A Residual Learning-Based Network Intrusion Detection System

Intrusion Detection based on a Novel Hybrid Learning Approach

Information security and Intrusion Detection System (IDS) plays a critical role in the Internet. IDS is an essential tool for detecting different kinds of attacks in a network and maintaining data integrity, confidentiality and system availability against possible threats. In this paper, a hybrid approach towards achieving high performance is proposed. In fact, the important goal of this paper ...

NetSTAT: A Network-based Intrusion Detection System

Fast Learning Neural Network Intrusion Detection System

Assuring the security of networks is an increasingly challenging task. The number of online services and migration of traditional services like stocktrading and online payments to the Internet is still rising. On the other side, criminals are attracted by the values of business data, money transfers, etc. Therefore, safeguarding the network infrastructure is essential. As Intrusion Detection Sy...

Machine Learning in Network Intrusion Detection System

During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP’99 is the mostly widely used data set for the evaluation of these systems. As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a cr...

A Deep Learning Approach for Network Intrusion Detection System

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organization. However, many challenges arise while developing a flexible and effective NIDS for unforeseen and unpredictable attacks. In this work, we propose a deep learning based approach to implement such an effective and flexible NIDS. We use Self-taught Learning (STL), a dee...