One of the main problems in Network Intrusion Detection comes from constant rise of new attacks, so that not enough labeled examples are available for the new classes of attacks. Traditional Machine Learning approaches hardly address such problem. This can be overcome with Zero-Shot Learning, a new approach in the field of Computer Vision, which can be described in two stages: the Attribute Lea...

Nowadays, the massive increment in applications running on a computer and excessive in network services forces to take convenient security policies into an account. Many methods of intrusion detection proposed to provide security in a computer system and network using data mining methods. These methods comprise of the outlier, unsupervised and supervised methods. As we know, each data mining me...

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...

Computer networks are nowadays subject to an increasing number of attacks. Intrusion Detection Systems (IDS) are designed to protect them by identifying malicious behaviors or improper uses. Since the scope is different in each case (register already-known menaces to later recognize them or model legitimate uses to trigger when a variation is detected), IDS have failed so far to respond against...

During the last decade, Intrusion Detection Systems (IDSs) have played an important role in defending critical computer systems and networks from cyber-attacks. Anomaly detection techniques have received a particularly great amount of attention because they offer intrinsic ability to detect unknown attacks. In this paper, we propose an enhanced hybrid anomaly detection approach based on negativ...

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...

DoS attack tools have become increasingly sophisticated challenging the existing detection systems to continually improve their performances. In this paper we present a victimend DoS detection method based on Artificial Neural Networks (ANN). In the proposed method a Feed-forward Neural Network (FNN) is optimized to accurately detect DoS attack with minimum resources usage. The proposed method ...

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...