Data mining has been used extensively and broadly by several network organizations. Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. IDS use the data mining techniques to analyze the resources from the...

Generalization error model provides a theoretical support for a classifier's performance in terms of prediction accuracy. However, existing models give very loose error bounds. This explains why classification systems generally rely on experimental validation for their claims on prediction accuracy. In this talk we will revisit this problem and explore the idea of developing a new generalizatio...

Self-organizing Maps (SOM) have been shown to be successful for intrusion detection. However, the static architecture and the lack of representation of hierarchical relations often results in low detection rates. The Growing Hierarchical SOM (GHSOM) addresses these limitations of SOM. In this paper, in order to obtain higher detection rate and improve the stability of intrusion detection, some ...

With the increasing worldwide network attacks, intrusion detection (ID) has become a popular research topic in last decade. Several artificial intelligence techniques such as neural networks and fuzzy logic have been applied in ID. The results are varied. The intrusion detection accuracy is the main focus for intrusion detection systems (IDS). Most research activities in the area aiming to impr...

In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). This algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also...

The critical data we share through computer network gets stolen by unethical means. This unethical way of accessing one’s data without proper authentication becomes intrusion. To solve this issue, in this paper we propose a new network intrusion detection method, Multi-Class Classification Multiple Criteria Linear Programming (MCC-MCLP) model. MCLP is a mathematical classification technique tha...

Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combinat...

A two-phase online anomaly detection method based on support vector clustering (SVC) in the presence of non-stationary data is developed in this paper which permits arbitrary-shaped data clusters to be precisely treated. In the first step, offline learning is performed to achieve an appropriate detection model. Then the current model dynamically evolves to match the rapidly changing real-world ...

While the network brings convenience to people, its own fragility offers intrusion opportunities for hackers and malicious attackers. Along with the diversity and complexity of intrusion attack, high performance intrusion detection techniques are required, and so the study of on-line detection, adaptive detection and multiclass detection techniques becomes current hotspot. To improve the perfor...

Intrusion Detection Systems (IDS) have become an important building block of any sound defense network infrastructure. Malicious attacks have brought more adverse impacts on the networks than before, increasing the need for an effective approach to detect and identify such attacks more effectively. In this study two learning approaches, K-Means Clustering and Naïve Bayes classifier (KMNB) are u...