This paper introduces a new similarity measure, the covering similarity, which we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pairwise similarity can also be directly derived from the covering similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed which uses a suffix-tree data structure, but other implementations, based on suffix array for instance, are possible and are possibly necessary for handling very large-scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of host-based intrusion detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state-of-the-art methods, according to the comparative study, we have carried out based on three challenging similarity measures commonly used for string processing, or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls.
In a broad sense we address anomaly detection in sequential data @cite_49 while focusing on intrusion detection in cyber-physical systems. Intrusion @cite_2 refers to possible security breaches in (cyber-)systems, namely malicious activity or policy violations. It covers both intrusions , i.e. attacks from the outside, and misuse, i.e. attacks from within the system. An intrusion detection system (IDS) is thus a device that monitors a system for detecting potential intrusions. The IDS will be referred to as NIDS if the detection takes place on a network and HIDS if it takes place on a host of a network. Furthermore, we distinguish i) signature-based IDS approaches, that detects attacks by looking for predefined specific patterns, such as byte sequences in network packets, or known malicious sequences of instructions used by malware, to ii) anomaly-based intrusion detection systems that were primarily introduced to detect unknown attacks (zero-day attacks).
@cite_49 @cite_2
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The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems. Network security has become an essential component of any computer network. Despite significant advances having been made on network-based intrusion prevention and detection, ongoing attacks penetrating network-based security mechanisms have been reported. It is being realized that network-based security mechanisms such as firewalls or intrusion detection systems (IDS) are not effective in detecting certain attacks such as insider attacks and attacks without generating significant network traffic. The trend of network security will be to merge host-based IDS (HIDS) and networkbased IDS (NIDS). This chapter will provide the fundamentals of host-based anomaly IDS as well as their developments. A new architectural framework is proposed for intelligent integration of multiple detection engines. The novelty of this framework is that it provides a feedback loop so that one output from a detection engine can be used as an input for another detection engine. It is also illustrated how several schemes can be derived from this framework. New research topics for future research are discussed.