Cyberattack Prevention Using Artificial Intelligence with Advanced Technical Solutions
DOI:
https://doi.org/10.65204/djes.v3i3.790Keywords:
Artificial Intelligence Cyberattack Prevention Deep Learning Intrusion Detection System Machine Learning Network SecurityAbstract
The rapid proliferation of interconnected digital systems has significantly expanded the attack surface for malicious cyber activities, rendering traditional signature-based and rule-driven security mechanisms increasingly inadequate against sophisticated and evolving threats. This paper presents a comprehensive framework for cyberattack prevention leveraging artificial intelligence (AI), with emphasis on machine learning (ML) and deep learning (DL) methodologies. The proposed system integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures within an ensemble learning paradigm to perform real time intrusion detection, anomaly classification, and threat mitigation. The UNSW-NB15 benchmark dataset is employed for training and evaluation, with feature selection techniques applied to reduce dimensionality while preserving discriminative capability. Experimental results demonstrate that the proposed hybrid AI model achieves a detection accuracy of 97.8%, an F1 score of 0.976, and a false positive rate of 1.8%, outperforming conventional approaches. Adversarial robustness evaluation confirms minimal performance degradation under evasion attacks. The proposed framework is further validated for applicability in Internet of Things (IoT) and 5G network environments.
References
REFERENCES
A. L. Buczak and E. Guven, "A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection," IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153-1176, 2016.
H. Hindy et al., "A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 3140-3175, 2020.
M. A. Ferrag et al., "Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study," Journal of Information Security and Applications, vol. 50, p. 102419, 2020.
Y. Xin et al., "Machine Learning and Deep Learning Methods for Cybersecurity," IEEE Access, vol. 6, pp. 35365-35381, 2018.
A. Khraisat et al., "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019.
D. Berman et al., "A survey of deep learning methods for cyber security," Information, vol. 10, no. 4, p. 122, 2019.
I. H. Sarker et al., "Cybersecurity data science: an overview from machine learning perspective," Journal of Big Data, vol. 7, no. 1, pp. 1-29, 2020.
R. Vinayakumar et al., "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525-41550, 2019.
G. Apruzzese et al., "Evading Machine Learning-based Network Intrusion Detection Systems," IEEE Security and Privacy, vol. 17, no. 4, pp. 44-53, 2019.
E. Hodo et al., "Threat analysis of IoT networks using artificial neural network intrusion detection system," in International Symposium on Networks, Computers and Communications (ISNCC), 2016, pp. 1-6.
M. Roopak et al., "Deep learning models for cyber security in IoT networks," in IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), 2019, pp. 0452-0457.
T. A. Tang et al., "Deep learning approach for network intrusion detection in software defined networking," in International Conference on Wireless Networks and Mobile Communications (WINCOM), 2016, pp. 258-263.
S. M. Kasongo and T. Sun, "A deep learning method with wrapper based feature extraction for wireless intrusion detection system," Computers & Security, vol. 92, p. 101752, 2020.
P. Mishra et al., "A detailed investigation and analysis of using machine learning techniques for intrusion detection," IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 686-728, 2018.
C. Yin et al., "A deep learning approach for intrusion detection using recurrent neural networks," IEEE Access, vol. 5, pp. 21954-21961, 2017.
Z. Ahmad et al., "Network intrusion detection system: A systematic study of machine learning and deep learning approaches," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, p. e4150, 2021.
K. Shaukat et al., "Performance comparison and current challenges of using machine learning techniques in cybersecurity," Energies, vol. 13, no. 10, p. 2509, 2020.
L. Fernandez Maimo et al., "A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks," IEEE Access, vol. 6, pp. 7700-7712, 2018.
M. A. Ambusaidi et al., "Building an intrusion detection system using a filter-based feature selection algorithm," IEEE Transactions on Computers, vol. 65, no. 10, pp. 2986-2998, 2016.
