With developing technologies, network security is critical, predominantly active, and distributed ad hoc in networks. An intrusion detection system (IDS) plays a vital role cyber detecting malicious activities traffic. However, class imbalance has triggered challenging issue where many instances of some classes are more than others. Therefore, traditional classifiers suffer classifying result l...

<span lang="EN-US">The identification of abnormal situations in information and telecommunication systems is considered, based on analyze statistical network traffic packages. The method identifying an anomalous situation segmentation data sample proposed. aimed at using classifying algorithms that have the best quality indicators individual segments. proposed will be useful for monitorin...

In all technologies, including traditional computing and cloud computing, security has always been the primary concern. recent years, become widely accepted on a global scale. Cyber attacks aimed at it have increased along with its widespread acceptance. Although ample research is done in domain based rigid fundamentals, advancing network create need for an advanced mechanism. Also, multiclass ...

The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly wireless networks (WSNs). Dynamic routing information communication and unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools can detect prevent system vulnerabilities network. Unfortunately, there no possib...

As an important part of intrusion detection, feature selection plays a significant role in improving the performance detection. Krill herd (KH) algorithm is efficient swarm intelligence with excellent data mining. To solve problem low efficiency and high false positive rate detection caused by increasing high-dimensional data, improved krill based on linear nearest neighbor lasso step (LNNLS-KH...

An Intrusion detection system (IDS) is extensively used to identify cyber-attacks preferably in real-time and achieve integrity, confidentiality, availability of sensitive information. In this work, we develop a novel IDS using machine learning techniques increase the performance attack process. order cope with high dimensional feature-rich traffic large networks, introduce Bat-Inspired Optimiz...

Network technology plays an increasingly important role in all aspects of social life. The Internet has brought a new round industrial revolution and upgrading. arrival the “Internet” era is accompanied by large-scale increase network applications number netizens. At same time, severity cyberattacks continue to increase. Therefore, intrusion detection systems (IDSs) have become part current sec...

The primary objective of an intrusion detection system (IDS) is to monitor the network performance and look into any indications malformation over network. While providing high-security IDS played a vital role for past couple years. will fail identify all types attacks, when it comes anomaly detection, often connected with high false alarm rate accuracy very average. Recently, utilize machine l...

Generally, the existing Intrusion Detection Systems (IDS) solutions suffer from low detection accuracy for some attack types compared with overall of attacks. The data imbalance technically affects ratio frequent attacks class (e.g. zero-day attack) to more instances. Therefore, IDS-based machine learning algorithms potentially high false-positive rates. To overcome limitation solutions, a hype...

The widespread use of the Internet has an adverse effect being vulnerable to cyber attacks. Defensive mechanisms like firewalls and IDSs have evolved with a lot research contributions happening in these areas. Machine learning techniques been successfully used defense especially IDSs. Although they are effective some extent identifying new patterns variants existing malicious patterns, many att...