A framework for designing resilient distributed intrusion detection systems for critical infrastructures

Genge, Béla; Haller, Piroska; Kiss, István Z.

doi:10.1016/j.ijcip.2016.06.003

Cited by 21 publications

(4 citation statements)

References 17 publications

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“…It can be assumed that without detailed specifications, attackers will not be able to manipulate the equipment (and will not even try to do it). This approach to information hiding will block all opportunities for cyberattacks on individual devices or networks [9].…”

Section: Methods For Preventing Cyber Attacksmentioning

confidence: 99%

Information Security of Power Plants

Рустем¹

2020

E3S Web of Conf.

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Section: Methods For Preventing Cyber Attacksmentioning

confidence: 99%

Information Security of Power Plants

Рустем¹

2020

E3S Web of Conf.

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“…Genge et al [32]suggested an innovative approach for resilient distributed intrusion detection systems. The framework controls the outcomes of the risk assessment method to recognizeand rank serious communications flows.…”

Section: Iirelated Workmentioning

confidence: 99%

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

et al. 2022

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Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples.Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. Theresults show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes. INDEX TERMSNetwork IntrusionDetection System (NIDS), Density-based spatial clustering of applications with noise (DBSCAN), Anticipated Distance-based Clustering (ADC), Data Preprocessing, Likelihood Naïve Bayes (LNB), and IDS Datasets.

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“…The Cyber Security Framework (CSF) is a set of rules that private sector firms can use to detect, identify, and respond to cyber threats. Cyber security frameworks have the potential to become instruments for enforcing government security legislation [118]. The framework also contains guidance to assist businesses in preventing and recovering from cyber-attacks.…”

Section: Cyber Security Frameworkmentioning

confidence: 99%

A Systematic Literature Review on the Cyber Security

Perwej

Abbas

Dixit³

et al. 2021

int.jour.sci.res.mana.

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In recent years, the Internet has become an integral element of people's everyday lifestyles all across the world. Online criminality, on the other hand, has risen in tandem with the growth of Internet activity. Cyber security has advanced greatly in recent years in order to keep up with the rapid changes that occur in cyberspace. Cyber security refers to the methods that a country or organization can use to safeguard its products and information in cyberspace. Two decades ago, the term "cyber security" was barely recognized by the general public. Cyber security isn't just a problem that affects individuals but it also applies to an organization or a government. Everything has recently been digitized, with cybernetics employing a variety of technologies such as cloud computing, smart phones, and Internet of Things techniques, among others. Cyber-attacks are raising concerns about privacy, security, and financial compensation. Cyber security is a set of technologies, processes, and practices aimed at preventing attacks, damage, and illegal access to networks, computers, programmes, and data. The primary goal of this article is to conduct a thorough examination of cyber security kinds, why cyber security is important, cyber security framework, cyber security tools, and cyber security difficulties. Cyber security safeguards the data and integrity of computing assets that are part of or connected to an organization's network, with the goal of defending such assets from all threat actors throughout the life cycle of a cyber-attack.

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A framework for designing resilient distributed intrusion detection systems for critical infrastructures

Cited by 21 publications

References 17 publications

Information Security of Power Plants

Information Security of Power Plants

An Innovative Perceptual Pigeon Galvanized Optimization (PPGO) Based Likelihood Naïve Bayes (LNB) Classification Approach for Network Intrusion Detection System

A Systematic Literature Review on the Cyber Security

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