Sammendrag
Cybersecurity with machine learning has received widespread attention in education, research, and innovation in both the private and public sectors. Unfortunately, while essential for strong cyber security, offensive cyber operations with machine learning have seen significantly less innovation, at least in open academic literature. This thesis's contribution to the field of offensive cyber operations with machine learning can naturally be divided into the following: 1. Algorithmic cryptanalysis with machine learning 2. SQL injection with machine learning The historical cipher Enigma's plugboard is shown to be susceptible to an attack powered by the machine learning technique Genetic Algorithms, being broken far faster than any earlier attack. Modern ciphers are naturally more robust than historical ciphers. The cryptographic algorithm ASCON is still secure, but the novel machine learning technique, The Phantom Gradient Attack, is shown to attack many of its subfunctions successfully. OWASP's top 10 had SQL injections as the number one web vulnerability in 2017; in 2021, it was number three. This thesis highlights the possibility of automated SQL injection exploitation and identification with reinforcement learning for accelerated penetration testing. The reinforcement learning agent can exploit all 5 SQL injection archetypes, distinguish between them, and determine whether or not the website is vulnerable.
Artikkelliste
Paper I. Å. Å. Sommervoll and A. Jøsang “Machine Learning for Offensive Cyber Operations”. In: The NISK 2021 Proceedings (NISK Norsk informasjonssikkerhetskonferanse), vol. 8, Issue. 3, (Jan 2022). The article is included in the thesis. |
Paper II. Å. Å. Sommervoll and L. Nilsen “Genetic algorithm attack on Enigma’s plugboard”. In: Cryptologia. Vol. 45, Issue. 3, (Mar 2020), pp. 194–226. DOI: 10.1080/01611194.2020.1721617. The article is included in the thesis. Also available at: https://doi.org/10.1080/01611194.2020.1721617 |
Paper III. Å. Å. Sommervoll “Dreaming of keys: Introducing the phantom gradient attack”. In: Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021). Vol. 7, Paper nr. 90, (Feb 2021), pp. 619–627. DOI: 10.5220/0010317806190627. The article is included in the thesis. Also available at: https://doi.org/10.5220/0010317806190627 |
Paper IV. Å. Å. Sommervoll “The Phantom Gradient Attack: A Study of Replacement Functions for the XOR Function”. In: QShine 2021: Quality, Reliability, Security and Robustness in Heterogeneous Systems proceedings. Vol. 402, (Nov 2021), pp. 228–238. DOI: 10.1007/978-3-030-91424-0_14. The article is included in the thesis. Also available at: https://doi.org/10.1007/978-3-030-91424-0_14 |
Paper V. L. Erdődi, Å. Å. Sommervoll and F. M. Zennaro “Simulating SQL injection vulnerability exploitation using Q-learning reinforcement learning agents”. In: Journal of Information Security and Applications. Vol. 61, (September 2021), DOI: 10.1016/j.jisa.2021.102903. The article is included in the thesis. Also available at: https://doi.org/10.1016/j.jisa.2021.102903 |
Paper VI. L. Erdődi, Å. Å. Sommervoll and F. M. Zennaro “Simulating all Archetypes of SQL Injection Vulnerability Exploitation Using Reinforcement Learning Agents”. Submitted to International Journal of Information Security. To be published. The paper is not available in DUO awaiting publishing. |