In this paper, we propose SassCache, a secure skewed associative cache with keyed index mapping. For this purpose, we design a new two-layered, low-latency cryptographic construction with configurable output coverage based on state-of-the-art cryptographic primitives. Based on this construction, SassCache is the first secure randomized cache with secure spacing. Victim cache lines automatically hide in locations the attacker cannot reach after less than 1 access on average. Consequently, attackers cannot evict the cache line, no matter which and how many memory accesses they perform. Our security analysis shows that all existing techniques for eviction set construction fail, and state-of-the-art attacks only apply to 1 in 3 million addresses, where SassCache is still as secure as ScatterCache. Compared to standard caches, Sass Cache has a single-threaded performance penalty of 1.75 % on the last-level cache hit rate in the SPEC2017 benchmark, and an average decrease of 11.7 p.p. in hit rate for MiBench, GAP and Scimark for our high-security settings.

In this paper, we present CSI:Rowhammer, a principled hardware-software co-design Rowhammer mitigation with cryptographic security and integrity guarantees, that does not focus on any specific properties of Rowhammer. We design a new memory error detection mechanism based on a low-latency cryptographic MAC and an exception mechanism initiating a software-level correction routine. The exception handler uses a novel instruction-set extension for the error correction and resumes execution afterward. In contrast to regular ECC-DRAM that remains exploitable if more than 2 bits are flipped, CSI:Rowhammer maintains the security level of the cryptographic MAC. We evaluate CSI:Rowhammer in a gem5 proof-of-concept implementation. Under normal conditions, we see latency overheads below 0.75% and no memory overhead compared to off-the-shelf ECC-DRAM. While the average latency to correct a single bitflip is below 20 ns (compared to a range from a few nanoseconds to several milliseconds for state-of-the-art ECC memory), CSI:Rowhammer can detect any number of bitflips with overwhelming probability and correct at least 8 bitflips in practical time constraints.

The introduction of Statistical Ineffective Fault Attacks (SIFA) has led to a renewed interest in fault attacks. SIFA requires minimal knowledge of the concrete implementation and is effective even in the presence of common fault or power analysis countermeasures. However, further investigations reveal that undesired and frequent ineffective events, which we refer to as the noise phenomenon, are the bottleneck of SIFA that can considerably diminish its strength. This includes noise associated with the attack’s setup and caused by the countermeasures utilized in the implementation. This research aims to address this significant drawback. We present two novel statistical fault attack variants that are far more successful in dealing with these noisy conditions. The first variant is the Statistical Effective Fault Attack (SEFA), which exploits the non-uniform distribution of intermediate variables in circumstances when the induced faults are effective. The idea behind the second proposed method, dubbed Statistical Hybrid Fault Attacks (SHFA), is to take advantage of the biased distributions of both effective and ineffective cases simultaneously. Our experimental results in various case studies, including noise-free and noisy setups, back up our reasoning that SEFA surpasses SIFA in several instances and that SHFA outperforms both or is at least as efficient as the best of them. For example, in the case of a 4-bits random-AND fault injected into the AES with a 35% missed fault rate, utilizing SEFA reduces the number of needed ciphertexts by 50%. In the same case study, SHFA can yield 10% and 55% reductions compared to SEFA and SIFA.

Abstract Automata learning techniques automatically generate systemmodels fromtest observations. Typically, these techniques fall into two categories: passive and active. On the one hand, passive learning assumes no interaction with the system under learning and uses a predetermined training set, e.g., system logs. On the other hand, active learning techniques collect training data by actively querying the system under learning, allowing one to steer the discovery ofmeaningful information about the systemunder learning leading to effective learning strategies. A notable example of active learning technique for regular languages is Angluin’s L ∗ -algorithm. The L ∗ -algorithm describes the strategy of a student who learns the minimal deterministic finite automaton of an unknown regular language L by asking a succinct number of queries to a teacher who knows L . In this work, we study L ∗ -based learning of deterministic Markov decision processes, a class of Markov decision processes where an observation following an action uniquely determines a successor state. For this purpose, we first assume an ideal setting with a teacher who provides perfect information to the student. Then, we relax this assumption and present a novel learning algorithm that collects information by sampling execution traces of the system via testing. Experiments performed on an implementation of our sampling-based algorithm suggest that our method achieves better accuracy than state-of-the-art passive learning techniques using the same amount of test obser vations. In contrast to existing learning algorithms which assume a predefined number of states, our algorithm learns the complete model structure including the state space.

Abstract Authenticated encryption satisfies the basic need for authenticity and confidentiality in our information infrastructure. In this paper, we provide the specification of Ascon -128 and Ascon -128a. Both authenticated encryption algorithms provide efficient authenticated encryption on resource-constrained devices and on high-end CPUs. Furthermore, they have been selected as the “primary choice” for lightweight authenticated encryption in the final portfolio of the CAESAR competition. In addition, we specify the hash function Ascon-Hash , and the extendable output function Ascon-Xof . Moreover, we complement the specification by providing a detailed overview of existing cryptanalysis and implementation results.