STATL is an extensible state/transition-based attack description language designed to support intrusion detection. The language allows one to describe computer penetrations as sequences of actions that an attacker performs to compromise a computer system. A STATL description of an attack scenario can be used by an intrusion detection system to analyze a stream of events and detect possible ongoing intrusions. Since intrusion detection is performed in different domains (i.e., the network or the hosts) and in different operating environments (e.g., Linux, Solaris, or Windows NT), it is useful to have an extensible language that can be easily tailored to different target environments. STATL defines domain-independent features of attack scenarios and provides constructs for extending the language to describe attacks in particular domains and environments. The STATL

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

Abstract—Alert correlation is a process that analyzes the alerts produced by one or more intrusion detection systems and provides a more succinct and high-level view of occurring or attempted intrusions. Even though the correlation process is often presented as a single step, the analysis is actually carried out by a number of components, each of which has a specific goal. Unfortunately, most approaches to correlation concentrate on just a few components of the process, providing formalisms and techniques that address only specific correlation issues. This paper presents a general correlation model that includes a comprehensive set of components and a framework based on this model. A tool using the framework has been applied to a number of well-known intrusion detection data sets to identify how each component contributes to the overall goals of correlation. The results of these experiments show that the correlation components are effective in achieving alert reduction and abstraction. They also show that the effectiveness of a component depends heavily on the nature of the data set analyzed. Index Terms—Intrusion detection, alert correlation, alert reduction, correlation data sets. 1

Web servers are ubiquitous, remotely accessible, and often misconfigured. In addition, custom web-based applications may introduce vulnerabilities that are overlooked even by the most security-conscious server administrators. Consequently, web servers are a popular target for hackers. To mitigate the security exposure associated with web servers, intrusion detection systems are deployed to analyze and screen incoming requests. The goal is to perform early detection of malicious activity and possibly prevent more serious damage to the protected site. Even though intrusion detection is critical for the security of web servers, the intrusion detection systems available today only perform very simple analyses and are often vulnerable to simple evasion techniques. In addition, most systems do not provide sophisticated attack languages that allow a system administrator to specify custom, complex attack scenarios to be detected. This paper presents WebSTAT, an intrusion detection system that analyzes web requests looking for evidence of malicious behavior. The system is novel in several ways. First of all, it provides a sophisticated language to describe multistep attacks in terms of states and transitions. In addition, the modular nature of the system supports the integrated analysis of network traffic sent to the server host, operating system-level audit data produced by the server host, and the access logs produced by the web server. By correlating different streams of events, it is possible to achieve more effective detection of web-based attacks.

This paper presents a hierarchical model to support attack specification and event abstraction in distributed intrusion detection. The model involves three concepts: system view, signature, and view definition. A system view provides an abstract interface of a particular type of information; defined on the instances of system views, a signature specifies certain distributed attacks or events to be monitored; a view definition is then used to derive information from the matches of a signature and presents it through a system view. With the three elements, the model provides a hierarchical framework for maintaining signatures, system views as well as event abstraction. As a benefit, the model allows generic signatures that can accommodate unknown variants of known attacks. Moreover, abstraction represented by a system view can be updated without changing either its specification or the signatures defined on the basis of it. This paper then presents a decen

Efforts toward automated detection and identification of multistep cyber attack scenarios would benefit significantly from a methodology and language for modeling such scenarios. The Correlated Attack Modeling Language (CAML) uses a modular approach, where a module represents an inference step and modules can be linked together to detect multistep scenarios. CAML is accompanied by a library of predicates, which functions as a vocabulary to describe the properties of system states and events. The concept of attack patterns is introduced to facilitate reuse of generic modules in the attack modeling process. CAML is used in a prototype implementation of a scenario recognition engine that consumes first-level security alerts in real time and produces reports that identify multistep attack scenarios discovered in the alert stream.

ADeLe is an attack description language designed to model a database of known attack scenarios. As the descriptions might contain executable attack code, it allows one to test the efficiency of given Intrusion Detection Systems (IDS). Signatures can also be extracted from the descriptions to configure a particular IDS.

Current distributed intrusion detection systems are not completely distributed with respect to data analysis because of the presence of centralized data analysis components. This deficiency has many undesirable implications. Here we present a framework for doing distributed intrusion detection with no centralized analysis components. Our approach uses agents that are the only data analysis components. Agents cooperate by using a hierarchical communication framework. This cooperation is driven by interests expressed by the agents.

Generally, the intruder must perform several actions, organized in an intrusion scenario, to achieve his or her malicious objective. We argue that intrusion scenarios can be modelled as a planning process and we suggest modelling a malicious objective as an attempt to violate a given security requirement.

In our present work we introduce the use of data fusion in the field of DoS anomaly detection. We present DempsterShafer 's Theory of Evidence (D-S) as the mathematical foundation for the development of a novel DoS detection engine. Based on a data fusion paradigm, we combine multiple evidence generated from simple heuristics to feed our D-S inference engine and attempt to detect flooding attacks.