Purity of such networks is a big concern, mostly for the applications where confidentiality has prime importance. Therefore, in order to function WSNs in a secure way, any kind of intrusions should be noticed before attackers can harm the network survey of the state-of-the-art in Intrusion Detection Systems (IDSs) that are proposed for WSNs is presented.RSA algorithm is using here for providing...

Accurate taxonomies are critical for the advancement of research fields. Taxonomies for intrusion detection systems (IDSs) are not fully agreed upon, and further lack convincing motivation of their categories. We survey and summarize previously made taxonomies for intrusion detection. Focusing on categories relevant for detection methods, we extract commonly used concepts and define three new a...

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 s...

Anomaly based intrusion detection is a critical research area, since it does not require any prior knowledge of attack signatures in order to detect them. Typically, intrusion detection systems (IDSs) consider a fix learning model, and generally do not contemplate any memory constraint. These assumptions may not be valid in real world scenarios, where the learning model may evolve and the avail...

Prevention of security breaches completely using the existing security technologies is unrealistic. As a result, intrusion detection is an important component in network security. However, many current intrusion detection systems (IDSs) are signature-based systems, The signature based IDS also known as misuse detection looks for a specific signature to match, signalling an intrusion. Provided w...

Computational Intelligence (CI) methods are increasingly being used for problem solving. This paper concerns using CI-type learning machines for intrusion detection, which is a problem of general interest to transportation infrastructure protection since a necessary task thereof is to protect the computers responsible for the infrastructure’s operational control, and an effective Intrusion Dete...

The basic task in intrusion detection system is to classify network activities as normal or abnormal while minimizing misclassification. In literature, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) to protect the special computer systems, vulnerable traffics cyber-attacks for computer networks. In addition, Support Vector Machine (SV...

Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information fit the requirements of building a good intrusion detection model. Here we want to provi...

Intrusion Detection Systems (IDS) is defined as a component that analyses system and user operations in computer and network systems in search of activities considered undesirable from security perspectives. Applying mobile agent (MA) to intrusion detection design is a recent development and it is aimed at effective intrusion detection in distributed environment. From the literature, it is clea...

Artificial Immune Systems (AISs) are biologically inspired problem solvers that have been used successfully as intrusion detection systems (IDSs). This paper describes how the design of AIS-based IDSs can be improved through the use of evolutionary hackers in the form of GENERTIA red teams (GRTs) to discover holes (in the form of type II errors) found in the immune system. GENERTIA is an intera...