Feature selection for filtering HTTP-traffic in Web application firewalls (WAFs) is an important task. We focus on the GenericFeature-Selection (GeFS) measure [4], which was successfully tested on low-level package filters, i.e., the KDD CUP’99 dataset. However, the performance of the GeFS measure in analyzing high-level HTTP-traffic is still unknown. In this paper we study the GeFS measure for...

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algo...

Intrusion detection system is automatic system which recognize intrusions of computers or computer network systems. The existing security detection systems have many problems such as wrong detection of intrusions, false intrusions, poor real-time performance. To solve these problems, this paper improves the particle swarm optimization algorithm and presents an attribute-weighted distance calcul...

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, an...

Computer systems vulnerabilities such as software bugs are often exploited by malicious users to intrude into information systems. With the recent growth of the Internet such security limitations are becoming more and more pressing. One commonly used defense measure against such malicious attacks in the Internet are Intrusion Detection Systems (IDSs). Due to increasing incidents of cyber attack...

One of the central challenges with computer security is determining the difference between normal and potentially harmful behavior. For decades, developers have protected their systems using classical methods. However, the growth and complexity of computer systems or networks to protect require the development of automated and adaptive defensive tools. Promising solutions are emerging with biol...

Intrusion Detection Systems have been used along with various techniques to detect intrusions in networks, distributed databases and web databases. However, all these systems are able to detect the intruders with high false alarm rate. In this paper, we propose a new intrusion detection model using the combination of outlier detection method and multiclass SVM classification. For this purpose, ...

Attacks against computer networks are evolving rapidly. Conventional intrusion detection system based on pattern matching and static signatures have a significant limitation since the signature database should be updated frequently. The unsupervised learning algorithm can overcome this limitation. Ant Clustering Algorithm (ACA) is a popular unsupervised learning algorithm to classify data into ...

Intrusion Detection System (IDS) has been an important tool for network security. However, existing IDSs that have been proposed do not perform well for anomaly traffics especially Remote to Local (R2L) attack which is one of the most concerns. We thus propose a new efficient technique to improve IDS performance focusing mainly on R2L attacks. The Principal Component Analysis (PCA) and Simplifi...

The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches have been conducted and attempted to overcome this problem, there are some constraints in these work...