Genetic algorithm based feature selection approach for effective intrusion detection system

Desale, Ketan Sanjay; Ade, Roshani

doi:10.1109/iccci.2015.7218109

Cited by 42 publications

(28 citation statements)

References 7 publications

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“…We first analyze and compare the training time of classifiers using different feature fusion techniques based on different datasets. For the KDD dataset series (DARPA99, KDD99, and KDD99 10%), we can find that the training time of network intrusion classifier using the following feature fusion techniques is shorter than others, such as GFR, FRM-SFM [18], and CART [23]; CFS-GA [25] is very efficient for the NSL-KDD dataset; based on the Kyoto 2006+ dataset, PLS [12] helps to reduce time consumption of classifier training. In summary, these mentioned fusion techniques are outstandingly efficient in the training time of network behavior classifier.…”

Section: Comparison Of Feature Fusion Techniquesmentioning

confidence: 97%

Data Fusion for Network Intrusion Detection: A Review

Yan

et al. 2018

Security and Communication Networks

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Rapid progress of networking technologies leads to an exponential growth in the number of unauthorized or malicious network actions. As a component of defense-in-depth, Network Intrusion Detection System (NIDS) has been expected to detect malicious behaviors. Currently, NIDSs are implemented by various classification techniques, but these techniques are not advanced enough to accurately detect complex or synthetic attacks, especially in the situation of facing massive high-dimensional data. Besides, the inherent defects of NIDSs, namely, high false alarm rate and low detection rate, have not been effectively solved. In order to solve these problems, data fusion (DF) has been applied into network intrusion detection and has achieved good results. However, the literature still lacks thorough analysis and evaluation on data fusion techniques in the field of intrusion detection. Therefore, it is necessary to conduct a comprehensive review on them. In this article, we focus on DF techniques for network intrusion detection and propose a specific definition to describe it. We review the recent advances of DF techniques and propose a series of criteria to compare their performance. Finally, based on the results of the literature review, a number of open issues and future research directions are proposed at the end of this work.

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Section: Comparison Of Feature Fusion Techniquesmentioning

confidence: 97%

Data Fusion for Network Intrusion Detection: A Review

Yan

et al. 2018

Security and Communication Networks

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“…In the other hand, the work by K. S. Desale et al [12] shows the use of GA, which is chosen as a standout amongst the most ground-breaking instruments to look in a huge space with the possibility to locate the best arrangement in the pursuit space. Notwithstanding, in the later advancement of the populace, a bigger hybrid and change likelihood may result in the loss of good qualities and postponed intermingling of the calculation.…”

Section: Outcome Of the Parallel Research Workmentioning

confidence: 99%

“…Lemma -2: The inferable rulesets from any rule engine is more effective to detect more number of attacks from any dataset. Where, A is the set of identified and known attacks can be defined as Hence, the rulesets defining the characteristics can also possibly be inferred as, 12 FF  (Eq. 14) As, the Eq.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Secondly, the many of the algorithms shares similar accuracy but one of those algorithms outperforms the other algorithms due to lesser time complexity. Henceforth, firstly the accuracy of some of the popular feature selection methods are compared here [Table - 12]. The proposed method for feature selection and few of the most popular feature reduction and selection algorithms are applied on the KDD dataset and the accuracy of detecting intrusions are tested.…”

Section: Comparative Analysismentioning

confidence: 99%

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Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

Sudhakar*,

Narsimha,

Narsimaha

2019

IJEAT

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With the rapid growth in the data processing and data sharing, the application owners and the consumers of the applications are more influenced to use the remote storage on cloud-based data centre and the application generated data is also growing ups and bounds. Nevertheless, the adaptation of the data sharing, and data processing applications were not easy for the consumers. The application owners and the service providers have struggled with the sensitive data of the consumers and the consumers were also faced trust issues with the complete framework. The standard legacy applications were designed for the traditional centralized scenarios, where the intrusion detection can be performed only using the network status analysis and the application characteristics analysis. Moreover, most of the parallel calculations initially enhance the hybrid likelihood and change likelihood of GA as indicated by the populace advancement variable-based math and wellness esteem. Nevertheless, the population of data and the attacks on the data is high and the correct population size is highly difficult to determine. Regardless to mention, that the use of fitness functions will restrict the attack detection to certain types and these algorithms are bound to fail in case of a newer attack. However, with the migration of application to the data processing framework, the consumers have started demanding more security against the intrusions. A good number of research attempts were made to map the traditional security algorithms into the data processing space, nonetheless, the attempts were highly criticized due to the lack of proper analysis of security attacks on data processing applications. Hence, this work proposes a novel framework to detect the intrusions on data processing framework with justifying attack characteristics. This work proposes a novel algorithm to reduce the features of attack characteristics to justify the gaps on data processing frameworks with significant reduction in time for processing and further, proposes an algorithm to derive a strong rule engine to analyse the attack characteristics for detecting newer attacks. The complete proposed framework demonstrates nearly 93% and higher accuracy, which is much higher than the existing parallel research outcomes with least time complexity.

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“…Desale and Ade [28] propose a Genetic Algorithm based Feature Selection Approach for Effective Intrusion Detection System. The genetic algorithm is used to search method when selecting features from the full NSL-KDD dataset.…”

Section: Tesfahun and Bhaskarimentioning

confidence: 99%

Data Mining and Intrusion Detection Systems

Dewa¹,

Μαγλαράς²

2016

ijacsa

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Abstract-The rapid evolution of technology and the increased connectivity among its components, imposes new cyber-security challenges. To tackle this growing trend in computer attacks and respond threats, industry professionals and academics are joining forces in order to build Intrusion Detection Systems (IDS) that combine high accuracy with low complexity and time efficiency. The present article gives an overview of existing Intrusion Detection Systems (IDS) along with their main principles. Also this article argues whether data mining and its core feature which is knowledge discovery can help in creating Data mining based IDSs that can achieve higher accuracy to novel types of intrusion and demonstrate more robust behaviour compared to traditional IDSs.

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Genetic algorithm based feature selection approach for effective intrusion detection system

Cited by 42 publications

References 7 publications

Data Fusion for Network Intrusion Detection: A Review

Data Fusion for Network Intrusion Detection: A Review

Detection of Intrusion using Hybrid Feature Selection and Flexible Rule Based Machine Learning

Data Mining and Intrusion Detection Systems

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