Principle and method of deception systems synthesizing for malware and computer attacks detection
Abstract
Keywords
Full Text:
PDFReferences
Lysenko, S., & Savenko, B. Distributed Discrete Malware Detection Systems Based on Partial Centralization and Self-Organization. International Journal of Computing, 2023, vol. 22, no, 2. pp. 117-39. DOI: 10.47839/ijc.22.2.3082.
Breeden, J. 5 top deception tools and how they ensnare attackers. Available at: https://www.csoonline.com/article/570063/5-top-deception-tools-and-how-they-ensnare-attackers.html (accessed 06.08.2023).
Acalvio ShadowPlex. Autonomous Deception. Available at: https://www.acalvio.com/product/ 04.09.2023 (аccessed 06.08.2023).
SentinelOne. Available at: https://www.sentinelone.com/surfaces/identity/ (аccessed 06.08.2023).
Proofpoint Identity Threat Defense. Available at: https://www.proofpoint.com/us/illusive-is-now-proofpoint (аccessed 06.08.2023).
Counter Craft Security. Available at: https://www.countercraftsec.com/ (аccessed 06.08.2023).
Fidelis Security. Available at: https://fidelissecurity.com/fidelis-elevate/ (аccessed 06.08.2023).
The Commvault Data Protection Platform. Available at: https://www.commvault.com/ (аccessed 06.08.2023).
Labyrinth Deception Platform. Available at: https://labyrinth.tech/platform (аccessed 06.08.2023).
Labyrinth Deception Platform. Datasheet. Available at: https://labyrinth.tech/assets/media/pdf/labyrinth-data-sheet.pdf (аccessed 06.08.2023).
Feng, M., Xiao, B., Yu, B., Qian, J., Zhang, X., Chen, P., & Li, B. A Novel Deception Defense-Based Honeypot System for Power Grid Network. International Conference on Smart Computing and Communication, 2021, Vol. 13202, pp. 297-307. Cham: Springer International Publishing. DOI: 10.1007/978-3-030-97774-0_27.
Walter, E., Ferguson-Walter, K., & Ridley, A. Incorporating deception into cyberbattlesim for autonomous defense. 2021. arXiv preprint arXiv:2108.13980. DOI: 10.48550/arXiv.2108.13980.
Anwar, A. H., Kamhoua, C. A., Leslie, N. O., & Kiekintveld, C. Honeypot Allocation for Cyber Deception Under Uncertainty. IEEE Transactions on Network and Service Management, 2022, vol. 19. no. 3, pp. 3438-3452. DOI: 10.1109/TNSM.2022.3179965.
Sayed, M. A., Anwar, A. H., Kiekintveld, C., & Kamhoua, C. Honeypot Allocation for Cyber Deception in Dynamic Tactical Networks: A Game Theoretic Approach. 14th International Conference on Decision and Game Theory for Security. GameSec 2023. 2023. arXiv preprint. arXiv:2308.11817. DOI: 10.48550/arXiv.2308.11817.
Anwar, A. H., & Kamhoua, C. A. Cyber Deception using Honeypot Allocation and Diversity: A Game Theoretic Approach. 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2022, pp. 543-549. DOI: 10.1109/CCNC49033.2022.9700616.
Anwar, A. H., Kamhoua, C., & Leslie, N. Honeypot allocation over attack graphs in cyber deception games. International Conference on Computing, Networking and Communications (ICNC), 2020, pp. 502-506, IEEE. DOI: 10.1109/ICNC47757.2020.9049764.
Acosta, J. C., Basak, A., Kiekintveld, C., & Kamhoua, C. Lightweight On-Demand Honeypot Deployment for Cyber Deception. In Gladyshev, P., Goel, S., James, J., Markowsky, G., Johnson, D. (eds) Digital Forensics and Cyber Crime. ICDF2C 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2022, vol. 441, pp. 294-312. Springer, Cham. DOI: 10.1007/978-3-031-06365-7_18.
Priya, D., & Chakkaravarthy, S. Containerized cloud-based honeypot deception for tracking attackers. Scientific Reports, 2023, vol. 13. DOI: 10.1038/s41598-023-28613-0.
Al-Shaer, E., Wei, J., Hamlen, K. W., & Wang, C. Autonomous Cyber Deception. Reasoning. Adaptive Planning. and Evaluation of HoneyThings. Springer Nature Switzerland AG, 2019. DOI: 10.1007/978-3-030-02110-8.
Wegerer, M., & Tjoa, S. Defeating the Database Adversary Using Deception – A MySQL Database Honeypot. International Conference on Software Security and Assurance (ICSSA), Saint Pölten. Austria, 2016. pp. 6-10. DOI: 10.1109/ICSSA.2016.8.
Kedrowitsch, A., Danfeng, Y., Gang. W., & Cameron, K. A First Look: Using Linux Containers for Deceptive Honeypots. Proceedings of the 2017 Workshop on Automated Decision Making for Active Cyber Defense (SafeConfig ‘17). Association for Computing Machinery, New York, NY, USA, 2017, pp. 15–22. DOI: 10.1145/3140368.3140371.
Almeshekah, M. H., & Spafford, E. H. Cyber Security Deception. In: Jajodia. S., Subrahmanian. V., Swarup. V., Wang. C. (eds). Cyber Deception, 2016, p. 318, Cham. Springer. DOI: 10.1007/978-3-319-32699-3_2.
Zobal, L., Kolář, D., & Fujdiak, R. Current State of Honeypots and Deception Strategies in Cybersecurity. 11th International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops (ICUMT). Dublin. Ireland. 2019. pp. 1-9. DOI: 10.1109/ICUMT48472.2019.8970921.
Dahbul, R. N., Lim C., & Purnama. J. Enhancing honeypot deception capability through network service fingerprint. Journal of Physics: Conference Series, 2017, vol. 801, article no. 012057. DOI: 10.1088/1742-6596/801/1/012057.
Razali, M. F., Razali, M. N., Mansor, F. Z., Muruti, G., & Jamil, N. IoT Honeypot: A Review from Researcher's Perspective. IEEE Conference on Application. Information and Network Security (AINS). Langkawi. Malaysia, 2018. pp. 93-98. DOI: 10.1109/AINS.2018.8631494.
La, Q. D., Quek, T. Q. S., Lee, J., & Zhu, H. Deceptive Attack and Defense Game. Honeypot-Enabled Networks for the Internet of Things. IEEE Internet of Things Journal, 2016, vol. 3, no. 6. pp. 1025-1035. DOI: 10.1109/JIOT.2016.2547994.
Rowe, N. C. Honeypot Deception Tactics. In: Al-Shaer, E., Wei, J., Hamlen, K., Wang, C. (eds) Autonomous Cyber Deception. Springer. Cham, 2019. DOI: 10.1007/978-3-030-02110-8_3.
Lysenko, S., Savenko, O., Bobrovnikova, K., & Kryshchuk, A. Self-adaptive system for the corporate area network resilience in the presence of botnet cyberattacks. Communications in Computer and Information Science, 2018, vol. 860, pp. 385-401. DOI: 10.1007/978-3-319-92459-5_31.
Pomorova, O., Savenko, O., Lysenko, S., Kryshchuk, A., & Bobrovnikova, K. A Technique for the Botnet Detection Based on DNS-Traffic Analysis. Computer Networks. CN 2015. Communications in Computer and Information Science, 2015, vol. 522, pp. 127-138. DOI: 10.1007/978-3-319-19419-6_12.
Bobrovnikova, K., Lysenko, S., Savenko, B., Gaj, P., & Savenko, O. Technique for IoT malware detection based on control flow graph analysis. Radioelectronic and Computer Systems, 2022, vol. 1, pp. 141–153. DOI: 10.32620/reks.2022.1.11.
Lysenko, S., Savenko, O., Bobrovnikova, K., Kryshchuk, A., & Savenko, B. Information technology for botnets detection based on their behaviour in the corporate area network. Communications in Computer and Information Science, 2017, vol. 718, pp. 166–181. DOI: 10.1007/978-3-319-59767-6_14.
Moskalenko, V., Zarets'kyy, M., Moskalenko, A., Kudryavtsev, A., & Semashko, V. Multi-layer model and training method for malware traffic detection based on decision tree ensemble. Radioelectronic and Computer Systems, 2020, vol. 2, pp. 92-101. DOI: 10.32620/reks.2020.2.08.
Morozova, O., Nicheporuk, A, Tetskyi, A., & Tkachov, V. Methods and technologies for ensuring cybersecurity of industrial and web-oriented systems and networks. Radioelectronic and Computer Systems, 2021, vol. 4, pp. 145-156. DOI: 10.32620/reks.2021.4.12.
Dovbysh A., Liubchak, V., Shelehov, I., Simonovskiy, J., & Tenytska, A. Information-extreme machine learning of a cyber attack detection system. Radioelectronic and Computer Systems. 2022, vol. 3, pp. 121-131. DOI: 10.32620/reks.2022.3.09.
Fursov, I., Yamkovyi, K., & Shmatko, O. Smart Grid and wind generators: an overview of cyber threats and vulnerabilities of power supply networks. Radioelectronic and Computer Systems, 2022, vol. 4. pp. 50-63. DOI: 10.32620/reks.2022.4.04.
Ahmed, J., Karpenko, A., Tarasyuk, O., Gorbenko, A., & Sheikh-Akbari, A. Consistency issue and related trade-offs in distributed replicated systems and databases: a review. Radioelectronic and Computer Systems, 2023, vol. 2. pp. 171-179. DOI: 10.32620/reks.2023.2.14.
Alnajim, A. M., Habib, S., Islam, M., Albelaihi, R, & Alabdulatif, A. Mitigating the Risks of Malware Attacks with Deep Learning Techniques. Electronics, 2023, vol. 12, iss. 14. pp. 3166. DOI: 10.3390/electronics12143166.
da Silva, A. A., & Pamplona Segundo, M. On Deceiving Malware Classification with Section Injection. Machine Learning and Knowledge Extraction, 2023, vol. 5, iss. 1. pp. 144-168. DOI: 10.3390/make5010009.
Saminathan, K., Mulka, S. T. R., Damodharan, S., Maheswar, R., & Lorincz, J. An Artificial Neural Network Autoencoder for Insider Cyber Security Threat Detection. Future Internet. 2023, vol. 15, iss. 12, article no. 373. DOI: 10.3390/fi15120373.
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., & Doulamis, N. Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies, 2021, vol. 9. DOI: 10.3390/technologies9040081.
Tharwat, A. Classification assessment methods. Applied Computing and Informatics, 2021, vol. 17, no. 1, pp. 168-192. DOI: 10.1016/j.aci.2018.08.003.
Powers, D. Evaluation: From Precision. Recall and F-Measure to ROC. Informedness. Markedness & Correlation. arXiv 2020. DOI: 10.48550/arXiv.2010.16061.
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., & Doulamis, N. A Machine Learning Based Classification Method for Customer Experience Survey Analysis. Technologies, 2020, vol. 8, article no. 76. DOI: 10.3390/technologies8040076.
Lysenko, S., Savenko, O., & Bobrovnikova, K. DDoS Botnet Detection Technique Based on the Use of the Semi-Supervised Fuzzy c-Means Clustering. CEUR-WS, 2018, vol. 2104, pp. 688-695.
Lysenko, S., Bobrovnikova, K., Shchuka, R., & Savenko, O. A Cyberattacks Detection Technique Based on Evolutionary Algorithms. 11th International Conference on Dependable Systems. Services and Technologies (DESSERT), 2020, vol. 1, pp. 127-132. DOI: 10.1109/DESSERT50317.2020.9125016.
DOI: https://doi.org/10.32620/reks.2023.4.10
Refbacks
- There are currently no refbacks.