Optimization of a Depiction Procedure for an Artificial Intelligence-Based Network Protection System Using a Genetic Algorithm
Source abstract
Abstract
The current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more important. Another serious phenomenon that is apparent these days and that must not be discounted is the growing use of artificial intelligence techniques for carrying out network attacks. To combat these attacks, effective protection methods must also utilize artificial intelligence. Hence, we are introducing a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based network-traffic-handling controller. This decision procedure is based on a convolutional neural network that processes the incoming flow characteristics and provides a decision; the procedure can be understood as a firewall rule. The main advantage of this decision procedure is its depiction process, which has the ability to transform the incoming flow characteristics into a graphical structure. Graphical structures are regarded as very efficient data structures for processing by convolutional neural networks. This article’s main contribution consists of the development and improvement of the depiction process using a genetic algorithm. The results presented at the end of the article show that the decision procedure using an optimized depiction process brings significant improvements in comparison to previous experiments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Smart Grid Security and Resilience
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Electrical Engineering and Informatics, University of Pardubice, 532 10 Pardubice, Czech Republic
Funding
Univerzita Pardubice
References (51)
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1. Pinhasi, Z. (2020, December 20). Coronavirus Alert—Ransomware Attacks Up by 800%. Available online: https://monstercloud.com/blog/2020/03/23/coronavirus-alert-ransomware-attacks-up-by-800/.
2. NETGEAR (2020, December 20). What Is the Difference between Network Security and Cyber Security?. Available online: https://kb.netgear.com/000060950/What-is-the-difference-between-network-security-and-cyber-security.
3. Sagar, B., Niranjan, S., Nithin, K., and Sachin, D. (2019, January 27–29). Providing Cyber Security using Artificial Intelligence—A survey. Proceedings of the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.
4. Ashford, W. (2020, December 20). McAfee Combining Threat Intel with AI. Available online: https://www.computerweekly.com/news/252450903/McAfee-combining-threat-intel-with-AI.
5. Ponemon Institute LLC (2018). The Value of Artificial Intelligence in Cybersecurity, Ponemon Institute LLC. Technical Report.
6. Fulco, F., Inoguchi, M., and Mikami, T. (2018, January 10–13). Cyber-Physical Disaster Drill: Preliminary Results and Social Challenges of the First Attempts to Unify Human, ICT and AI in Disaster Response. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.10.1109/BigData.2018.8622266 ↗
7. Marin, E., Almukaynizi, M., and Shakarian, P. (2020, January 6–8). Inductive and Deductive Reasoning to Assist in Cyber-Attack Prediction. Proceedings of the 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.10.1109/CCWC47524.2020.9031154 ↗
8. Laurence, A. (2020, December 20). The Impact of Artificial Intelligence on Cyber Security. Available online: https://www.cpomagazine.com/cyber-security/the-impact-of-artificial-intelligence-on-cyber-security/.
9. Industrial Network Protection by SDN-Based IPS with AI
10. Wavelet against random forest for anomaly mitigation in software-defined networking10.1016/j.asoc.2019.02.046 ↗
11. Ahmed, M.E., and Kim, H. (2017, January 6–9). DDoS Attack Mitigation in Internet of Things Using Software Defined Networking. Proceedings of the 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), San Francisco, CA, USA.10.1109/BigDataService.2017.41 ↗
12. Santos da Silva, A., Wickboldt, J.A., Granville, L.Z., and Schaeffer-Filho, A. (2016, January 25–29). ATLANTIC: A framework for anomaly traffic detection, classification, and mitigation in SDN. Proceedings of the 2016 IEEE/IFIP Network Operations and Management Symposium (NOMS 2016), Istanbul, Turkey.10.1109/NOMS.2016.7502793 ↗
13. Cheng, Q., Wu, C., Zhou, H., Zhang, Y., Wang, R., and Ruan, W. (2018, January 28–30). Guarding the Perimeter of Cloud-Based Enterprise Networks: An Intelligent SDN Firewall. Proceedings of the 2018 IEEE 20th International Conference on High Performance Computing and Communications/IEEE 16th International Conference on Smart City/IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, UK.10.1109/HPCC/SmartCity/DSS.2018.00149 ↗
14. Bhushan, K., and Gupta, B.B. (2018, January 22–23). Detecting DDoS Attack using Software Defined Network (SDN) in Cloud Computing Environment. Proceedings of the 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.10.1109/SPIN.2018.8474062 ↗
15. Hyun, D., Kim, J., Hong, D., and Jeong, J.P. (2017, January 18–20). SDN-based network security functions for effective DDoS attack mitigation. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea.10.1109/ICTC.2017.8190794 ↗
16. ArOMA: An SDN based autonomic DDoS mitigation framework10.1016/j.cose.2017.07.008 ↗
17. Holik, F. (2020, January 23–26). Meeting Smart City Latency Demands with SDN. Proceedings of the Asian Conference on Intelligent Information and Database Systems, Phuket, Thailand.10.1007/978-3-030-14132-5_4 ↗
18. Fiessler, A., Lorenz, C., Hager, S., and Scheuermann, B. (2018, January 1–4). FireFlow—High Performance Hybrid SDN-Firewalls with OpenFlow. Proceedings of the 2018 IEEE 43rd Conference on Local Computer Networks (LCN), Chicago, IL, USA.10.1109/LCN.2018.8638090 ↗
19. Chang, Y., and Lin, T. (2018, January 15–18). Cloud-clustered firewall with distributed SDN devices. Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain.10.1109/WCNC.2018.8377305 ↗
20. Multilayer feedforward networks are universal approximators10.1016/0893-6080(89)90020-8 ↗
21. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, Prentice Hall.
22. Development of artificial intelligence based module to industrial network protection system10.1007/978-3-030-55190-2_18 ↗
23. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Available online: http://www.deeplearningbook.org.
24. Aloysius, N., and Geetha, M. (2017, January 6–8). A review on deep convolutional neural networks. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.10.1109/ICCSP.2017.8286426 ↗
25. Kizuna, H., and Sato, H. (2017, January 19–22). The Entering and Exiting Management System by Person Specification Using Deep-CNN. Proceedings of the 2017 Fifth International Symposium on Computing and Networking (CANDAR), Aomori, Japan.10.1109/CANDAR.2017.40 ↗
26. Detecting new forms of network intrusion using genetic programming
27. Gong, R., Zulkernine, M., and Abolmaesumi, P. (2005, January 23–25). A software implementation of a genetic algorithm based approach to network intrusion detection. Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, Towson, MD, USA.
28. Folino, G., Pizzuti, C., and Spezzano, G. (2005, January 22–25). GP ensemble for distributed Intrusion detection systems. Proceedings of the International Conference on Pattern Recognition and Image Analysis, Bath, UK.10.1007/11551188_6 ↗
29. Middlemiss, M., and Dick, G. (2003). Feature Selection of Intrusion Detection Data using a Hybrid Genetic Algorithm/KNN Approach. Design and Application of Hybrid Intelligent Systems, ACM.
30. Mizukoshi, M., and Munetomo, M. (2015, January 25–28). Distributed denial of services attack protection system with genetic algorithms on Hadoop cluster computing framework. Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan.10.1109/CEC.2015.7257075 ↗
31. A Distributed Network Intrusion Detection System for Distributed Denial of Service Attacks in Vehicular Ad Hoc Network10.1109/ACCESS.2019.2948382 ↗
32. Garcia, D., Lugo, A.E., Hemberg, E., and O’Reilly, U.M. (2017, January 15–19). Investigating Coevolutionary Archive Based Genetic Algorithms on Cyber Defense Networks. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO’17), Berlin, Germany.10.1145/3067695.3076081 ↗
33. Adversarial genetic programming for cyber security: A rising application domain where GP matters10.1007/s10710-020-09389-y ↗
34. Genetic algorithms in wireless networking: Techniques, applications, and issues10.1007/s00500-016-2070-9 ↗
35. Optimization in wireless sensor network using soft computing10.1007/978-981-15-1480-7_74 ↗
36. Genetic Algorithms and Machine Learning10.1023/A:1022602019183 ↗
37. Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Muller, U.A., Sackinger, E., and Simard, P. (1994, January 9–13). Comparison of classifier methods: A case study in handwritten digit recognition. Proceedings of the 12th IAPR International Conference on Pattern Recognition, Volume 3—Conference C: Signal Processing (Cat. No.94CH3440-5), Jerusalem, Israel.
38. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
39. ImageNet classification with deep convolutional neural networks
40. Simonyan, K., and Zisserman, A. (2015, January 7–9). Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015—Conference Track Proceedings, San Diego, CA, USA.
41. Millstein, F. (2018). Deep Learning with Keras, CreateSpace Independent Publishing Platform.
42. Howard, A., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
43. (2020, December 20). IEC 61850-5: Communication Networks and Systems in Substation. Available online: https://ci.nii.ac.jp/naid/10017256674/.
44. Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.
45. NVIDIA (2020, December 20). NVIDIA Jetson NANO. Available online: https://developer.nvidia.com/EMBEDDED/jetson-nano-developer-kit.
46. Abubakar, A., and Pranggono, B. (2017, January 6–8). Machine learning based intrusion detection system for software defined networks. Proceedings of the 2017 Seventh International Conference on Emerging Security Technologies (EST), Canterbury, UK.10.1109/EST.2017.8090413 ↗
47. Oo, M.M., Kamolphiwong, S., Kamolphiwong, T., and Vasupongayya, S. (2019). Advanced Support Vector Machine- (ASVM-) Based Detection for Distributed Denial of Service (DDoS) Attack on Software Defined Networking (SDN). J. Comput. Netw. Commun., 2019.10.1155/2019/8012568 ↗
48. A multi-criteria based software defined networking system Architecture for DDoS-attack mitigation
49. Wang, P., Chao, K., Lin, H., Lin, W., and Lo, C. (2016, January 4–6). An Efficient Flow Control Approach for SDN-Based Network Threat Detection and Migration Using Support Vector Machine. Proceedings of the 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE), Macau, China.10.1109/ICEBE.2016.020 ↗
50. Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8–10). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.10.1109/CISDA.2009.5356528 ↗
51. Phan, T.V., Bao, N.K., and Park, M. (2016, January 18–21). A Novel Hybrid Flow-Based Handler with DDoS Attacks in Software-Defined Networking. Proceedings of the 2016 International IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France.10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0069 ↗