Cyber intrusion detection by combined feature selection algorithm

Mohammadi, Sara; Mirvaziri, Hamid; Ghazizadeh-Ahsaee, Mostafa; Karimipour, Hadis

doi:10.1016/j.jisa.2018.11.007

Cited by 177 publications

(83 citation statements)

References 28 publications

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“…In [8], a combination of the filter and wrapper approaches have been employed to select the ideal feature subsets from the main dataset. The authors have used the feature grouping concept, which leads to lower variance and greater feature-selection stability.…”

Section: Related Workmentioning

confidence: 99%

“…These systems define malicious behavior as an activity which demonstrates a deviation from the regular operation. The most significant benefit that these systems can provide for the users is the influential detection rate of both known and unknown attacks [8]. A proper tuned IDS guarantees deeper insight into networks by providing visibility and control measures to minimize the threats and attacks.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

Golrang

Yayilgan

et al. 2020

Electronics

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Machine-learning techniques have received popularity in the intrusion-detection systems in recent years. Moreover, the quality of datasets plays a crucial role in the development of a proper machine-learning approach. Therefore, an appropriate feature-selection method could be considered to be an influential factor in improving the quality of datasets, which leads to high-performance intrusion-detection systems. In this paper, a hybrid multi-objective approach is proposed to detect attacks in a network efficiently. Initially, a multi-objective genetic method (NSGAII), as well as an artificial neural network (ANN), are run simultaneously to extract feature subsets. We modified the NSGAII approach maintaining the diversity control in this evolutionary algorithm. Next, a Random Forest approach, as an ensemble method, is used to evaluate the efficiency of the feature subsets. Results of the experiments show that using the proposed framework leads to better outcomes, which could be considered to be promising results compared to the solutions found in the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

Golrang

Yayilgan

et al. 2020

Electronics

View full text Add to dashboard Cite

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“…Thus, to accommodate these decentralization and digitalization trends, local distributed control and management techniques are in need [20]. The importance of this issue has led many researchers to seek new methodologies and concepts to improve the performance and security of the power systems [8], [13], [21], [22]. Application of blockchain is one of the newest ones.…”

Section: Fig 1 Power System Changes and Challengesmentioning

confidence: 99%

Blockchain Applications in Power Systems: A Bibliometric Analysis

Rouzbahani

Karimipour

Dehghantanha

et al. 2020

Advances in Information Security

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Power systems are growing rapidly, due to ever-increasing demand for electrical power. These systems require novel methodologies and modern tools and technologies, to better perform, particularly for communication among different parts. Therefore, power systems are facing new challenges such as energy trading and marketing and cyber threats. Using blockchain in power systems, as a solution, is one of the newest methods. Most studies aim to investigate innovative approaches of blockchain application in power systems. Even though, many articles published to support the research activities, there has not been any bibliometric analysis which specifies the research trends. This paper aims to present a bibliographic analysis of the blockchain application in power systems related literature, in the Web of Science (WoS) database between January 2009 and July 2019 . This paper discusses the research activities and performed a detailed analysis by looking at the number of articles published, citations, institutions, research area, and authors. From the analysis, it was concluded that there are several significant impacts of research activities in China and USA, in comparison to other countries.

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“…This model decreases the training and testing time in intrusion detection system. Saraet al [11] developed an IDS based feature selection model which integrates both filter and wrapper method. This work used linear correlation coefficient for feature grouping and cuttlefish algorithm correspondingly.…”

Section: Related Workmentioning

confidence: 99%

Knowledgeable Handling of Impreciseness in Feature Subset Selection using Intuitionistic Fuzzy Mutual Information of Intrusion Detection System

Sudha*¹,

Gunavathi²

2019

IJITEE

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One of the most promising areas of domain in research field is security because of its exponential usage in everyday commercial activities. Due to prevalence diffusion of network connectivity, there is a high demand for protection against cyber-attack which necessitates the importance of intrusion detection system as a significant tool for network security. There are many intrusion detection models available to classify the network traffic s either normal or attack type. Because of huge volume of network traffic data, these classifier techniques fail to attain high detection rate with less false alarms. To overcome the above problem, this paper introduces the potential feature subset selection model using Intuitionistic Fuzzy Mutual Information (IFMI). This model efficiently selects the optimal set of attributes without loss of information even in presence of impreciseness among attributes. This is achieved by representing each attribute in the dataset in terms of degree of membership, non-membership and hesitation. To validate the performance of the IFMI its reduced feature subset is used for classification using random forest classifier. After analyzing the feature subset, the simulation results proved that the proposed model has improved the performance of classifier for predicting the network intrusion attempts. It also helps the classification model to achieve high classification rate and reduced false alarm rate in an optimized way.

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Cyber intrusion detection by combined feature selection algorithm

Cited by 177 publications

References 28 publications

A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

Blockchain Applications in Power Systems: A Bibliometric Analysis

Knowledgeable Handling of Impreciseness in Feature Subset Selection using Intuitionistic Fuzzy Mutual Information of Intrusion Detection System

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