of applying Genetic Algorithm (GA) to network Intrusion Detection Systems (IDSs). A brief overview of the Intrusion Detection System, genetic algorithm, and related detection techniques is presented. Parameters and evolution process for GA are discussed in detail. Unlike other implementations of the same problem, this implementation considers both temporal and spatial information of network connections in encoding the network connection information into rules in IDS. This is helpful for identification of complex anomalous behaviors. This work is focused on the TCP/IP network protocols. 1.

Intrusion detection (ID) systems have become increasingly accepted as an essential layer in the information security infrastructure. However, there has been little research into understanding the human component of ID work. Currently, security analysts face an increasing workload as their environments expand and attacks become more frequent. We conducted contextual interviews with security analysts to gain an understanding of the people and work of ID. Our findings reveal that organizational changes must be combined with improved technical tools for effective, long-term solutions to the difficulties of scaling ID work. We propose a three-phase task model in which tasks could be decoupled according to requisite expertise. In particular, monitoring tasks can be separated and staffed by less experienced ID analysts with corresponding tool support. Thus, security analysts will be better able to cope with increasing security threats in their expanding networks. Additionally, organizations will be afforded more flexibility in hiring and training new analysts.

Abstract — We consider adhoc networks with multiple, mobile colluding intruders. We investigate the placement of the intrusion detection modules for misuse intrusion detection. Our goal is to maximize the detection performance subject to limitation in the computational resources. We mathematically formulate different detection objectives, and show that computing the optimal solution is NP-hard in each case. Thereafter, we propose a family of algorithms that approximate the optimal solution, and prove that some of these algorithms have guaranteeable approximation ratios. The algorithms that have analytically guaranteeable performance require re-computation every time the topology changes due to mobility. We next modify the computation strategy so as to seamlessly adapt to topological changes due to mobility. Using simulation we evaluate these algorithms, and identify the appropriate algorithms for different detection performance and resource consumption tradeoffs. I.

In this paper we introduce the idea of using behavioral biometrics in intrusion detection applications. We present a new biometrics-based technique, which can be used to detect intrusion without the need for any special hardware implementation and without forcing the user to perform any special actions. The technique is based on using “keystroke dynamics” and “mouse dynamics ” biometrics. We discuss the efficiency and applicability of such an approach. 1.

Intrusion detection has become an indispensable defense line in the information security infrastructure. However, every intrusion detection approach has been limited by their problems: signature-based intrusion detection can identify the known intrusions but cannot detect the novel intrusions, anomaly-based intrusion detection has the potential to detect all intrusions but has the limitation of a higher false alarm rate. For this reason, most existing intrusion detection techniques have not met the requirements for practical deployment. In this paper, we propose a theoretical basis for intrusion detection to argue about their principles and to analyze the existing problems for intrusion detection in a quantified manner. The root causes of these problems are identified as model inaccuracy and model incompleteness as well as the distinguishability lack in the features utilized. In addition, we also found that static analysis [1], with a properly selected feature vector, is a promising intrusion detection technique in principle because it can avoid the quality issue of its behavior models.

Abstract. Anomaly detection, detection of deviations from what is considered normal, is an important complement to misuse detection based on attack signatures. Anomaly detection in real-time places hard requirements on the algorithms used, making many proposed data mining techniques less suitable. ADWICE (Anomaly Detection With fast Incremental Clustering) uses the first phase of the existing BIRCH clustering framework to implement fast, scalable and adaptive anomaly detection. We extend the original clustering algorithm and apply the resulting detection mechanism for analysis of data from IP networks. The performance is demonstrated on the KDD data set as well as on data from a test network at a telecom company. Our experiments show a good detection quality (95 %) and acceptable false positives rate (2.8 %) considering the online, real-time characteristics of the algorithm. The number of alarms is then further reduced by application of the aggregation techniques implemented in the Safeguard architecture. 1