This paper proposes SCABoosting, a novel boosting framework for deep learning-based Side-Channel Analysis (SCA) that significantly enhances key recovery efficiency while maintaining computational lightweightness. Our method integrates multiple randomly generated CNN classifiers through a sequential boosting strategy, achieving superior performance with only 26 M parameters compared to 68 M parameters required by state-of-the-art CNNbest models. Extensive evaluation on three standard datasets demonstrates compelling results: on ASCAD dataset, our approach reduces guessing entropy (GE) to 102 (vs 204 for CNNbest) and achieves 92
This paper presents a ground-up approach to attack a novel single-trace side-channel vulnerability in FALCON, a lattice-based post-quantum digital signature protocol recently approved for standardization by NIST. We target the discrete Gaussian sampling operation within FALCON’s key generation scheme. Notably, negating the results of a 63-bit right-shift operation on 64-bit secret values leaks critical information about the assignment of ‘-1’ versus ‘0’ to intermediate coefficients during sampling. These information makes it feasible to extract the full secret key. We demonstrate a ground-up approach to the attack on an ARM Cortex-M4 microcontroller executing both the reference and optimized source code implementations from FALCON’s NIST round 3 software package. Although single-trace attacks generally require a profiling phase involving a substantial number of traces, our experimental results demonstrate that reliable power profiles can be constructed using as few as 10 profiling traces. We further quantify the attacker’s success rate using a univariate Gaussian template model, providing generalizable guarantees. Statistical analysis reveals a projected per-coefficient success rate of 99.9999999478
Anonymity infrastructures such as mix networks, anonymous storage, and privacy-preserving replication rely on public re-randomization: any party holding only public information can transform a ciphertext into a fresh-looking encryption of the same plaintext, hiding the linkage between the two. Classical ElGamal-based solutions are broken by quantum adversaries, while existing lattice-based alternatives carry very large ciphertexts with unanalyzed noise growth, rely on heavyweight homomorphic-encryption stacks with approximate (rounded) decryption, or lack a precise analysis of how many re-randomizations are safe. We address this gap with a practical Ring Learning with Errors (Ring-LWE) public-key encryption scheme supporting public re-randomization without ciphertext growth. Our construction is Lyubashevsky–Peikert–Regev/Fan–Vercauteren (LPR/BFV)-style encryption over R=ℤ[x]/(x^n+1) with n=4096 , engineered around a two-limb Chinese Remainder Theorem (CRT) modulus q=t· q_2 with 32-bit primes. Embedding plaintext as Δ M = q_2 M makes the message vanish modulo q_2 , so the q_2 -limb carries only the decryption noise, enabling exact message recovery without rounding. We prove correctness with explicit decryption-failure bounds that remain valid under repeated re-randomization, via an aggregation lemma showing that arbitrarily many re-randomizations affect decryption only through a single aggregated randomness triple. We also prove that two-limb ciphertexts are pseudorandom (indistinguishable from uniform, IND) under Decision Ring-LWE over the combined modulusq=tq_2; security against chosen-plaintext attack (IND-CPA) and re-randomization unlinkability follow. A constant-time Rust implementation encrypts in 0.80 ms, re-randomizes in 0.51 ms, and decrypts in 0.21 ms per 64 KiB ciphertext carrying 15.5 KiB of payload on a fixed-frequency 3.8 GHz CPU—on par with a modulus-matched Microsoft SEAL baseline—and passes timing-leakage tests. Empirical noise simulations validate the analysis.
Physical Unclonable Functions (PUFs) are emerging security primitives designed to enhance the security of physical devices and overcome the challenges posed by conventional cryptographic algorithms. PUFs utilize logical operations to create a unique identity for each device based on the processing hardware’s unpredictable and non-reproducible physical characteristics. This generated key serves as device fingerprint providing a high level of security against cloning and counterfeiting. The dependence of PUFs on logical operation comes at the cost of exploiting the underlying hardware and contributes to the power consumption as well. This work addresses to reduce the resource consumption of delay-based Arbiter PUF(APUF) with help of Reversible Logic (RL) and its novel PUF design. RL is a computational method where each output state precisely determines its input, thereby reducing information loss and energy dissipation. This paper proposes a novel RL-based APUF design that generates 8/16/32 and 64-bit keys. The key generated is sensitive to the user input but not controlled by the user to maintain randomness. The research on PUFs shows that the best values of uniqueness, uniformity, and entropy metrics achieved across 8-bit/16-bit/32-bit and 64-bit implementations at 100
The rapid proliferation of Internet of Things (IoT) and Artificial Intelligence (AI) applications has escalated the prominence of Hardware Trojans (HTs) as a profoundly menacing threat to Integrated Circuits (ICs). Recent research has harnessed the power of Machine Learning (ML) and Deep Learning (DL) models for HT detection across diverse IC phases. However, the efficacy of these models in HT detection is hampered by three critical challenges: the absence of standardized large datasets prevalent in other domains, the pervasive class imbalance issue, and non-interpretable predictions. These challenges diminished the ability of ML/DL models to achieve the desired accuracy in HT detection. Further, supervised learning entails laborious and error-prone manual labeling of training samples, while unsupervised learning underperforms due to the unavailability of labeled samples. Moreover, the hidden nature of ML models also poses a limitation to understanding the predictions made by them. Addressing these issues, this paper proposes an Explainable Semi-Supervised Self-Training approach for HT detection in the pre-silicon IC phase. The proposed approach addresses all three challenges present in both supervised and unsupervised approaches. The proposed self-training SSL-based approach effectively handles the scarcity of labeled training data by leveraging both labeled and unlabeled samples. Further, a hybrid ADASYN-ENN sampling strategy is proposed, which integrates oversampling and undersampling to tackle severe class imbalance arising due to a small number of Trojan-inserted samples. Finally, Local Interpretable Model-agnostic Explanations (LIME) is utilized to interpret model predictions, i.e., which influential features are driving the ML model decisions. Experimental results show that the proposed technique accurately detects HT from TrustHub benchmarks of small and large size with 98
