Network intrusion detection is the problem of detecting anomalous network connections caused by intrusive activities. Many intrusion detection systems proposed before use both normal and intrusion data to build their classifiers. However, intrusion data are usually scarce and difficult to collect. We propose to solve this problem using a novelty detection approach. In particular, we propose to ...

High-dimensional intrusion detection data concentration information redundancy results in lower processing velocity of intrusion detection algorithm. Accordingly, the current study proposes an intrusion feature selection algorithm based on particle swarm optimization (PSO). Analyzing the features of the relevance between network intrusion data allows the PSO algorithm to optimally search in a f...

Computer and network security has received and will still receive much attention. Any unexpected intrusion will damage the network. It is therefore imperative to detect the network intrusion to ensure the normal operation of the internet. There are many studies in the intrusion detection and intrusion patter recognition. The artificial neural network (ANN) has proven to be powerful for the intr...

Recently, devices in real-time systems, such as residential facilities, vehicles, factories, and social infrastructure, have been increasingly connected to communication networks. Although these provide administrative convenience enable the development of more sophisticated control critical cybersecurity concerns challenges remain. In this paper, we propose a hybrid anomaly detection method tha...

Assuring secure and reliable operation of networks has become a priority research area these days because of ever growing dependency on network technology. Intrusion detection systems (IDS) are used as the last line of defense. Intrusion Detection System identifies patterns of known intrusions (misuse detection) or differentiates anomalous network data from normal data (anomaly detection). In t...

In today’s world, almost everybody is affluent with computers and network based technology is growing by leaps and bounds. So, network security has become very important, rather an inevitable part of computer system. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied...

In this paper we propose a framework for intrusion detection called Fuzzy Agent-Based Intrusion Detection System . A unique feature of this model is that the agent uses the fuzzy logic to process log files. This reduces the overhead in a distributed intrusion detection system. We have developed an agent communication architecture that provides a prototype implementation. This paper discusses th...

Intrusion Detection System (IDS) which is increasing the key element of system security is used to identify the malicious activities in a computer system there are different approaches being employed in intrusion detection systems. The prediction process may produce false alarms in many anomaly based intrusion detection systems. With the concept of fuzzy logic, the false alarm rate in establish...

Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is discussed.