An intrusion detection system (IDS) inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. Soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems. Knowledge Discovery...

Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomal...

Information systems need to be constantly monitored and audited; analysis of security event logs in heavy traffic networks is a challenging task. In this paper we considered Differential Evolution for the intrusion detection problem. We used NSL_KDD dataset for our experiments which is derived from the standard KDD CUP 99 Intrusion Dataset. We also provided the comparative results of the differ...

In order to improve network intrusion detection precision, this paper proposed a network intrusion detection model based on simultaneous selecting features and parameters of support vector machine (SVM) by particle swarm optimization (PSO) algorithm. Firstly, the features and parameters of SVM are coded to particle, and then the PSO is used to find the optimal features and SVM parameters by col...

Computer networks have experienced an explosive growth over the past few years and have become the targets for hackers and intruders. An intrusion detection system's main goal is to classify activities of a system into two major categories: normal activity and suspicious or intrusive activity. The objective of this paper is to expose ANFIS as a neuro-fuzzy classifier to detect intrusions in com...

Developmening systems applying machine learning and data mining techniques is one of the approaches to combating network intrusion.Many IDS Intrusion Detection Systems)suffer from a high rate of false alarms and missed intrusions.Tha challenge is to be able to improve the intrusion detection rate at a reduced false positive rate. To counter imbalance in data, a combination of oversampling (synt...

Intrusion Detection is the task of detecting, preventing and possibly reacting to intrusion in a network based computer systems. This paper investigates the application of the Feed Forward Neural Network trained by Back Propagation algorithm for intrusion detection. Mutual Information based Feature Selection method is used to identify the important features of the network. The developed network...

Due to a growing number of the computer networks in recent years, there has been an increasing interest in the intrusion detection systems (IDSs). In this paper we have proposed a method applied to the instance selection from KDD CUP 99 dataset which is used for evaluating the anomaly detection techniques. In order to determine the performance of proposed method in the dataset reduction, a feed...

The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm. Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize...

Anomaly detection approaches build models of normal data and detect deviations from the normal model in observed data. Anomaly detection applied to intrusion detection and computer security has been an active area of research. The major benefit of anomaly detection algorithms is their ability to potentially detect unforeseen attacks. In this paper, a novel weighted support vector clustering alg...