Data Fusion-Based Machine Learning Architecture for Intrusion Detection

A Hybrid Machine Learning Method for Intrusion Detection

Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...

Machine Learning for Intrusion Detection

Detection of unknown attacks in network traffic is gaining increasing importance as modern attacks are characterized by high variabilities and mutation rates. Traditional signature-based intrusion detection systems (IDS) are not able to detect unknown attacks due to failing availability of appropriate signatures. We present an alternative approach based on machine learning techniques which enab...

A Generic Architecture for Fusion-Based Intrusion Detection Systems

A Virtual Machine Introspection Based Architecture for Intrusion Detection

Today’s architectures for intrusion detection force the IDS designer to make a difficult choice. If the IDS resides on the host, it has an excellent view of what is happening in that host’s software, but is highly susceptible to attack. On the other hand, if the IDS resides in the network, it is more resistant to attack, but has a poor view of what is happening inside the host, making it more s...

Intrusion Detection: Supervised Machine Learning

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher de...