We analyzed two non-repudiation protocols and found some new attacks on the fairness and termination property of these protocols. Our attacks are enabled by several inherent design weaknesses, which also apply to other non-repudiation protocols. To prevent these attacks, we propose generic countermeasures that considerably strengthen the design and implementation of non-repudiation protocols. The application of these countermeasures is finally shown by our construction of a new fair non-repudiation protocol

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Abstract. The Trusted Platform Module TPM is a basic but nevertheless very complex security component that can provide the foundations and the root of security for a variety of applications. In contrast to the TPM, other basic security mechanisms like cryptographic algorithms or security protocols have frequently been subject to thorough security analysis and formal verification. This paper presents a first methodic security analysis of a large part of the TPM specification. A formal automata model based on asynchronous product automata APA and a finite state verification tool SHVT are used to emulate a TPM within an executable model. On this basis four different generic scenarios were analysed with respect to security and practicability: secure boot, secure storage, remote attestation and data migration. A variety of security problems and inconsistencies was found. Subsequently, the TPM specification was adapted to overcome the problems identified. In this paper, the analysis of the remote attestation scenario and some of the problems found are explained in more detail.

Transfer learning provides an effective solution for feasibly and fast customize accurate Student models, by transferring the learned knowledge of pre-trained Teacher models over large datasets via fine-tuning. Many pre-trained Teacher models used in transfer learning are publicly available and maintained by public platforms, increasing their vulnerability to backdoor attacks. In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: pruning-based, retraining-based and input pre-processing-based defenses. Specifically, (A) ranking-based selection mechanism to speed up the backdoor trigger generation and perturbation process while defeating pruning-based and/or retraining-based defenses. (B) autoencoder-powered trigger generation is proposed to produce a robust trigger that can defeat the input pre-processing-based defense, while guaranteeing that selected neuron(s) can be significantly activated. (C) defense-aware retraining to generate the manipulated model using reverse-engineered model inputs. We conduct an in-depth study on the backdoor attacks in building and operating both image and time series data transfer learning systems. We launch effective misclassification attacks on Student models over real-world images, brain Magnetic Resonance Imaging (MRI) data and Electrocardiography (ECG) learning systems. The experiments reveal that our enhanced attack can maintain the 98.4% and 97.2% classification accuracy as the genuine model on clean image and time series inputs respectively while improving 27.9% − 100% and 27.1% − 56.1% attack success rate on trojaned image and time series inputs respectively in the presence of pruning-based and/or retraining-based defenses.

Abstract.Historically, various different notions of trust can be found, each addressing particular aspects of ICT systems, e.g. trust in electronic commerce systems based on reputation and recommendation, or trust in public key infrastructures. While these notions support the understanding of trust establishment and degrees of trustworthiness in their respective application domains, they are insufficient for the more general notion of trust needed when reasoning about security in ICT systems. In this paper we present a formal definition of trust to be able to exactly express trust requirements from the view of different entities involved in the system and to support formal reasoning such that security requirements, security and trust mechanisms and underlying trust assumptions can be formally linked and made explicit. Integrated in our Security Modeling Framework this formal definition of trust can support security engineering processes and formal validation and verification by enabling reasoning about security properties w.r.t. trust.