With the enormous growth of computer networks and the huge increase in the number of applications that rely on it, network security is gaining increasing importance. Moreover, almost all computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), a...

Intrusion Detection systems are increasingly a key part of system defense. Various approaches to Intrusion Detection are currently being used but they are relatively ineffective. Among the several soft computing paradigms, we investigated genetic algorithms and neural networks to model fast and efficient Intrusion Detection Systems. With the feature selection process proposed it is possible to ...

IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been used in ...

Intrusions constitute one of the main issues in computer network security. Through malicious actions, hackers can have unauthorised access that compromises the integrity, the confidentiality, and the availability of resources or services. Intrusion detection systems (IDSs) have been developed to monitor and filter network activities by identifying attacks and alerting network administrators. Di...

Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm fo...

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher de...

In this paper, we introduce new learning algorithms for reducing false positives in intrusion detection. It is based on decision tree-based attribute weighting with adaptive naïve Bayesian tree, which not only reduce the false positives (FP) at acceptable level, but also scale up the detection rates (DR) for different types of network intrusions. Due to the tremendous growth of network-based se...

In the present paper, we show that a multi-classification process can be significantly enhanced by selecting an optimal set of the features used as input for the training operation. The selection of such a subset will reduce the dimensionality of the data samples and eliminate the redundancy and ambiguity introduced by some attributes. The used classifier can then operate only on the selected f...

Traditional Network Intrusion Detection Systems (NIDSs) rely on either specialized signatures of previously seen attacks, or on expensive and difficult to produce labeled traffic datasets for user-profiling to hunt out network attacks. Despite being opposite in nature, both approaches share a common downside: they require the knowledge provided by an external agent, either in terms of signature...

* Corresponding author. This work is supported by the National Natural Science Foundation of China under Grant 60303012 Abstract: In recent years, intrusion detection has emerged as an important technique for network security. Due to the large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, to optimize the performance of intrusion detection syste...