The increasing scale and sophistication of network attacks have become a major concern for organizations around the world. As result, there is an demand effective accurate classification to enhance cyber security measures. Most existing schemes assume that available training data labeled; is, based on supervised learning. However, this not always case since real expected be unlabeled. In paper,...

Intrusion detection is one of core technologies of computer security. It is required to protect the security of computer network systems. Most of existing IDs use all features in the network packet to look for known intrusive patterns. Some of these features are irrelevant or redundant. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Th...

The present era is being dominated by cloud computing technology which provides services to the users as per demand over internet. Satisfying needs of huge people makes prone activities come up a threat. Intrusion detection system (IDS) an effective method providing data security information stored in works analyzing network traffic and informs case any malicious activities. In order control hi...

Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) vital detecting intruder attacks, it also used monitor the traffic network. The paper aimed control attacks using machine learning (ML) technique integrated with artificial bee colony (ABC) named Group-ABC (G-ABC). IDS d...

Novelty Detection is a task of recognition abnormal data points within given system. Recently, this has been performed using Deep Learning Autoencoders, but they face several drawbacks which include the problem identity mapping, adversarial perturbations and optimization algorithms. In paper, we have proposed novel approach LPRNet, Denoising Autoencoder uses algorithms such as Least Trimmed Squ...

The intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all administrators. Several existing methods have proposed for early systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced l...

Abstract Researchers publish various studies to improve the performance of network intrusion detection systems. However, there is still a high false alarm rate and missing intrusions due class imbalance in multi-class dataset. This imbalanced distribution classes results low accuracy for minority classes. paper proposes Synthetic Multi-minority Oversampling (SMMO) framework by integrating with ...

COVID-19 has altered the way businesses throughout world perceive cyber security. It resulted in a series of unique cyber-crime-related conditions that impacted society and business. Distributed Denial Service (DDoS) dramatically increased recent year. Automated detection this type attack is essential to protect business assets. In research, we demonstrate use different deep learning algorithms...

In this paper a multi-layer Bayesian based intrusion detection system is developed. The system is trained a priori using a subset of the KDD dataset. The trained classifier is then tested using a larger subset of KDD dataset. The Bayesian classifier was able to detect intrusion with detection rate that is superior to most published results. Index Terms Intrusion Detection – Bayesian Filter – KD...

The world’s reliance the use of internet is growing constantly, and data are considered most precious parameter nowadays. It critical to keep information secure from unauthorized people organizations. When a network compromised, taken. An intrusion detection system detects both known unexpected assaults that allow be breached. In this research, we model an trained identify such attacks in LANs,...