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Anomalous payloads in network packets are a potential source for intrusion in computer networks. In this paper we come up with an efficient machine learning approach to detect anomalous payloads. The approach uses n-gram preprocessing to extract words included in the payload. Bayesian inference is used to learn normal and anomalous traffic patterns from the words extracted during training. During...
Recently, with wide use of computer systems, internet, and rapid growth of computer networks, the problem of intrusion detection in network security has become an important issue of concern. In this regard, various intrusion detection systems have been developed for using misuse detection and anomaly detection methodologies. These systems try to improve detection rates of variation in attack types...
Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can result in huge loss of data and make resources unavailable for legitimate users. With continuous growth of Internet users and traffic, the importance of Intrusion Detection System (IDS) for detection of DoS/DDoS network attacks has also grown. Different techniques such as data mining and pattern recognition are being used...
Anomaly detection in computer networks is an actively researched topic in the field of intrusion detection. The Internet Analysis System (IAS) is a software framework which provides passive probes and centralized backend services to collect purely statistical network data in distributed computer networks. This paper presents an empirical evaluation of the IAS data format for detecting anomalies, caused...
Internet became one of life's basics in these days. More networks are connected to the Internet every day, which increases the amount of valuable data and the number of resources that can be attacked. Some systems have been designed and developed to secure these data and prevent attacks on resources. Unfortunately, new attacks are being created everyday, which make it hard to design a system that...
A novel approach based on applying a modern meta-heuristic Gene Expression Programming (GEP) to detecting Web application attacks is presented in the paper. This class of attacks relates to malicious activity of an intruder against applications, which use a database for storing data. The application uses SQL to retrieve data from the database and Web server mechanisms to put them in a Web browser...
This paper presents a neural network-based intrusion detection method for the internet-based attacks on a computer network. Intrusion detection systems (IDS) have been created to predict and thwart current and future attacks. Neural networks are used to identify and predict unusual activities in the system. In particular, feedforward neural networks with the back propagation training algorithm were...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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