Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behaviour or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with ...

The internet of things (IoT) is a collection common physical which can communicate and synthesize data utilizing network infrastructure by connecting to the internet. IoT networks are increasingly vulnerable security breaches as their popularity grows. Cyber attacks among most popular severe dangers security. Many academics interested in enhancing systems. Machine learning (ML) approaches were ...

Intrusion detection is one of the main challenges in wireless systems especially in Internet of things (IOT) based networks. There are various attack types such as probe, denial of service, remote to local and user to root. In addition to known attacks and malicious behaviors, there are various unknown attacks that some of them have similar behavior with respect to each other or mimic the norma...

Recently by developing the technology, the number of network-based servicesis increasing, and sensitive information of users is shared through the Internet.Accordingly, large-scale malicious attacks on computer networks could causesevere disruption to network services so cybersecurity turns to a major concern fornetworks. An intrusion detection system (IDS) could be cons...

Abstract Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources services, are spreading fast can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, application machine learning methods this area enrich intrusion efficiency. Various methods, such as pattern recognition from ev...

Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of in almost every field, provision services by private public institutions over network servers, cloud technologies database are completely remotely controlled. Due to these requirements systems, malicious software users, unfortunately, their interest areas. Some organizations exposed hundreds o...

As a security defense technique to protect networks from attacks, network intrusion detection model plays crucial role in the of computer systems and networks. Aiming at shortcomings complex feature extraction process insufficient information existing models, an named FCNN-SE, which uses fusion convolutional neural (FCNN) for stacked ensemble (SE) classification, is proposed this paper. The mai...

This research present a proposal Hybrid Multilevel Network Intrusion Detection System (HMNIDS) which is a "hybrid multilevel IDS", is hybrid because use misuse and anomaly techniques in intrusion detection, and is multilevel since it apply the two detection techniques hierarchal in two levels. First level applies anomaly ID technique using Support Vector Machine (SVM) for detecting the traffics...

In this study, I evaluate the performance of diagonal Confidence-Weighted (CW) online linear classification on the KDD Cup 1999 dataset for network intrusion detection systems (NIDS). This is a compatible relationship due to the large number of instances in NIDS datasets, as well as the constantly changing feature distributions. CW learning achieves approximately 92% accuracy on the KDD dataset...