KDD 99 intrusion detection datasets, which are based on DARPA 98 dataset, provides labeled data for researchers working in the field of intrusion detection and is the only labeled dataset publicly available. Numerous researchers employed the datasets in KDD 99 intrusion detection competition to study the utilization of machine learning for intrusion detection and reported detection rates up to ...

The potential of machine learning mechanisms played a key role in improving the intrusion detection task. However, other factors such as quality data, overfitting, imbalanced problems, etc. may greatly affect performance an intelligent system (IDS). To tackle these issues, this paper proposes novel learning-based IDS called i-2NIDS. novelty approach lies application nested cross-validation meth...

Abstract Fog computing, as a new distributed computing framework, extends the tasks originally done in cloud data center to edge of network and brings more serious security challenges while providing convenience. Abnormal traffic detection is an effective means defense malicious behavior, can detect variety known attacks. Although application deep learning method field abnormal easier than trad...

With the growing rates of cyber-attacks and cyber espionage, need for better more powerful intrusion detection systems (IDS) is even warranted nowadays. The basic task an IDS to act as first line defense, in detecting attacks on internet. As tactics from intruders become sophisticated difficult detect, researchers have started apply novel Machine Learning (ML) techniques effectively detect henc...

With the growing need of internet in daily life and the dependence on the world wide system of computer networks, the network security is becoming a necessary requirement of our world to secure the confidential information available on the networks. Efficient intrusion detection is needed as a defence of the network system to detect the attacks over the network. Using feature selection, we redu...

Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed study (CCAID: computing, attack, intrusion detection) aims to improve performance detection systems (IDS) operating CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. includes FS algo...

Deep learning based intrusion detection system has acquired prominence in digital protection frameworks. The fundamental component of such a framework is to give an assurance the ICT foundation interruption recognition (IDS). Wise arrangements are exceptionally essential and expected control intricacy identification new assault types. smart frameworks, for example, Machine have broadly been acq...

Recently, cyberattacks have been more complex than in the past, as a new cyber-attack is initiated almost every day. Therefore, researchers should develop efficient intrusion detection systems (IDS) to detect cyber-attacks. In order improve and prevention of aforementioned cyber-attacks, several articles developed IDSs exploiting machine learning deep learning. this paper, way find network intr...

The KDD Cup 99 dataset has been the point of attraction for many researchers in the field of intrusion detection from the last decade. Many researchers have contributed their efforts to analyze the dataset by different techniques. Analysis can be used in any type of industry that produces and consumes data, of course that includes security. This paper is an analysis of 10% of KDD cup’99 trainin...