2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 1 , PP: 57-69, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Thulasi Bikku 1 , Suresh Babu Chandolu 2 , S. Phani Praveen 3 * , Narasimha Rao Tirumalasetti 4 , K. Swathi 5 , U. Sirisha 6
Doi: https://doi.org/10.54216/JISIoT.120105
The proposed Quantum Neural Networks (QNN) perform better than traditional machine learning models. The escalating complexity of malware poses a significant challenge to cybersecurity, necessitating innovative approaches to keep pace with its rapid evolution. Contemporary malware analysis techniques underscore the urgent need for solutions that can adapt to the dynamic functionalities of evolving malware. In this context, Quantum Neural Networks (QNNs) emerge as a cutting-edge and distinctive approach to malware analysis, promising to overcome the limitations of conventional methods. Our exploration of QNNs focuses on uncovering their valuable applications, particularly in real-time malware research. We meticulously examine the advantages of QNNs in contrast to conventional machine-learning methods employed in malware detection and classification. The proposed QNN showcases its unique capability to handle complex patterns, emphasizing its potential to achieve heightened levels of accuracy. Our contribution extends to introducing a dedicated framework for QNN-based malware analysis, harnessing the formidable computational capabilities of quantum computing for real-time malware analysis. This framework is structured around three pivotal components, Malware Feature Extraction utilizes quantum feature extraction techniques to identify relevant features from malware samples. Malware Classification employs a QNN classifier to categorize malware samples as benign or malicious. Real-Time Analysis enables the instantaneous examination of malware samples by integrating feature extraction and classification within a streaming data pipeline. Our proposed methodology undergoes comprehensive evaluation using a benchmark dataset of malware samples. The Proposed Quantum Neural Networks (QNNs) demonstrated a high accuracy of 0.95, outperforming other quantum models such as Quantum Support Vector Machines (QSVM) and Quantum Decision Trees (QDT), as well as classical models like Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT) on the Malware DB dataset. The results affirm the framework's exceptional accuracy rates and low latency, establishing its suitability for real-time malware analysis. These findings underscore the potential for QNNs to revolutionize malware evaluation and strengthen real-time defenses against cyberattacks. While our research demonstrates promising outcomes, further exploration and development in this domain are imperative to fully exploit the extensive viability that QNNs offer for cybersecurity applications.
Quantum neural networks , malware analysis , real-time analysis , cybersecurity , machine learning.
[1] Nishant, Rohit, Mike Kennedy, and Jacqueline Corbett. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda." International Journal of Information Management 53 (2020): 102104.
[2] Zeguendry, Amine, Zahi Jarir, and Mohamed Quafafou. "Quantum machine learning: A review and case studies." Entropy 25.2 (2023): 287.
[3] Thatha, V. N., Donepudi, S., Safali, M. A., Praveen, S. P., Tung, N. T., & Cuong, N. H. H. (2023). Security and risk analysis in the cloud with software defined networking architecture. International Journal of Electrical & Computer Engineering (2088-8708), 13(5).
[4] Soldal, Kim Verner. Modularity as a solution to spatial interference in neural networks. MS thesis. Institutt for datateknikk og informasjonsvitenskap, 2012.
[5] Pawlicki, Marek, Rafał Kozik, and Michał Choraś. "A survey on neural networks for (cyber-) security and (cyber-) security of neural networks." Neurocomputing 500 (2022): 1075-1087.
[6] Shah, Bhavin, and Bhushan H. Trivedi. "Artificial neural network based intrusion detection system: A survey." International Journal of Computer Applications 39.6 (2012): 13-18.
[7] Phani Praveen, S., Ali, M. H., Jarwar, M. A., Prakash, C., Reddy, C. R. K., Malliga, L., & Chandru Vignesh, C. (2023). 6G assisted federated learning for continuous monitoring in wireless sensor network using game theory. Wireless Networks, 1-27.
[8] Sindhura, S., Phani Praveen, S., Madhuri, A., & Swapna, D. (2022, May). Different feature selection methods performance analysis for intrusion detection. In Smart Intelligent Computing and Applications, Volume 2: Proceedings of Fifth International Conference on Smart Computing and Informatics (SCI 2021) (pp. 523-531). Singapore: Springer Nature Singapore.
[9] Vasuki, M., et al. "Overview of Quantum Computing in Quantum Neural Network and Artificial Intelligence." (2023).
[10] Nazir, Ahsan, et al. "Advancing IoT security: A systematic review of machine learning approaches for detecting IoT botnets." Journal of King Saud University-Computer and Information Sciences (2023): 101820.
[11] Bikku, Thulasi, and Radhika Paturi. "A novel somatic cancer gene-based biomedical document feature ranking and clustering model." Informatics in Medicine Unlocked 16 (2019): 100188.
[12] Ciaramella, Giovanni, et al. "Introducing quantum computing in mobile malware detection." Proceedings of the 17th International Conference on Availability, Reliability and Security. 2022.
[13] Mercaldo, Francesco, et al. "Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification." Applied Sciences 12.23 (2022): 12025.
[14] Jyothi, V. E., Kumar, D. L. S., Thati, B., Tondepu, Y., Pratap, V. K., & Praveen, S. P. (2022, December). Secure Data Access Management for Cyber Threats using Artificial Intelligence. In 2022 6th International Conference on Electronics, Communication and Aerospace Technology (pp. 693-697). IEEE.
[15] Garg, Umang, et al. "Identification and Detection of Behavior-Based Malware using Machine Learning." 2023 International Conference on Artificial Intelligence and Smart Communication (AISC). IEEE, 2023.
[16] Ciaramella, Giovanni, et al. "Introducing quantum computing in mobile malware detection." Proceedings of the 17th International Conference on Availability, Reliability and Security. 2022.
[17] S Phani Praveen, V Sathiya Suntharam, S Ravi, U. Harita, Venkata Nagaraju Thatha and D Swapna, “A Novel Dual Confusion and Diffusion Approach for Grey Image Encryption using Multiple Chaotic Maps” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01408106.
[18] Abd El-Aziz, Rasha M., Ahmed I. Taloba, and Fahad A. Alghamdi. "Quantum computing optimization technique for IoT platform using the modified deep residual approach." Alexandria Engineering Journal 61.12 (2022): 12497-12509.
[19] Wu, Jindi, Zeyi Tao, and Qun Li. "wpScalable Quantum Neural Networks for Classification." 2022 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2022.
[20] Reddy, A. S., Praveen, S. P., Ramudu, G. B., Anish, A. B., Mahadev, A., & Swapna, D. (2023, January). A Network Monitoring Model based on Convolutional Neural Networks for Unbalanced Network Activity. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1267-1274). IEEE.
[21] Praveen, S. P., Sindhura, S., Srinivasu, P. N., & Ahmed, S. (2023, September). Combining CNNs and Bi-LSTMs for Enhanced Network Intrusion Detection: A Deep Learning Approach. In 2023 3rd International Conference on Computing and Information Technology (ICCIT) (pp. 261-268). IEEE.
[22] Jeffrey, Nicholas, Qing Tan, and José R. Villar. "A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems." Electronics 12.15 (2023): 3283.
[23] Reem Atassi, Fuad Alhosban. (2023). Predictive Maintenance in IoT: Early Fault Detection and Failure Prediction in Industrial Equipment. Journal of Journal of Intelligent Systems and Internet of Things, 9 ( 2 ), 231-238 (Doi : https://doi.org/10.54216/JISIoT.090217).
[24] Nihal N. Mostafa, Esmeralda Kazia. (2023). Smart Sensor Networks for Industrial IoT Applications. Journal of Journal of Intelligent Systems and Internet of Things, 8 ( 2 ), 45-53 (Doi : https://doi.org/10.54216/JISIoT.080204)
