Intrusion Detection System using Genetic Algorithm and K-NN Algorithm on Dos Attack

Fauzi, Muhammad Akmal; Hanuranto, Ahmad Tri; Setianingsih, Casi

doi:10.1109/icoris50180.2020.9320822

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…In classification and regression the K-NN algorithm is used. It is a supervised learning technique that classifies an unknown instance based on the distance between the instance and k selected neighbors, with the class determined by the majority of neighbors voting [46]. The K-NN algorithm is frequently used in classification, with the goal of classifying new objects based on attributes and training examples.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques

Gaber

Awotunde

Folorunso

et al. 2023

Wireless Communications and Mobile Computing

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The emergence of the Internet of Things (IoT) has witnessed immense growth globally with the use of various devices found in home, transportation, healthcare, and industry. The deployment and implementation of the IoT paradigm in industrial settings lead to the architectural changes of Industrial Automation and Control Systems (IACS) plus the countless connectivity of industrial systems. This resulted in what is referred to as the Industrial Internet of Things (IIoT), which removes the barrier of connecting IACS to isolated conventional ICT platforms. In recent times, the IoT has started hacking our personal lives and not only our world, thus creating a platform for impending IoT cyberattacks. The widespread use of the IoT has created a rich platform for possible IoT cyberattacks. Machine learning (ML) algorithms have been driven solutions to secure wireless communication in IIoT-based systems, and their use in solving various cybersecurity challenges. Therefore, this paper proposes a novel intrusion detection model based on the Particle Swarm Optimization (PSO) and Bat algorithm (BA) for feature selection, and the Random Forest (RF) classifier for the classification of malicious behaviors in IIoT-based network traffic. An IIoT-based cybersecurity dataset, WUSTL-IIOT-2021 Dataset, was used to evaluate the performance of the proposed model using accuracy, recall, precision, and F1-score. The results of the two feature selection were compared to identify the most promising one. The results were compared with other recent state-of-the-art ML and multiobjective algorithms, and the results showed better performance. The RF along with BA classifier had proved to be the best classifier.

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Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques

Gaber

Awotunde

Folorunso

et al. 2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…The crossover process is repeated until the generated random value is smaller than the crossover probability [29]. The crossover probability used in this study is 0.4.…”

Section: Fig 9 -Crossover Illustrationmentioning

confidence: 99%

Intelligent Electrical Device Load Scheduling for Building Energy Management

Setianingsih¹,

Murti²,

Saputra³

et al. 2022

IJIE

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Currently, electrical energy is one of the primary needs in everyday life. Almost every device used for daily life uses electric power. However, the lack of user awareness in electronic devices can cause monthly electricity bills to be out of control, both in-office and residential areas. To improve the efficiency of electrical energy use in offices and housing, we designed a management system and control of daily electricity consumption so that it does not exceed the target of the monthly electricity bill. In this system, we integrate the priority of electronic devices into the optimization algorithm. This system uses a priority queue algorithm and genetic algorithm in scheduling electronic devices optimally. The system was created in the form of a website to limit the use of electrical energy by optimizing device scheduling. The method used is the Genetic Algorithm to calculate the duration of use of each electrical device so as not to exceed the predetermined monthly budget limit. This method will provide a recommended number of operating hours per day for 30 days for each electrical device that has been registered in the system. At the same time, the priority queue algorithm will adjust the duration of operation of each device based on the priority order of the device. Based on the test, the optimal fitness value is in the 60th generation, with an average execution time of 0.18 seconds. In testing the rules for both methods, this system has an accuracy rate of 100%. The system has been running according to the designed regulations.

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“…Fauzi. In [33], the authors have used the IDS process to select the best features from 41 to 18 using Genetic Algorithms (GA) and KNN with the KDD99 dataset. The results of the K-NN algorithm for training data, which has an accuracy of 99.98%, and for testing data accuracy of 97.52%…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

Jahwar

Ameen

2021

JSCDM

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Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the main principle of information security, and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed, including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms.

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Intrusion Detection System using Genetic Algorithm and K-NN Algorithm on Dos Attack

Cited by 10 publications

References 12 publications

Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques

Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques

Intelligent Electrical Device Load Scheduling for Building Energy Management

A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

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