This paper introduces a hybrid scheme that combines the advantages of deep belief network and support vector machine. An application of intrusion detection imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the intrusion into two outcomes: normal or attack, and the attacks fall into four classes; R2L, DoS, U2R, and Probing. First, we...

Cyber security threats have become increasingly sophisticated and complex. Intrusion detection which is one of the main problems in computer security has the main goal to detect infrequent access or attacks and to protect internal networks. A new hybrid intrusion detection method combining multiple classifiers for classifying anomalous and normal activities in the computer network is presented....

Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing the events occurring in a computer system in order to detect different security violations. The aim of this paper is to classify activities of a system into two major categories: normal and abnormal activities. In this paper we present the comparison of different classifi...

Feature selection is a preprocessing phase to machine learning, which leads to increase the classification accuracy and reduce its complexity. However, the increase of data dimensionality poses a challenge to many existing feature selection methods. This paper formulates and validates a method for selecting optimal feature subset based on the analysis of the Pearson correlation coefficients. We...

With the increase in Internet users the number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the network. This has led to the research area of intrusion detection which essentially analyzes the network traffic and tries to determine normal and abnormal patterns of behavior.In this paper, we have analyze...

Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e...

Wireless Sensor Networks (WSNs) have become a key technology for the IoT and despite obvious benefits, challenges still exist regarding security. As more devices are connected to the internet, new cyber attacks are emerging which join well-known attacks posing significant threats to the confidentiality, integrity and availability of data in WSNs. In this work, we investigated two computational ...

Computer network assets expose to various cyber threats in today’s digital era. Network Anomaly Detection Systems (NADS) play a vital role protecting the purview of security. Intrusion detection systems data are imbalanced and high dimensioned, affecting models’ performance classifying malicious traffic. This paper uses denoising autoencoder (DAE) for feature selection reduce dimension. To bala...

-Intrusion detection system must be capable of known and unknown vulnerabilities. We already studied the previous problems which includes detection of known vulnerabilities and unknown vulnerabilities. In order to obtain good accuracy a relevant or efficient dataset should be there to detect the known attacks and unknown attacks. Therefore, there are numerous security systems and intrusion dete...