Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non-trivial problem because of the range and complexity of actions that a human expert may attempt. The authors focus their attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and analyse how model-free reinforcement learning algorithms may help solving them. In modelling these capture the flag competitions as reinforcement learning problems the authors highlight the specific challenges that characterize penetration testing. The authors show how this challenge may be eased by relying on different forms of prior knowledge that may be provided to the agent. Since complexity scales exponentially as soon as the set of states and actions for the reinforcement learning agent is extended, the need to restrict the exploration space by using techniques to inject a priori knowledge is highlighted, thus making it possible to achieve solutions more efficiently.
In this paper, the security of Advanced Encryption Standard-based authenticated encryption schemes, including AEGIS family, Tiaoxin-346, and Rocca by mixed integer linear programming tools is examined. Specifically, for the initialisation phase of AEGIS, Tiaoxin-346, and Rocca, the security against differential attacks and integral attacks is evaluated by estimating the lower bounds for the number of active S-boxes and utilising division property, respectively. In addition to the estimations of initialisation phases, the security of the encryption phases of AEGIS, Tiaoxin-346, and Rocca against distinguishing attacks on keystream is evaluated by exploiting integral properties. As a result, the authors show that the initialisation phases of AEGIS-128/128L/256, Tiaoxin-346, and Rocca are secure against differential attacks after 4/3/6, 5, and 6 rounds, respectively. Regarding integral attacks, the distinguisher is found on 6/6/7, 15, and 7 rounds in the initialisation phases of AEGIS-128/128L/256, Tiaoxin-346, and Rocca, respectively. Additionally, the integral distinguisher is presented on 2/2/4, 4, and 4 rounds in the encryption phases of AEGIS-128/128L/256, Tiaoxin-346, and Rocca, respectively. As far as it is known, this study’s results are the first distinguishing attacks on the keystream on AEGIS, Tiaoxin-346, and Rocca without relying on weak keys.
In 2018, Abbasinezhad-Mood and Nikooghadam (IEEE Transaction on Smart Grid, pp 6194–6205, 9(6), 2018) proposed an ultra-lightweight secure scheme for neighbourhood area network (