Currently, anomaly based network intrusion detection (ANID) is the solution for novel and sophisticated attacks. This review focuses on the comparison of Anomaly based Network Intrusion Detection Systems (ANIDS) based on efficiency. A collection of ANIDS that were trained and tested using KDD cup 99 dataset in the period 2002 to 2012 (May) are considered for this review paper. A total of 258 pa...

Research in the field of IDS has been going on since long time; however, there exists a number ways to further improve efficiency IDS. This paper investigates performance Intrusion detection system using feature reduction and EBPA. The first step involves features, based combination information gain correlation. In next step, error back propagation algorithm (EBPA) is used train network then an...

As network-based technologies become omnipresent, intrusion detection and prevention for these systems become increasingly important. This paper proposed a modified mutual information-based feature selection algorithm (MMIFS) for intrusion detection on the KDD Cup 99 dataset. The C4.5 classification method was used with this feature selection method. In comparison with dynamic mutual informatio...

Proliferation of network systems and growing usage of Internet make network security issue to be more important. Intrusion detection is an important factor in keeping network secure. The main aim of intrusion detection is to classify behavior of a system into normal and intrusive behaviors. However, the normal and the attack behaviors in networks are hard to predict as the boundaries between th...

Most current intrusion detection systems are signature based ones or machine learning based methods. Despite the number of machine learning algorithms applied to KDD 99 cup, none of them have introduced a pre-model to reduce the huge information quantity present in the different KDD 99 datasets. Clustering is an important task in mining evolving data streams. Besides the limited memory and one-...

We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM) recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we exp...

Data mining is the modern technique for analysis of huge of data such as KDD CUP 99 data set that is applied in network intrusion detection. Large amount of data can be handled with the data mining technology. It is still in developing state, it can become more effective as it is growing rapidly. Our work in this paper survey is for the most algorithms Data Mining using KDD CUP 99 data set in t...

The growing number of security threats has prompted the use a variety techniques. most common tools for identifying and tracking intruders across diverse network domains are intrusion detection systems. Machine Learning classifiers have begun to be used in threats, thus increasing systems’ performance. In this paper, investigation model an systems based on Principal Component Analysis feature s...

With the rapid growth of Internet in recent years, network intrusion has been a difficult problem to solve. Security of computers from harmful attacks has become a crucial issue. Recognition of attacks is becoming a harder problem to crack in the field of Computer Network Security. Denial of Service (DoS) attack is an attack which affects large number of computers in the world daily. Detection ...