A novel intrusion detection system based on feature generation with visualization strategy

Luo, Bin; Xia, Jingbo

doi:10.1016/j.eswa.2013.12.048

Cited by 64 publications

(34 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of above discussed mechanisms enhance IDS based on feature selection. Bin & Jingbo proposed novel IDS whose approach is to identify & select features based on visualization strategy [32]. Tactic in this work is to lower the intrusion guess time, deal with data for its irrelevantness and redundancy.…”

Section: A Anomaly Based Idsmentioning

confidence: 99%

A Review on Intrusion Detection Techniques and Intrusion Detection Systems in MANETs

Chakravarthi¹,

Veluru

2014

2014 International Conference on Computational Intelligence and Communication Networks

View full text Add to dashboard Cite

Over past few years, among wireless technologies, MANETs has become one of the most important and effective area in extending research capabilities. A great array of scope is lying as platform for research scholars as MANETs are vulnerable to attacks because of their high volatility in location and motion. Protocols assume that no malicious intruder node is present, which impacts all the layers by some means; chiefly, the network layer. This paper is a brief review on such attacks whose focus is on degrading MANETs efficiency. It extends in apprising well identified intrusion detection and detection techniques for wide range of attacks. Also, a comprehensive study is carried on the said area to forecast and explore on futuristic research areas. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the approaches to intrusion detection techniques and systems.

show abstract

Section: A Anomaly Based Idsmentioning

confidence: 99%

A Review on Intrusion Detection Techniques and Intrusion Detection Systems in MANETs

Chakravarthi¹,

Veluru

2014

2014 International Conference on Computational Intelligence and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Currently, there is much focus on the intrusion detection system (IDS), which has close links for the safety of network service applications [1][2][3][4][5]. It should be pointed out that it is difficult to differentiate between an attack and the typical network visit.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method consisting of GA and SVM for intrusion detection system

Aslahi-Shahri

Rahmani

Chizari

et al. 2015

Neural Comput & Applic

185

View full text Add to dashboard Cite

In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is proposed and its implementation in intrusion detection problem is explained. The proposed hybrid algorithm is employed in reducing the number of features from 45 to 10. The features are categorized into three priorities using GA algorithm as the highest important is the first priority and the lowest important is placed in the third priority. The feature distribution is done in a way that 4 features are placed in the first priority, 4 features in the second, and 2 features in the third priority. The results reveal that the proposed hybrid algorithm is capable of achieving a truepositive value of 0.973, while the false-positive value is 0.017.

show abstract

“…A new feature generation approach called four-angle-star is proposed by Luo et al [5]. Their aim was to evaluate the distance between samples in that star approach where 5-classes were available.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

Haque¹,

Al-Kharobi²

2015

IJMLC

View full text Add to dashboard Cite

Abstract-In this paper, we propose a novel method using ensemble learning scheme for classifying network intrusion detection from the most renowned KDD cup dataset. We have shown that reducing the dimensionality of the large dataset provides most accurate detection. Additionally, several machine learning algorithms are used to generate the accuracy metrics and analyzed further for proper comparison. Our approach found out that this algorithm outperforms all other learning techniques. Our goal is to analyze the network intrusion data and find out the best components and use them for the attack analysis. This scheme can be used in parallel with the intrusion detection system to augment its prediction performance for the future data packets. Empirical results show that the input dimensionality reduction can provide lightweight intrusion detection system that can be embedded with the vulnerable system for generating correct classification with significance improvement in execution time.

show abstract

A novel intrusion detection system based on feature generation with visualization strategy

Cited by 64 publications

References 25 publications

A Review on Intrusion Detection Techniques and Intrusion Detection Systems in MANETs

A Review on Intrusion Detection Techniques and Intrusion Detection Systems in MANETs

A hybrid method consisting of GA and SVM for intrusion detection system

Adaptive Hybrid Model for Network Intrusion Detection and Comparison among Machine Learning Algorithms

Contact Info

Product

Resources

About