Machine Learning Based Resilience Testing of an Address Randomization Cyber Defense


Mani G., Haliem M., Bhargava B., Manickam I., Kochpatcharin K., Kim M., ...Daha Fazla

IEEE Transactions on Dependable and Secure Computing, cilt.20, sa.6, ss.4853-4867, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 20 Sayı: 6
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1109/tdsc.2023.3234561
  • Dergi Adı: IEEE Transactions on Dependable and Secure Computing
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.4853-4867
  • Anahtar Kelimeler: cyber resilience, Cyber security, cyber-attacks, long-short-term-memory, moving target defense, random forest, supervised learning, unsupervised learning
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Moving target defenses (MTDs) are widely used as an active defense strategy for thwarting cyberattacks on cyber-physical systems by increasing diversity of software and network paths. Recently, machine Learning (ML) and deep Learning (DL) models have been demonstrated to defeat some of the cyber defenses by learning attack detection patterns and defense strategies. It raises concerns about the susceptibility of MTD to ML and DL methods. In this article, we analyze the effectiveness of ML and DL models when it comes to deciphering MTD methods and ultimately evade MTD-based protections in real-time systems. Specifically, we consider a MTD algorithm that periodically randomizes address assignments within the MIL-STD-1553 protocol - a military standard serial data bus. Two ML and DL-based tasks are performed on MIL-STD-1553 protocol to measure the effectiveness of the learning models in deciphering the MTD algorithm: 1) determining whether there is an address assignments change i.e., whether the given system employs a MTD protocol and if it does 2) predicting the future address assignments. The supervised learning models (random forest and k-nearest neighbors) effectively detected the address assignment changes and classified whether the given system is equipped with a specified MTD protocol. On the other hand, the unsupervised learning model (K-means) was significantly less effective. The DL model (long short-term memory) was able to predict the future addresses with varied effectiveness based on MTD algorithm's settings.