Network intrusion detection system using immune-genetic algorithm (IGA)

Suhaimi, Hamizan; Suliman, Saiful Izwan; Musirin, Ismail; Harun, Afdallyna Fathiyah; Mohamad, Rosfarizan; Kassim, Murizah; Shahbudin, Shahrani

doi:10.11591/ijeecs.v17.i2.pp1059-1065

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…After obtaining the output data of NSE, the GA_BP algorithm is utilized to construct the evaluation model. In the process of constructing the model, the GA will correspond to the connection weights and the network structure in the neural network, so that it can overcome the problems in the BP algorithm and significantly improve the data generalization ability [19][20]. The study takes the data vulnerabilities and risks in network security as inputs to the model, and utilizes the global search capability of the GA to search for the data vulnerabilities that exist.…”

Section: B Network Security Evaluation Model Construction Based On Ga_bpmentioning

confidence: 99%

Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Pang,

Liu

2024

ijacsa

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As the speed advancement of network technology and the popularization of applications, network security problems are becoming more and more prominent, all kinds of network attacks and security threats are increasing, and the demand for network security evaluation is becoming more and more urgent. To address the issues of long time-consuming and low accuracy in the traditional network security evaluation model, the study proposes a network security evaluation model based on improved genetic algorithm and weighted error BP algorithm. The study first combines the weighted error BP algorithm with the improved genetic algorithm for data analysis and research, and then integrates the two to construct a network security evaluation model. The results show that in the detection of network security vulnerabilities, the evaluation model of the data processing vulnerability detection accuracy, risk detection rate of 93.28%, 91.88%, respectively. The function training error of the model is 8.93% respectively, while the decoding accuracy and stability are 90.43% and 92.07% respectively, which are better than the comparison method. This indicates that the method has high accuracy and robustness in network security evaluation, and can provide network administrators and users with a more scientific and reliable basis for decision-making.

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Section: B Network Security Evaluation Model Construction Based On Ga_bpmentioning

confidence: 99%

Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Pang,

Liu

2024

ijacsa

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“…The CIC-IDS 2017 and KDD Cup 99 data sets were used to test their proposed method. Novel utilization of Genetic Algorithm (GA) alongside an immune algorithm was proposed in [47] to improve a computer's ability to detect intrusion. They carried out several simulations to verify the performance of their proposed method.…”

Section: Related Workmentioning

confidence: 99%

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

et al. 2021

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The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection.

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“…In literature, we understood that using different machine learning techniques, a number of intrusion detection systems are developed. For instance, some research studies apply single learning techniques, such as self-organizing map [22], neural networks [23], genetic algorithms [24], decision tree [25,26], and pattern matching algorithms [27] to develop intrusion detection model. On the other hand, some intrusion detection systems such as hybrid approach or ensemble techniques are [28] developed by combining different machine learning techniques and ensemble classifiers by combining multiple weak learners [29].…”

Section: Related Workmentioning

confidence: 99%

Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection

Chiche

Meshesha

2021

Journal of Computer Networks and Communications

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This paper introduces a new integrated learning approach towards developing a new network intrusion detection model that is scalable and adaptive nature of learning. The approach can improve the existing trends and difficulties in intrusion detection. An integrated approach of machine learning with knowledge-based system is proposed for intrusion detection. While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. It is empirically tested with NSL-KDD dataset of 40,558 total instances, by using ten-fold cross validation. Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. Therefore, security experts are recommended to integrate intrusion detection in their network and computer systems, not only for well-being of their computer systems but also for the sake of improving their working process.

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Network intrusion detection system using immune-genetic algorithm (IGA)

Cited by 6 publications

References 11 publications

Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Network Security Evaluation Based on Improved Genetic Algorithm and Weighted Error Backpropagation Algorithm

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection

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