The globalization of semiconductor manufacturing has increased vulnerabilities such as hardware Trojan insertion, overbuilding, reverse engineering, and intellectual property piracy. Logic locking has emerged as an effective countermeasure to protect integrated circuits using secret key activation. The Advanced Encryption Standard (AES) is widely deployed in Internet of Things (IoT) systems and therefore requires robust hardware-level protection. This work presents an architecture-level integration of Random Logic Locking and an Anti-SAT block within an AES encryption core implemented on an Artix-7 FPGA. Unlike prior studies that primarily evaluate Anti-SAT on benchmark circuits, the proposed approach embeds the locking framework directly into a complete cryptographic datapath and analyzes both hardware overhead and security characteristics. The design is evaluated in terms of area, power, throughput, Hamming distance corruptibility, and Satisfiability Checking(SAT) attack resilience. Experimental results demonstrate approximately 50\% output corruption under incorrect keys and incorrect key convergence during SAT attack attempts across multiple key sizes. The results indicate that the proposed integration enhances SAT attack resistance while maintaining acceptable hardware overhead, making it suitable for secure IoT hardware deployments.
We introduce LadderPrime, an exception-free scalar-point multiplication algorithm, which works on the Kummer line of an elliptic curve given by the equation B*y^2=x^3+A*x^2+ax+b. LadderPrime operates only on two coordinates and computes the correct output for all input points, all scalars, and all elliptic curves of characteristic > 2. This is achieved by an alternative set of differential-addition formulas which can handle not only generic points but also the problematic point (0 : Z) for Montgomery ladder. The main structure of LadderPrime is analogous to the scalar-point multiplication in Bernstein’s X25519 Diffie-Hellman key exchange (DH) specification. Unlike, X25519 which uses the non-prime order (h = 8) elliptic Curve25519, LadderPrime is able to work with prime order (h = 1) (and non-prime order) elliptic curves. When used with a prime order elliptic curve, LadderPrime does not need the initial raising of base point to a prime order subgroup. In other words, LadderPrime eliminates the need for masking lower bits of the scalar. LadderPrime also eliminates the need for Hamburg’s "Decaf" (CRYPTO 2015) and later refined "Ristretto" methods. Essential cryptographic protocols such as DH and qDSA can be instantiated over LadderPrime.
This study introduces Causal Image Processing (CIP), a mechanism based conceptual framework designed to address fundamental weaknesses of contemporary deep learning systems in security critical visual intelligence. Unlike conventional convolutional networks, vision transformers, or diffusion models that rely on observational correlations, CIP models the image formation process through four independent causal mechanisms: content, domain conditions, sensor characteristics, and identity factors. These mechanisms are formalized within a directed acyclic graph that provides a structured representation separating the physical and semantic processes underlying image generation. CIP integrates three forms of inference within a unified architecture: predictive inference, interventional inference, and counterfactual inference. Mechanism faithful representations enforce invariance to sensor and domain variations, while counterfactual reasoning enables principled evaluation of identity consistency under hypothetical acquisition conditions. The framework also introduces learning principles based on mechanism fidelity, sparse intervention sensitivity, and causal invariance, and defines evaluation criteria centered on counterfactual consistency and tamper resistance. Overall, CIP offers a theoretical foundation for developing robust, explainable, and tamper resistant vision systems capable of operating reliably across heterogeneous sensors, environments, and acquisition conditions. The framework establishes a mechanism centered alternative to correlation driven deep learning pipelines and provides a pathway for next generation secure visual intelligence.
The design of lightweight yet secure block ciphers, particularly for resource-constrained platforms such as RFID tags, tiny sensors, or pocket-sized mobile devices, has been a central focus of symmetric encryption research for over two decades. Midori, introduced by Banik et al. at ASIACRYPT 2015, is one of the most cryptanalyzed lightweight block ciphers and comes in two variants: Midori-64 (64-bit state, 16 rounds) and Midori-128 (128-bit state, 20 rounds), both using a 128-bit key.In this work, we extend the security analysis of Midori-64 via truncated differential techniques, providing a clearer understanding of its resistance against such attacks. We construct two 6-round truncated differentials by precisely computing the propagation probabilities of selected truncated differences through the cipher’s matrix-multiplication layer and tracking their diffusion across rounds. We compute their exact probabilities and use them as distinguishers. By exploiting these two distinguishers, we mount two distinct 10-round truncated differential attacks on Midori-64, representing the first truncated differential attacks reported on the cipher. While the full-round design remains secure, one of our attacks ranks among the best known attacks on 10-round Midori-64.
This paper presents a comprehensive evaluation of phishing detection architectures, systematically transitioning from single-modality text classifiers to parameter-efficient hybrid multimodal systems. Using the MTLP dataset, a large-scale collection containing 100,000 legitimate and phishing webpage samples, we systematically evaluated multiple model configurations. The dataset includes Uniform Resource Locators (URLs), HyperText Markup Language (HTML) content, and WHOIS metadata. Each experiment targeted a specific research question, examining the impact of Parameter-Efficient Fine-Tuning (PEFT) using Low Rank Adaptation (LoRA), feature fusion strategies, and feature engineering on detection accuracy and efficiency. The optimal configuration (V5.2) integrated a language-specific Turkish BERT encoder with 14 domain-specific URL features, achieving an F1-score of 99.14% and outperforming text-only, image-based, and WHOIS-based baselines. Our experiments show that increasing architectural complexity (e.g., cross-attention) does not necessarily lead to better performance; instead, a balanced fusion of linguistic and structural cues yields optimal results. We also observed a clear trade-off between parameter count and accuracy, as lightweight models with only 1,538 trainable parameters achieved near state-of-the-art performance. Error analysis revealed that even advanced systems struggle with structurally ambiguous or content-sparse webpages. Overall, the findings highlight the potential of domain-adapted hybrid architectures as a robust, efficient, and explainable approach to phishing detection.
