Decision Tree and Genetic Algorithm Based Intrusion Detection System

Azad, Chandrashekhar; Jha, Vijay Kumar

doi:10.1007/978-981-10-8234-4_13

Cited by 14 publications

(8 citation statements)

References 35 publications

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“…Azad and Jha [14], proposed an Intrusion Detection System that is based on a decision tree and genetic algorithm. The crossover operation was used to generate the new individual from the parent.…”

Section: Discussing the Use Of Conditionally Classified "Distributed" Ai Methods In Cybersecuritymentioning

confidence: 99%

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Naik

Mehta

Yagnik³

et al. 2021

Complex Intell. Syst.

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Given the prevailing state of cybersecurity, it is reasonable to understand why cybersecurity experts are seriously considering artificial intelligence as a potential field that can aid improvements in conventional cybersecurity techniques. Various progressions in the field of technology have helped to mitigate some of the issues relating to cybersecurity. These advancements can be manifested by Big Data, Blockchain technology, Behavioral Analytics, to name but a few. The paper overviews the effects of applications of these technologies in cybersecurity. The central purpose of the paper is to review the application of AI techniques in analyzing, detecting, and fighting various cyberattacks. The effects of the implementation of conditionally classified “distributed” AI methods and conveniently classified “compact” AI methods on different cyber threats have been reviewed. Furthermore, the future scope and challenges of using such techniques in cybersecurity, are discussed. Finally, conclusions have been drawn in terms of evaluating the employment of different AI advancements in improving cybersecurity.

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Section: Discussing the Use Of Conditionally Classified "Distributed" Ai Methods In Cybersecuritymentioning

confidence: 99%

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Naik

Mehta

Yagnik³

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…The researcher also attempted to decrease misclassification by utilising the benefits of GA in the development of a new model IDS. A research based on the DT and GA was presented to generate the C4.5, Decision Tree (DT) and GA were used to solve the problems of minor disjunction in the C4.5 on the kddcup99 dataset [12].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Diverse Analysis of Data Mining and Machine Learning Algorithms to Secure Computer Network

Kumar

2021

Wireless Pers Commun

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Network attacks are becoming more complex, making it more difficult to detect intrusions. Various research have been done over the years, employing different categorization techniques of Data Mining (DM) and Machine Learning (ML) inspired hybrid approaches to develop robust IDS. Almost all researchers suggested to improve accuracy in intrusion detection with low computational cost. Authors observed that dissimilar sets of features were picked for different classifiers to get the highest accuracy. This paper is dedicated to a review of relevant research, where an in-depth investigation was carried out with two emphasis points of IDS, which contain distinct pre-processing techniques in the form of feature selection and a diversity of classification algorithms. In addition, this paper presents a comparative algorithmic assessment of the DM and ML techniques applied to create an intelligent IDS. A novel feature selection method based on the CART algorithm has also introduced which provides optimal feature subset of the dataset so perfectly that it has made various existing DM and ML classifying algorithms more performant than earlier, this makes classifiers independent of feature selection. To validate the performance of proposed work, experiments have performed using the 'Python' programming language and 'corrected' & '10_percent' of 'Kddcup99' datasets used as benchmark. As an outcome proposed work, we found that feature reduction and selecting a classifier had a significant influence on the rate of intrusion detection accuracy. The results of simulation and comparison analysis of proposed work with existing DM and ML based classification approaches show that the suggested work is more competent in true prediction and attaining maximum intrusion detection accuracy with minimal computing cost of prediction.

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“…The researcher also made an effort to reduce misclassification by using the benefits of GA for developing a new model IDS. A paper based on the DT and GA was proposed to generate decision tree C4.5 and GA was used to overcome the problem of small disjunction in the C4.5 on kddcup99 dataset [13].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Diverse Analysis of Data Mining and Machine Learning Algorithms to Secure Computer Network

Kumar

2021

Preprint

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Information and Communication Technologies, to a long extent, have a major influence on our social life, economy as well as on worldwide security. Holistically, computer networks embrace the Information Technology. Although the world is never free from people having malicious intents i.e. cyber criminals, network intruders etc. To counter this, Intrusion Detection System (IDS) plays a very significant role in identifying the network intrusions by performing various data analysis tasks. In order to develop robust IDS with accuracy in intrusion detection, various papers have been published over the years using different classification techniques of Data Mining (DM) and Machine Learning (ML) based hybrid approach. The present paper is an in-depth analysis of two focal aspects of Network Intrusion Detection System that includes various pre-processing methods in the form of dimensionality reduction and an assortment of classification techniques. This paper also includes comparative algorithmic analysis of DM and ML techniques, which applied to design an intelligent IDS. An experiment al comparative analysis has been carried out in support the verdicts of this work using ‘Python’ language on ‘kddcup99’ dataset as benchmark . Experimental analysis had been done in which we had found more impact on dimensionality reduction and MLP performed well in the true classification to establish secure network. The motive behind this effort is to detect different kinds of malware as early as possible with accuracy, to provide enhanced observant among various existing techniques that may help the fascinated researchers for future potential works.

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Decision Tree and Genetic Algorithm Based Intrusion Detection System

Cited by 14 publications

References 35 publications

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review

Diverse Analysis of Data Mining and Machine Learning Algorithms to Secure Computer Network

Diverse Analysis of Data Mining and Machine Learning Algorithms to Secure Computer Network

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