Network-based attacks have become common and sophisticated. For this reason, intrusion detection systems are now shifting their focus from the hosts and their operating systems to the network itself. Network-based intrusion detection is challenging because network auditing produces large amounts of data, and different events related to a single intrusion may be visible in different places on th...

In modern era, the most pressing issue facing society is protection against cyberattacks on networks. The frequency of cyber-attacks in present world makes problem providing feasible security to computer system from potential risks important and crucial. Network cannot be effectively monitored protected without use intrusion detection systems (IDSs). DLTs (Deep learning methods) MLTs (machine t...

Recently machine learning based intrusion detection system developments have been subjected to extensive researches because they can detect both misuse detection and anomaly detection. In this paper, we propose an AdaBoost based algorithm for network intrusion detection system with single weak classifier. In this algorithm, the classifiers such as Bayes Net, Naïve Bayes and Decision tree are us...

Network security is one of the most important non-functional requirements in a system. Over the years, many software solutions have been developed to enhance network security. Intrusion Detection System (IDS) we have provided an overview of different types of intrusion Detection Systems, the advantages and disadvantages of the same. The need for IDS in a system environment and the generic block...

Traditional intrusion detection techniques examine all features to detect intrusion or misuse patterns. But all features are not relevant and some of them may be redundant and contribute little to the detection process. Irrelevant and redundant features may lead to complex intrusion detection model as well as poor detection accuracy. In this paper, we propose and investigate a novel hybrid feat...

The continuous evolution of the types of attacks against computer networks suggests a paradigmatic shift from misuse based intrusion detection system to anomaly based systems. Unsupervised learning algorithms are natural candidates for this task, but while they have been successfully applied in host-based intrusion detection, network-based applications are more difficult, for a variety of reaso...

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Intrusion detection is a problem of great significance to protecting information systems security. An intruder may move between multiple nodes in the network to conceal the origin of attack. Distributed intrusion detection and prevention plays an increasingly important role in securing computer networks. To overcome the limitations of conventional intrusion detection systems, alerts are made in...

Artificial Intelligence could make the use of Intrusion Detection Systems a lot easier than it is today. They could learn the preferences of the security officers and show the kind of alerts first that the officer has previously been most interested. As always, the hardest thing with learning AIs, is to make them learn the right things. AIs could learn the same things as a rule-based system by ...

Intruders damage or steal valuable information by either bypassing security tools or penetrating through them, necessitating the need to detect such intrusion attempts especially in case of multi-event based distributed attacks spanning over a considerable amount of time. In this paper ecology of agents is being suggested that uses class hierarchy to define complex intrusions. The source and ta...