text: 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.
  @cite_49: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
  @cite_2: Intrusion detection and prevention systems (IDPS) are focused on identifying possible incidents, logging information about them, attempting to stop them, and reporting them to security administrators. In addition, organizations use IDPSs for other purposes, such as identifying problems with security policies, documenting existing threats, and deterring individuals from violating security policies. This publication describes the characteristics of IDPS technologies and provides recommendations for designing, implementing, configuring, securing, monitoring, and maintaining them. The types of IDPS technologies are differentiated primarily by the types of events that they monitor and the ways in which they are deployed. This publication discusses the following four types of IDPS technologies: network-based, wireless, network behavior analysis (NBA), and host-based.
summary: 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).