Cyber-Attack Detection and Mitigation Using SVM for 5G Network

Alshunaifi, Sulaiman Yousef; Mishra, Shailendra; Alshehri, Mohammed

doi:10.32604/iasc.2022.019121

Cited by 15 publications

(10 citation statements)

References 30 publications

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“…The calculated throughput and intrusion detection rate are higher, while the latency, energy consumption, and packet loss rate are lower, indicating that the proposed approach has achieved better QoS. Alshunaifi et al [11] used MLP and LSTM networks for SQL injection detection. The study concluded that LSTM has excellent potential in threat intelligence detection.…”

Section: Related Workmentioning

confidence: 99%

“…In the studies, the authors suggest that further research using other machine learning methods with multi-class data is required because previously employed methods used binary data in their studies. Another study has discussed SQLi detection using Multi-Layered Perceptron (MLP) and long short-term memory (LSTM) networks, both of which achieved 97% accuracy, with MLP having a bit higher accuracy compared to the LSTM [11]. The keyword recognition capabilities and special character detection using LSTM need improvement.…”

Section: Introductionmentioning

confidence: 99%

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An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

Stiawan¹,

Bardadi²,

Afifah³

et al. 2023

Computer Systems Science and Engineering

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The Repository Mahasiswa (RAMA) is a national repository of research reports in the form of final assignments, student projects, theses, dissertations, and research reports of lecturers or researchers that have not yet been published in journals, conferences, or integrated books from the scientific repository of universities and research institutes in Indonesia. The increasing popularity of the RAMA Repository leads to security issues, including the two most widespread, vulnerable attacks i.e., Structured Query Language (SQL) injection and cross-site scripting (XSS) attacks. An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous. This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks. The proposed system combines a Long Short-Term Memory and Principal Component Analysis (LSTM-PCA) model as a classifier. This model can effectively solve the vanishing gradient problem caused by excessive positive samples. The experiment results show that the proposed system achieves an accuracy of 96.85% using an 80%:20% ratio of training data and testing data. The rationale for this best achievement is that the LSTM's Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks' patterns. The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded. In addition, the LSTM's Input Gate assists in finding out crucial information and stores specific relevant data in the memory.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

Stiawan¹,

Bardadi²,

Afifah³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…In the formula, λ � 0.5. X (0) is the original multiscale network trafc, X (1) is the cumulative sequence of X (0) and Z (1) is the adjacent mean sequence of X (1) , so the grey Verhulst prediction model can be constructed.…”

Section: Mathematical Modeling Of Multiscale Network Trafcmentioning

confidence: 99%

“…Instant messaging software, high-defnition video transmission, and other applications relying on high-speed networks are increasingly popular, which puts forward higher requirements for the Internet transmission capacity and information carrying capacity. At this stage, the network has become an inseparable part of people's lives, and the network is an important foundation of the information society, constantly promoting economic development and social progress [1,2]. As a special time series, network trafc has the characteristics of being multiscale, nonlinear, and scale-dependent [3].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Multiscale Network Traffic Combination Prediction Based on Fuzzy Support Vector Machine

Zhang

2023

Mathematical Problems in Engineering

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In the process of multiscale network traffic prediction using a single model, the results are often single, resulting in a decline in the accuracy of multiscale network traffic prediction. In order to solve this problem effectively, a mathematical modeling method of multiscale network traffic combination prediction based on fuzzy SVM is proposed. Firstly, according to the multiscale network approximation signal, the multiscale network traffic feature function is constructed to complete the multiscale network traffic feature extraction. Secondly, according to the feature extraction results, the fuzzy membership function is introduced into the SVM, and the fuzzy SVM is used to classify the multiscale network traffic. Finally, based on the traffic classification results, the combination prediction of multiscale network traffic is completed by combining the grey Verhulst prediction model with the GNN model. The experimental results show that the prediction accuracy of this method for multiscale network traffic is higher, and the prediction accuracy can always maintain above 95%, and the MSE and MAE values are relatively low.

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“…The security measures used for IoT devices, such as authentication, network security, encryption, access control, and application security, are ineffective, and their vulnerabilities are deeply rooted. Therefore, existing security methods need to be improved to ensure that the IoT ecosystem is indeed secure [18,19].…”

Section: Related Workmentioning

confidence: 99%

Cyber Threat Intelligence for IoT Using Machine Learning

2022

Self Cite

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The Internet of Things (IoT) is a technological revolution that enables human-to-human and machine-to-machine communication for virtual data exchange. The IoT allows us to identify, locate, and access the various things and objects around us using low-cost sensors. The Internet of Things offers many benefits but also raises many issues, especially in terms of privacy and security. Appropriate solutions must be found to these challenges, and privacy and security are top priorities in the IoT. This study identifies possible attacks on different types of networks as well as their countermeasures. This study provides valuable insights to vulnerability researchers and IoT network protection specialists because it teaches them how to avoid problems in real networks by simulating them and developing proactive solutions. IoT anomalies were detected by simulating message queuing telemetry transport (MQTT) over a virtual network. Utilizing DDoS attacks and some machine learning algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR), as well as an artificial neural network, multilayer perceptron (MLP), naive Bayes (NB) and decision tree (DT) are used to detect and mitigate the attack. The proposed approach uses a dataset of 4998 records and 34 features with 8 classes of network traffic. The classifier RF showed the best performance with 99.94% accuracy. An intrusion detection system using Snort was implemented. The results provided theoretical proof of applicability and feasibility.

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Cyber-Attack Detection and Mitigation Using SVM for 5G Network

Cited by 15 publications

References 30 publications

An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

Mathematical Modeling of Multiscale Network Traffic Combination Prediction Based on Fuzzy Support Vector Machine

Cyber Threat Intelligence for IoT Using Machine Learning

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