Integrated Clinical Environment Security Analysis Using Reinforcement Learning

Abstract

Many communication standards have been proposed recently and more are being developed as a vision for dynamically composable and interoperable medical equipment. However, few have security systems that are sufficiently extensive or flexible to meet current and future safety requirements. This paper aims to analyze the cybersecurity of the Integrated Clinical Environment (ICE) through the investigation of its attack graph and the application of artificial intelligence techniques that can efficiently demonstrate the subsystems’ vulnerabilities. Attack graphs are widely used for assessing network security. On the other hand, they are typically too huge and sophisticated for security administrators to comprehend and evaluate. Therefore, this paper presents a Q-learning-based attack graph analysis approach in which an attack graph that is generated for the Integrated Clinical Environment system resembles the environment, and the agent is assumed to be the attacker. Q-learning can aid in determining the best route that the attacker can take in order to damage the system as much as possible with the least number of actions. Numeric values will be assigned to the attack graph to better determine the most vulnerable part of the system and suggest this analysis to be further utilized for bigger graphs.

Security Analysis of Cyber-Physical Systems Using Reinforcement Learning

“…Another traditional reinforcement approach was employed in our prior work [ 5 ] to determine the best route an attacker may follow to compromise an integrated clinical environment system, and the algorithm utilized was based on Q-learning. Both Q-learning and SARSA rely on tables to store value.…”

“…Both Q-learning and SARSA rely on tables to store value. In [ 5 ], one target node was the lone node in the attack graph, which had seven nodes. It was found that the Q-learning results showed the shortest path, independent of the cumulative reward, as opposed to the SARSA algorithm, which disregarded the shortest path, such as 1 → 3 → 4.…”

“…The security of CPS was studied through our earlier work, such as [ 3 , 4 , 5 ], where we sought to visualize the attack sequence within the attack graph that an attacker may carry out to compromise the system. Additionally, a variety of frameworks were employed in these studies, including the use of architecture analysis and design language for system modeling, matrices, and algebra to determine any assault that may occur, and Q-based reinforcement learning (RL) for establishing the worst damage an attacker can cause.…”

“…Furthermore, this approach can be useful in detecting the most susceptible section of the system, which can aid in the development of a more secure system. This strategy is compared with earlier work [ 5 ] based on Q-learning (off-policy) to discover the differences among various RL approaches.…”

Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions

“…Fortunately, the technology at our disposal offers cost-effective solutions to these problems. Machine learning (ML) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], artificial intelligence (AI) [16][17][18][19][20][21][22][23][24][25][26][27][28][29] and Big Data [30][31][32][33][34][35] can provide robust and innovative answers to long-standing problems.…”