How to cite this paper
Abualhaj, M., Abu-Shareha, A., Shambour, Q., Alsaaidah, A., Al-Khatib, S & Anbar, M. (2024). Customized K-nearest neighbors’ algorithm for malware detection.International Journal of Data and Network Science, 8(1), 431-438.
Refrences
Abualhaj, M. M., Abu-Shareha, A. A., Hiari, M. O., Alrabanah, Y., Al-Zyoud, M., & Alsharaiah, M. A. (2022). A Para-digm for DoS Attack Disclosure using Machine Learning Techniques. International Journal of Advanced Computer Science and Applications, 13(3).
Al Zaabi, A., & Mouheb, D. (2020, November). Android malware detection using static features and machine learning. In 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) (pp. 1-5). IEEE.
Al-Mimi, H., Hamad, N. A., Abualhaj, M. M., Daoud, M. S., Al-dahoud, A., & Rasmi, M. (2023). An Enhanced Intrusion Detection System for Protecting HTTP Services from Attacks. International Journal of Advances in Soft Computing & Its Applications, 15(2).
Alsharaiah, M., Abu-Shareha, A., Abualhaj, M., Baniata, L., Adwan, O., Al-saaidah, A., & Oraiqat, M. (2023). A new phishing-website detection framework using ensemble classification and clustering. International Journal of Data and Network Science, 7(2), 857-864.
Alves, T., Das, R., & Morris, T. (2018). Embedding encryption and machine learning intrusion prevention systems on pro-grammable logic controllers. IEEE Embedded Systems Letters, 10(3), 99-102.
Belaoued, M., Derhab, A., Mazouzi, S., & Khan, F. A. (2020). MACoMal: A multi-agent based collaborative mechanism for anti-malware assistance. IEEE Access, 8, 14329-14343.
Chen, C. W., Su, C. H., Lee, K. W., & Bair, P. H. (2020, February). Malware family classification using active learning by learning. In 2020 22nd International Conference on Advanced Communication Technology (ICACT) (pp. 590-595). IEEE.
Choudhary, S., & Sharma, A. (2020, February). Malware detection & classification using machine learning. In 2020 Inter-national Conference on Emerging Trends in Communication, Control and Computing (ICONC3) (pp. 1-4). IEEE.
Das, D., & Nanda, S. (2013, December). Securing computer networks by networking multiple OS kernels. Revisting net-work security: protecting computer networks from malwares. In World Congress on Internet Security (WorldCIS-2013) (pp. 95-98). IEEE.
Dener, M., Ok, G., & Orman, A. (2022). Malware detection using memory analysis data in big data environment. Applied Sciences, 12(17), 8604.
Gao, X., & Li, G. (2020). A KNN model based on manhattan distance to identify the SNARE proteins. Ieee Access, 8, 112922-112931.
Hegedus, J., Miche, Y., Ilin, A., & Lendasse, A. (2011, December). Methodology for behavioral-based malware analysis and detection using random projections and k-nearest neighbors classifiers. In 2011 seventh international conference on computational intelligence and security (pp. 1016-1023). IEEE.
Jain, P., Rajvaidya, I., Sah, K. K., & Kannan, J. (2022, February). Machine Learning Techniques for Malware Detection-a Research Review. In 2022 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE.
Kolesnikov, N. (2023). 50+ cybersecurity statistics for 2023 you need to know – where, who & what is targeted. Techope-dia. https://www.techopedia.com/cybersecurity-statistics.
Kolhar, M., Al-Turjman, F., Alameen, A., & Abualhaj, M. M. (2020). A three layered decentralized IoT biometric archi-tecture for city lockdown during COVID-19 outbreak. Ieee Access, 8, 163608-163617.
Lei, J., Gao, S., Shi, J., Wei, X., Dong, M., Wang, W., & Han, Z. (2022). A Reinforcement Learning Approach for Defend-ing Against Multiscenario Load Redistribution Attacks. IEEE Transactions on Smart Grid, 13(5), 3711-3722.
Ma, C., & Chi, Y. (2022). KNN normalized optimization and platform tuning based on hadoop. IEEE Access, 10, 81406-81433.
Maruf, Z. R., & Laksito, A. D. (2020, November). The comparison of distance measurement for optimizing KNN collabo-rative filtering recommender system. In 2020 3rd International Conference on Information and Communications Tech-nology (ICOIACT) (pp. 89-93). IEEE.
Peng, W., Li, F., Zou, X., & Wu, J. (2013). Behavioral malware detection in delay tolerant networks. IEEE Transactions on Parallel and Distributed systems, 25(1), 53-63.
Qbeitah, M. A., & Aldwairi, M. (2018, April). Dynamic malware analysis of phishing emails. In 2018 9th International Conference on Information and Communication Systems (ICICS) (pp. 18-24). IEEE.
Rosmansyah, Y., & Dabarsyah, B. (2015, August). Malware detection on android smartphones using API class and ma-chine learning. In 2015 International Conference on Electrical Engineering and Informatics (ICEEI) (pp. 294-297). IEEE.
Sai, M., Tyagi, A., Panda, K., & Kumar, S. (2022, November). Machine learning-based malware detection using stacking of opcodes and bytecode sequences. In 2022 Seventh International Conference on Parallel, Distributed and Grid Com-puting (PDGC) (pp. 204-209). IEEE.
Saurabh. (2018, December). Advance malware analysis using static and dynamic methodology. In 2018 International Con-ference on Advanced Computation and Telecommunication (ICACAT) (pp. 1-5). IEEE.
Sen, S., Aydogan, E., & Aysan, A. I. (2018). Coevolution of mobile malware and anti-malware. IEEE Transactions on In-formation Forensics and Security, 13(10), 2563-2574.
Shi, K., Chen, S., Li, D., Tian, K., & Feng, M. (2022, November). Analysis of the Optimized KNN Algorithm for the Data Security of DR Service. In 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) (pp. 1634-1637). IEEE.
Sonicwall, 2022 sonicwall cyber threat report. https://www.infopoint-security.de/media/2022-sonicwall-cyber-threat-report.pdf
Tirumala, S. S., Valluri, M. R., & Babu, G. A. (2019, January). A survey on cybersecurity awareness concerns, practices and conceptual measures. In 2019 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-6). IEEE.
Wu, H., Han, M., Chen, Z., Li, M., & Zhang, X. (2023). A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream. ACM Transactions on Knowledge Discovery from Data, 17(5), 1-21.
Yeo, M., Koo, Y., Yoon, Y., Hwang, T., Ryu, J., Song, J., & Park, C. (2018, January). Flow-based malware detection using convolutional neural network. In 2018 International Conference on Information Networking (ICOIN) (pp. 910-913). IEEE.
Zhang, S., Li, J., & Li, Y. (2022). Reachable distance function for KNN classification. IEEE Transactions on Knowledge and Data Engineering.
Al Zaabi, A., & Mouheb, D. (2020, November). Android malware detection using static features and machine learning. In 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) (pp. 1-5). IEEE.
Al-Mimi, H., Hamad, N. A., Abualhaj, M. M., Daoud, M. S., Al-dahoud, A., & Rasmi, M. (2023). An Enhanced Intrusion Detection System for Protecting HTTP Services from Attacks. International Journal of Advances in Soft Computing & Its Applications, 15(2).
Alsharaiah, M., Abu-Shareha, A., Abualhaj, M., Baniata, L., Adwan, O., Al-saaidah, A., & Oraiqat, M. (2023). A new phishing-website detection framework using ensemble classification and clustering. International Journal of Data and Network Science, 7(2), 857-864.
Alves, T., Das, R., & Morris, T. (2018). Embedding encryption and machine learning intrusion prevention systems on pro-grammable logic controllers. IEEE Embedded Systems Letters, 10(3), 99-102.
Belaoued, M., Derhab, A., Mazouzi, S., & Khan, F. A. (2020). MACoMal: A multi-agent based collaborative mechanism for anti-malware assistance. IEEE Access, 8, 14329-14343.
Chen, C. W., Su, C. H., Lee, K. W., & Bair, P. H. (2020, February). Malware family classification using active learning by learning. In 2020 22nd International Conference on Advanced Communication Technology (ICACT) (pp. 590-595). IEEE.
Choudhary, S., & Sharma, A. (2020, February). Malware detection & classification using machine learning. In 2020 Inter-national Conference on Emerging Trends in Communication, Control and Computing (ICONC3) (pp. 1-4). IEEE.
Das, D., & Nanda, S. (2013, December). Securing computer networks by networking multiple OS kernels. Revisting net-work security: protecting computer networks from malwares. In World Congress on Internet Security (WorldCIS-2013) (pp. 95-98). IEEE.
Dener, M., Ok, G., & Orman, A. (2022). Malware detection using memory analysis data in big data environment. Applied Sciences, 12(17), 8604.
Gao, X., & Li, G. (2020). A KNN model based on manhattan distance to identify the SNARE proteins. Ieee Access, 8, 112922-112931.
Hegedus, J., Miche, Y., Ilin, A., & Lendasse, A. (2011, December). Methodology for behavioral-based malware analysis and detection using random projections and k-nearest neighbors classifiers. In 2011 seventh international conference on computational intelligence and security (pp. 1016-1023). IEEE.
Jain, P., Rajvaidya, I., Sah, K. K., & Kannan, J. (2022, February). Machine Learning Techniques for Malware Detection-a Research Review. In 2022 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) (pp. 1-6). IEEE.
Kolesnikov, N. (2023). 50+ cybersecurity statistics for 2023 you need to know – where, who & what is targeted. Techope-dia. https://www.techopedia.com/cybersecurity-statistics.
Kolhar, M., Al-Turjman, F., Alameen, A., & Abualhaj, M. M. (2020). A three layered decentralized IoT biometric archi-tecture for city lockdown during COVID-19 outbreak. Ieee Access, 8, 163608-163617.
Lei, J., Gao, S., Shi, J., Wei, X., Dong, M., Wang, W., & Han, Z. (2022). A Reinforcement Learning Approach for Defend-ing Against Multiscenario Load Redistribution Attacks. IEEE Transactions on Smart Grid, 13(5), 3711-3722.
Ma, C., & Chi, Y. (2022). KNN normalized optimization and platform tuning based on hadoop. IEEE Access, 10, 81406-81433.
Maruf, Z. R., & Laksito, A. D. (2020, November). The comparison of distance measurement for optimizing KNN collabo-rative filtering recommender system. In 2020 3rd International Conference on Information and Communications Tech-nology (ICOIACT) (pp. 89-93). IEEE.
Peng, W., Li, F., Zou, X., & Wu, J. (2013). Behavioral malware detection in delay tolerant networks. IEEE Transactions on Parallel and Distributed systems, 25(1), 53-63.
Qbeitah, M. A., & Aldwairi, M. (2018, April). Dynamic malware analysis of phishing emails. In 2018 9th International Conference on Information and Communication Systems (ICICS) (pp. 18-24). IEEE.
Rosmansyah, Y., & Dabarsyah, B. (2015, August). Malware detection on android smartphones using API class and ma-chine learning. In 2015 International Conference on Electrical Engineering and Informatics (ICEEI) (pp. 294-297). IEEE.
Sai, M., Tyagi, A., Panda, K., & Kumar, S. (2022, November). Machine learning-based malware detection using stacking of opcodes and bytecode sequences. In 2022 Seventh International Conference on Parallel, Distributed and Grid Com-puting (PDGC) (pp. 204-209). IEEE.
Saurabh. (2018, December). Advance malware analysis using static and dynamic methodology. In 2018 International Con-ference on Advanced Computation and Telecommunication (ICACAT) (pp. 1-5). IEEE.
Sen, S., Aydogan, E., & Aysan, A. I. (2018). Coevolution of mobile malware and anti-malware. IEEE Transactions on In-formation Forensics and Security, 13(10), 2563-2574.
Shi, K., Chen, S., Li, D., Tian, K., & Feng, M. (2022, November). Analysis of the Optimized KNN Algorithm for the Data Security of DR Service. In 2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2) (pp. 1634-1637). IEEE.
Sonicwall, 2022 sonicwall cyber threat report. https://www.infopoint-security.de/media/2022-sonicwall-cyber-threat-report.pdf
Tirumala, S. S., Valluri, M. R., & Babu, G. A. (2019, January). A survey on cybersecurity awareness concerns, practices and conceptual measures. In 2019 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-6). IEEE.
Wu, H., Han, M., Chen, Z., Li, M., & Zhang, X. (2023). A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream. ACM Transactions on Knowledge Discovery from Data, 17(5), 1-21.
Yeo, M., Koo, Y., Yoon, Y., Hwang, T., Ryu, J., Song, J., & Park, C. (2018, January). Flow-based malware detection using convolutional neural network. In 2018 International Conference on Information Networking (ICOIN) (pp. 910-913). IEEE.
Zhang, S., Li, J., & Li, Y. (2022). Reachable distance function for KNN classification. IEEE Transactions on Knowledge and Data Engineering.