Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih, Azar Abid; Abdulazeez, Adnan Mohsin

doi:10.30880/jscdm.2021.02.01.004

Cited by 45 publications

(37 citation statements)

References 65 publications

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“…In order to calculate theses performance metrics, a confusion matrix is used [24,25], which is presented Table 2. The equations below are used to evaluate the accuracy, recall, precision, and F-measure [26,27], respectively of the proposed IDS:…”

Section: Benchmark Dataset and Evaluation Metricsmentioning

confidence: 99%

Probe Attack Detection Using an Improved Intrusion Detection System

Almazyad¹,

Halman²,

Alsaeed³

2023

Computers, Materials &Amp; Continua

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The novel Software Defined Networking (SDN) architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features. Nevertheless, such benefits lead to a more adverse environment entailing network breakdown, systems paralysis, and online banking fraudulence and robbery. As one of the most common and dangerous threats in SDN, probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities, which is then manipulated to undermine the entire system. Precision, high performance, and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time, optimize prediction performance, and provide a holistic understanding of machine learning data. As the extension of astute machine learning algorithms into an Intrusion Detection System (IDS) through SDN has garnered much scholarly attention within the past decade, this study recommended an effective IDS under the Grey-wolf optimizer (GWO) and Light Gradient Boosting Machine (Light-GBM) classifier for probe attack identification. The InSDN dataset was employed to train and test the proposed IDS, which is deemed to be a novel benchmarking dataset in SDN. The proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within SDN. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 99.8% accuracy, 99.7% recall, 99.99% precision, and 99.8% F-measure.

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Section: Benchmark Dataset and Evaluation Metricsmentioning

confidence: 99%

Probe Attack Detection Using an Improved Intrusion Detection System

Almazyad¹,

Halman²,

Alsaeed³

2023

Computers, Materials &Amp; Continua

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“…This system detects the organization traffic and alerts the administrator if any suspicious activities occur. One attribute of HIDS is that file systems storing the network analytical are protected from misplacements or changes and then an alert is sent to the administrator [55]. An IoT HIDS that can identify cyberattacks was created by Wang et al [56].…”

Section: Host-based Ids For Iot Systems (Hids)mentioning

confidence: 99%

IoT Based Intrusion Detection Systems from The Perspective of Machine and Deep Learning: A Survey and Comparative Study

Ashraf¹,

Areed²,

Salem³

et al. 2022

Delta University Scientific Journal

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The term "Internet of Things" (IoT) refers to a group of gadgets that are capable of connecting to the Internet in order to gather and share data. The growth of Internet connections and the arrival of new technologies like the Internet of Things (IoT) have increased the privacy and security threats associated with the introduction of various gadgets. In order to increase the detection of cyber-attacks, industries are increasing their research spending. Institutions choose wise testing and verification techniques by comparing the highest rates of accuracy. IoT use has been accelerating recently across a variety of industries, including health care, smart homes, intelligent transportation, smart cities, and smart grids. where technology researchers and developers started to take notice of the IoT possibilities. Unfortunately, the privacy and security concerns imposed on by energy restrictions and the scalability of IoT devices present the most significant challenge to IoT. Therefore, how to address the IoT's security and privacy challenges remains an essential issue in the field of information security. With a decentralized design, edge computing plays a vital role in enabling IoT devices to compute, make decisions, take actions, and push only pertinent information to the cloud. Since sensitive data is more readily available and can be used right away, the IDS performs better when employing machine learning (ML) and deep learning (DL) algorithms to identify and prevent various threats. In terms of technical limitations, this study classifies the current, recent research in IoT intrusion detection systems employing machine learning, deep learning, and edge computing architecture.

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“…All of the network forensics approaches having been proposed have been evaluated in terms of detection rate (True Positive Rate (TPR)) and false alarms (False Positive Rate (FPR)). Since the dataset used in this paper is highly imbalanced, f-Score measure and accuracy are used for better evaluation [43][44][45]. Table 3 illustrates the confusion matrix while the next four equations present the calculation for TPR, FPR, f-Score, and accuracy, respectively, based on the values from the confusion matrix: (10) Detection Rate (TPR) =…”

Section: Data Examinationmentioning

confidence: 99%

A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer

Alazzam

AbuAlghanam

Al-zoubi

et al. 2022

Cybernetics and Information Technologies

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The Internet of Things (IoT) is widespread in our lives these days (e.g., Smart homes, smart cities, etc.). Despite its significant role in providing automatic real-time services to users, these devices are highly vulnerable due to their design simplicity and limitations regarding power, CPU, and memory. Tracing network traffic and investigating its behavior helps in building a digital forensics framework to secure IoT networks. This paper proposes a new Network Digital Forensics approach called (NDF IoT). The proposed approach uses the Owl optimizer for selecting the best subset of features that help in identifying suspicious behavior in such environments. The NDF IoT approach is evaluated using the Bot IoT UNSW dataset in terms of detection rate, false alarms, accuracy, and f-score. The approach being proposed has achieved 100% detection rate and 99.3% f-score and outperforms related works that used the same dataset while reducing the number of features to three features only.

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Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Cited by 45 publications

References 65 publications

Probe Attack Detection Using an Improved Intrusion Detection System

Probe Attack Detection Using an Improved Intrusion Detection System

IoT Based Intrusion Detection Systems from The Perspective of Machine and Deep Learning: A Survey and Comparative Study

A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer

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