With the increasing use of RFID tags, there is a need for specific protocols to communicate with these tags. Among these protocols, the ownership transfer stands out as it ensures the security and privacy of objects for the new owner after a change of ownership. Recently, a lightweight object ownership transfer protocol has been proposed for RFID networks. This protocol utilizes a lightweight linear function for security. The designers of the protocol claim that it is secure against known attacks while also being lightweight. In this paper, we identify vulnerabilities in the function used in this protocol and demonstrate that it is susceptible to the secret disclosure attack. We show that with at most 4 × L executions of the protocol (where L is the key length), one can obtain the necessary information from intercepted data to execute the attack and subsequently recover the shared keys used in the protocol.

As the intensity of global cybersecurity threats continues to rise, the need for training security professionals has gained greater significance. Educational programs, complemented by laboratories and the execution of cybersecurity exercises, play a fundamental role in enhancing both offensive and defensive capabilities. The execution of such exercises is particularly crucial in operational networks, where testing cyberattacks may not be feasible. Cyber ranges offer an appropriate platform for conducting these exercises. A primary challenge in cybersecurity education is aligning training programs with the diverse skill levels of learners. Adaptive learning, powered by artificial intelligence and recommendation systems, can provide an effective solution for delivering personalized instruction. This study focuses on the KYPO Cyber Range to examine the potential of substituting or augmenting the role of the instructor with an AI-based recommendation agent. The objective of this research is to minimize human intervention and improve the efficiency of the training process. To this end, data collected from the KYPO Cyber Range, developed by Masaryk University, has been utilized, and various machine learning models have been applied to automate and optimize the training process. The results of this research indicate that the integration of artificial intelligence can enhance the performance of educational systems and reduce evaluation time.