Volume 13 , Issue 2 , PP: 124-139, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
U. Harita 1 , Moulana Mohammed 2 *
Doi: https://doi.org/10.54216/JCIM.130210
For a long time, malware has posed a significant risk to computer system security. The effectiveness of conventional detection techniques based on static and dynamic analysis is restricted due to the quick advancement of anti-detection technologies. In recent years, AI-based malware detection has increasingly been employed to combat malware due to its improved predictive ability. Unfortunately, because malware may be so diverse, it can be challenging to extract features from it, which makes using AI for malware detection ineffective. A malware classifier based on an Improved Salp Swarm optimization for feature selection and a Boosted tree with Conditional Quantile Estimation (ISSO-BCQE) is developed to adapt different malware properties to solve the problem. Specifically, the malware code is extracted, and the feature sequence is generated into a boosting tree where the feature map of the node is extracted using BCQE, where a boosting network is used to design a classifier and the method's performance is finally analyzed and compared. The results show that our model works better than other approaches regarding FPR and accuracy. It also shows that the method beats current methods with the highest accuracy of 99.6% in most detecting circumstances. It is also stable in handling malware growth and evolution.
IoT , network traffic , feature selection , classification , feature optimization
[1] Y. LeCun, Y. Bengio, and G. Hinton, ‘‘Deep learning,’’ Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
[2] Reddy, Y. Ramadevi, and K. V. N. Sunitha, ‘‘Effective discriminant function for intrusion detection using SVM,’’ in Proc. Int. Conf. Adv. Comput., Commun. Inform. (ICAC), Sep. 2016, pp. 1148–1153.
[3] Ingre and A. Yadav, ‘‘Performance analysis of NSL-KDD dataset using ANN,’’ in Proc. Int. Conf. Signal Process. Commun. Eng. Syst., Jan. 2015, pp. 92–96.
[4] Farnaaz and M. A. Jabbar, ‘‘Random forest modelling for network intrusion detection system,’’ Procedia Comput. Sci., vol. 89, pp. 213–217, Jan. 2016.
[5] Khan and N. Jain, ‘‘A survey on intrusion detection systems and classification techniques,’’ Int. J. Sci. Res. Sci., Eng. Technol., vol. 2, no. 5, pp. 202–208, 2016
[6 Amel Ali Alhussan, Hassan K. Ibrahim Al-Mahdawi, Ammar Kadi, Spam Detection in Connected Networks Using Particle Swarm and Genetic Algorithm Optimization: Youtube as a Case study, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 6 , No. 1 , (2023) : 08-18 (Doi : https://doi.org/10.54216/IJWAC.060101)
[7] Ashfaq, X.-Z. Wang, J. Z. Huang, H. Abbas, and Y.-L. He, ''Fuzziness based semi-supervised learning approach for an intrusion detection system,'' Inf. Sci., vol. 378, pp. 484–497, Feb. 2017.
[8] Ashfaq, X.-Z. Wang, J. Z. Huang, H. Abbas, and Y.-L. He, ''Fuzziness based semi-supervised learning approach for an intrusion detection system,'' Inf. Sci., vol. 378, pp. 484–497, Feb. 20
[9] Chang, W. Li, and Z. Yang, "Network intrusion detection based on random forest and support vector machine," Proc. IEEE Int. Conf. Comput. Sci. Eng./IEEE Int. Conf. Embedded Ubiquitous Comput., Jul. 2017, pp. 635–638
[10 ]Sathya Preiya V, Kumar VDA. Deep Learning-Based Classification and Feature Extraction for Predicting Pathogenesis of Foot Ulcers in Patients with Diabetes. Diagnostics. 2023; 13(12):1983. https://doi.org/10.3390/diagnostics13121983.
[11] Balakrishnan C, Ambeth Kumar VD. IoT-Enabled Classification of Echocardiogram Images for Cardiovascular Disease Risk Prediction with Pre-Trained Recurrent Convolutional Neural Networks. Diagnostics (Basel). 2023 Feb 18;13(4):775. doi: 10.3390/diagnostics13040775. PMID: 36832263; PMCID: PMC9955174.
[12] Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, “Deep learning and its applications to machine health monitoring: A survey,” Submitted to IEEE Trans. Neural Netw. Learn. Syst., 2016. [Online]. Available: http://arxiv.org/abs/1612.07640
[13] Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn—Res., vol. 11, pp. 3371–3408, 2010.
[14] Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy, Enhancing K-Nearest Neighbors Algorithm in Wireless Sensor Networks through Stochastic Fractal Search and Particle Swarm Optimization, Journal of Journal of Cybersecurity and Information Management, Vol. 13 , No. 1 , (2024) : 76-84 (Doi : https://doi.org/10.54216/JCIM.130108)
[15] Alrawashdeh and C. Purdy, “Toward an online anomaly intrusion detection system based on deep learning,” in Proc. 15th IEEE Int. Conf. Mach. Learn. Appl., Anaheim, CA, USA, Dec. 2016, pp. 195–200
[16] Hemamalini, Selvamani, and Visvam Devadoss Ambeth Kumar. 2022. "Outlier Based Skimpy Regularization Fuzzy Clustering Algorithm for Diabetic Retinopathy Image Segmentation" Symmetry 14, no. 12: 2512. https://doi.org/10.3390/sym14122512.
[17] Kumar, V.D.A., Sharmila, S., Kumar, A. et al. A novel solution for finding postpartum haemorrhage using fuzzy neural techniques. Neural Comput & Applic 35, 23683–23696 (2023). https://doi.org/10.1007/s00521-020-05683-z
[18] V. D. A. Kumar, M. Raghuraman, A. Kumar, M. Rashid, S. Hakak and M. P. K. Reddy, "Green-Tech CAV: Next Generation Computing for Traffic Sign and Obstacle Detection in Connected and Autonomous Vehicles," in IEEE Transactions on Green Communications and Networking, vol. 6, no. 3, pp. 1307-1315, Sept. 2022, doi: 10.1109/TGCN.2022.3162698.
[19] Potluri and C. Diedrich, "Accelerated deep neural networks for an enhanced intrusion detection system," Proc. IEEE 21st Int. Conf. Emerg. Technol. Factory Autom., Berlin, Germany, Sep. 2016, pp. 1–8.
[20] Tang, L. Mhamdi, D. McLernon, S. A. R. Zaidi, and M. Ghogho, "Deep learning approach for network intrusion detection in software-defined networking," Proc. Int. Conf. Wireless Netw. Mobile Commun., Oct. 2016, pp. 258–263
[21] Hodo, X. J. A. Bellekens, A. Hamilton, C. Tachtatzis, and R. C. Atkinson, Shallow and deep networks intrusion detection system: A taxonomy and survey, Submitted to ACM Survey, 2017, [Online]. Available: http://arxiv.org/abs/1701.02145
[22] Kim, G., Yi, H., Lee, J., Paek, Y., Yoon, S.: Lstm-based system-call language modelling and robust ensemble method for designing host-based intrusion detection systems. arXiv preprint arXiv:1611.01726 (2016)
[23] Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials 18(2) (2016) 1153–1176
[24] Javaid, A., Niyaz, Q., Sun, W., Alam, M.: A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), New York, NY, USA. Volume 35. (2015) 2126
[25] Abhishek Kumar, Kamred Udham Singh, Visvam Devadoss Ambeth Kumar, Tapan Kant, Abdul Khader Jilani Saudagar, Abdullah Al Tameem, Mohammed Al Khathami, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Khalid Mahmood Malik, " Robust Watermarking Scheme for NIfTI Medical Images", Vol.71, No.2, 2022, pp.3107-3125, doi:10.32604/cmc.2022.022817
[26]V.D.Ambeth Kumar and M.Ramakrishan (2013), “Temple and Maternity Ward Security using FPRS” in the month of May for the Journal of Electrical Engineering & Technology (JEET) ,Vol. 8, No. 3, PP: 633-637.
[28] Safa Otoum, Burak Kantarci, and Hussein T. Mouftah, “Adaptively supervised and intrusion-aware data aggregation for wireless sensor clusters in critical infrastructures,” in 2018 IEEE International Conference on Communications (ICC), May 2018, pp. 1–6
[29] Arnaldo Gouveia and Miguel Correia, A Systematic Approach for the Application of Restricted Boltzmann Machines in Network Intrusion Detection, vol. 10305, 05 2017.
[30] Beigh and M. A. Peer, “Performance evaluation of different intrusion detection system: An empirical approach,” in Intl Conf. on Computer Communication and Informatics, Jan 2014, pp. 1–7.
[31] Zhou W., Wen J., Koh Y., Xiong Q., Gao M., Dobbie G., Alam S. (2015), “Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis” PLoS One, July, 10(7), p.e0130968
[32] Kumari T., Bedi P. (2017), “A Comprehensive Study of Shilling Attacks in Recommender Systems”, IJCSI International Journal of Computer Science Issues, 14(4), 44-50
[33] Cao, J., Wu, Z., Mao, B. & Zhang, Y (2013). “Shilling attack detection utilizing semi-supervised learning method for attack detection utilizing semi-supervised learning method for collaborative recommender system”. World Wide Web Journal, 16(5-6): 729-748.
[34] Yu H, Gao R, Wang K, Zhang F (2017), “A novel robust recommendation method based on kernel matrix factorization”. J Intell Fuzzy Syst 32(3):2101–2109
[35] Yang Z., Cai Z. (2017), “Detecting abnormal profiles in collaborative filtering recommender systems”. Journal of Intelligent Information Systems, 48(3), 499-518
[36] Zhou W., Wen J., Qu Q., Zeng J., Cheng T. (2018), "Shilling attack detection for recommender systems based on the credibility of group users and rating time series", PLoS One, May, 13(5), p.e0196533
[37] Turk A., Bilge A., (2019). “Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks”, Expert Systems with Applications, 115, p.386-402
[38] Moradi P., Ahmadian S., (2015), “A reliability-based recommendation method to improve trust-aware recommender systems”, Expert Systems with Applications, 42, 7386-7389.
[39] Paradarami, N.D. Bastian, J.L. Wightman, “A Hybrid recommender system using artificial neural networks”, Expert Systems with Applications, Vol. 83, (2017), 300-313.
[40] Agar ap, ''A neural network architecture combines gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data,'' Proc. 10th Int. Conf. Mach. Learn. Comput., Feb. 2018, pp. 26–30.
[41] Around, M.-A. El Hussaini, A. El Hore, and J. Ben-Othman, ''Real-time detection of MAC layer misbehaviour in mobile ad hoc networks,’’ Appl. Comput. Information., vol. 13, no. 1, pp. 1–9, 2017.
[42] P. Sherubha, P Amudhavalli, SP Sasirekha, “Clone attack detection using random forest and multi-objective cuckoo search classification”, International Conference on Communication and Signal Processing (ICCSP), pp. 0450-0454, 2019.
[43] S. Dinesh, K. Maheswari, B. Arthi, P. Sherubha, A. Vijay, S. Sridhar, T. Rajendran, and Yosef Asrat Waji, “Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms”, Hindawi Journal of Healthcare Engineering, Volume 2022.
