A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
Automated cyber defence: A review
S Vyas, J Hannay, A Bolton, PP Burnap - arXiv preprint arXiv:2303.04926, 2023 - arxiv.org
Within recent times, cybercriminals have curated a variety of organised and resolute cyber
attacks within a range of cyber systems, leading to consequential ramifications to private and …
attacks within a range of cyber systems, leading to consequential ramifications to private and …
Intrusion prevention through optimal stopping
We study automated intrusion prevention using reinforcement learning. Following a novel
approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping …
approach, we formulate the problem of intrusion prevention as an (optimal) multiple stopping …
Comparative DQN-improved algorithms for stochastic games-based automated edge intelligence-enabled IoT malware spread-suppression strategies
Y Shen, C Shepherd, CM Ahmed… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Massive volumes of malware spread incidents continue to occur frequently across the
Internet of Things (IoT). Owing to its self-learning and adaptive capability, artificial …
Internet of Things (IoT). Owing to its self-learning and adaptive capability, artificial …
Digital twins for security automation
We present a novel emulation system for creating high-fidelity digital twins of IT
infrastructures. The digital twins replicate key functionality of the corresponding …
infrastructures. The digital twins replicate key functionality of the corresponding …
Learning near-optimal intrusion responses against dynamic attackers
We study automated intrusion response and formulate the interaction between an attacker
and a defender as an optimal stopping game where attack and defense strategies evolve …
and a defender as an optimal stopping game where attack and defense strategies evolve …
Learning security strategies through game play and optimal stopping
We study automated intrusion prevention using reinforcement learning. Following a novel
approach, we formulate the interaction between an attacker and a defender as an optimal …
approach, we formulate the interaction between an attacker and a defender as an optimal …
Nasimemu: Network attack simulator & emulator for training agents generalizing to novel scenarios
Current frameworks for training offensive penetration testing agents with deep reinforcement
learning struggle to produce agents that perform well in real-world scenarios, due to the …
learning struggle to produce agents that perform well in real-world scenarios, due to the …
Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search
The CAGE-2 challenge is considered a standard benchmark to compare methods for
autonomous cyber defense. Current state-of-the-art methods evaluated against this …
autonomous cyber defense. Current state-of-the-art methods evaluated against this …
Scalable learning of intrusion response through recursive decomposition
We study automated intrusion response for an IT infrastructure and formulate the interaction
between an attacker and a defender as a partially observed stochastic game. To solve the …
between an attacker and a defender as a partially observed stochastic game. To solve the …