The advent of digital technologies has revolutionized traditional power distribution networks, transforming them into smart grids that are more reliable, efficient, and sustainable. Despite these advancements, electricity theft remains a significant threat to the effective operation of large electrical networks. To address this issue, we propose EnThM, a lightweight and communication-efficient scheme for real-time mitigation of power theft in smart grid systems. Our approach uses the hierarchical structure of the smart grid infrastructure to verify the authenticity of the metering data at multiple levels of the power distribution network. Our work focuses primarily on issues related to cryptographic security. The verification process involves statistically modeling the cumulative averages of the power usage data and applying rule-based checks on the aggregated power consumption at each level, while accounting for seasonal and daily consumption variations. The proposed method has been tested on benchmark consumption data, yielding high accuracy, efficient implementation, and real-time applicability.

The Advanced Encryption Standard (AES) is a widely recognized, extensively utilized, and thoroughly studied industry-standard symmetric key block cipher. Consequently, cryptanalysis of AES is a highly relevant and significant task for cryptography researchers. In the field of cryptanalysis, the Square attack is a powerful key recovery technique used against symmetric ciphers. This study aims to provide two efficient implementations of the partial sum attack (PSA), a variant of the square attack, on reduced rounds of AES. The goal is to demonstrate the effectiveness of these implementations in terms of reduced time complexity for full key recovery. We demonstrate two efficient implementations of PSA on reduced rounds of AES. The first implementation is a GPU-based attack applied to five and six rounds of AES, achieving full key recovery in 0.8s and 7.58 days, respectively. The second implementation is cluster-based, applied to six rounds of AES, with an elapsed time of 2.5 days for full key recovery. Both implementations leverage parallelization to enhance performance, showing a significant reduction in attack time proportional to the number of working threads. The findings confirm that PSA effectively reduces the time complexity for full key recovery in reduced rounds of AES, demonstrating the practicality and efficiency of these implementations.

This paper proposes a new block cipher called HARPOCRATES, which is different from traditional SPN, Feistel, or ARX designs. The new design structure that we use is called the substitution convolution network. The novelty of the approach lies in that the substitution function does not use fixed S-boxes. Instead, it uses a key-driven lookup table storing a permutation of all 8-bit values. If the lookup table is sufficiently randomly shuffled, the round sub-operations achieve good confusion and diffusion to the cipher. While designing the cipher, the security, cost, and performances are balanced, keeping the requirements of encryption of data-at-rest in mind. The round sub-operations are massively parallelizable and designed such that a single active bit may make the entire state (an $8 \times 16$8x16 binary matrix) active in one round. We analyze the security of the cipher against linear, differential, and impossible differential cryptanalysis. The cipher's resistance against many other attacks like algebraic attacks, structural attacks, and weak keys are also shown. We implemented the cipher in software and hardware; found that the software implementation of the cipher results in better throughput than many well-known ciphers. Although HARPOCRATES is appropriate for the encryption of data-at-rest, it is also well-suited in data-in-transit environments.

The medical implant communication system is a critical cyber-physical system with significant security concerns due to its crucial role in patient health and safety. Implant devices integrate wireless technology, exposing them to potential cyber threats. This paper presents a secure data transmission protocol incorporating cryptographic primitives with a lightweight authentication scheme. This is specifically designed for secure communication in an Implantable Cardioverter Defibrillators (ICD) environment. We present a set of practical attacks and their countermeasures in the ICD environment using the proposed authentication scheme.

A smart grid is a widespread electrical infrastructure equipped with diverse computing and communication resources that enable power transmission across different suppliers and consumers. While such connectivity allows balanced power distribution and smart monitoring, it introduces several vulnerabilities that expose the network to cyber threats. An unauthorized access to power grid poses a critical threat that may fully jeopardize the system causing massive power outage. Most of the existing schemes are designed to safeguard the security of customers’ data and rely on key-based authentication, verified only once in the beginning. However, cyber-attacks arriving later may cleverly occlude a user’s identity and redirect the power flow stealthily to the interceptor thus depriving both the genuine consumer and the supplier. In this work, we propose, for the first time, a secured smart-grid authentication scheme (SECRET) that is capable of providing “key-less” as well as “periodic” authentication in real-time. Our scheme utilizes time-series data from smart meters and produces a “tag” to be verified by the supply nodes. We use a technique based on Cellular Automata (CA) to support lightweight implementation of the authentication scheme. The dynamic evolution of CA provides randomness that is utilized to enhance security measures. Not only it obviates the complexity of key generation, but also reduces the cost and overhead for sharing keys. The proposed scheme has been analyzed against accessibility and various network attacks. Hardware implementation of the CA-based scheme on the Xilinx FPGA platform demonstrates that a small chip attached to smart meters can achieve faster and cost-effective authentication of users in smart-grid infrastructures.

This paper presents a systematic exploration of the evolution of algorithms for S-Box modeling, tracing their progression from foundational techniques, such as the greedy algorithm by Sun et al., to state-of-the-art methods like the SuperBall approach by Li and Sun. Through a detailed comparative analysis, we examine these methodologies, highlight their key differences, and provide insightful observations. Early approaches, such as the greedy algorithm, relied on mathematical tools like SageMath, while more recent advancements leverage Mixed Integer Linear Programming (MILP) to model S-Box inequalities more effectively. Building on these developments, we introduce novel algorithms that utilize MILP techniques to enhance both the efficiency and accuracy of S-Box inequality generation. Our first algorithm, direct inequality generation, constructs the final set of inequalities directly using MILP models. The second, greedy generation and reduction, generates an expanded inequality set, which is then refined using the Sasaki and Todo reduction algorithm to obtain the final result. Both approaches employ the method of undetermined coefficients, similar to the SuperBall approach. Our comparative analysis demonstrates that the proposed algorithms not only meet cryptographic standards but also deliver comparable results to existing models. We further enhance the greedy algorithm proposed by Sun et al. through key modifications, while a novel iterative inequality augmentation technique based on convex hull methods improves the results introduced by Boura and Coggia. Additionally, we identify a lower bound in the SuperBall approach, enabling faster convergence to optimal results. We also suggest methods for deriving a minimal set of inequalities associated with the LAT, BCT, and DPT, as well as the DDT for any given S-box. These methods are useful for enhancing cryptanalysis techniques such as linear cryptanalysis, boomerang attacks, and division property-based attacks on ciphers.

The transformation of traditional electrical grids into smart grids has significantly streamlined electricity distribution and monitoring. However, like any modern cyber-physical system, smart grids are susceptible to various cyberattacks. Among these, one of the most common and disruptive are signaling Denial-of-Service (DoS)-attacks, where attackers flood the cellular traffic within the Advanced Metering Infrastructure (AMI) with redundant requests. In this paper, we propose DoSAGE, a method for detecting signaling DoS-attacks within the AMI that enables both keyless and periodic threat detection in real-time. To detect potential vulnerabilities our scheme leverages information embedded within smart meter datagrams. For identifying DoS attacks, we design a cellular automata (CA)-based key less authenticated encryption scheme together with a three-way handshaking protocol, which allows a lightweight implementation with minimal additional overhead. Our method exhibits improved throughput and faster computation compared to the existing standard authenticated encryption schemes, demonstrating its effectiveness in enhancing the security of the smart grid environment.

This research introduces a new deep learning-based technique that significantly enhances the efficiency and accuracy of deep learning based cryptographic distinguishers. By employing a sequence detection approach, we have achieved significant improvements in finding distinguishers for analyzing ciphertext sequences. Two innovative models, an LSTM-based Encoder Classifier (LbEC) and a Transformer based Encoder-only Classifier (TbEC), are proposed. The dataset has been transformed into a list of vector embeddings of the individual sequence data, which is used to train the models. Experimental results demonstrate that this approach has not only achieved results comparable to the existing related works but also outperformed some of the existing schemes. Thereby, distinguishers for HIGHT covering 16 rounds, PRESENT covering 12 rounds, LEA covering 13 rounds, SPARX covering 6 rounds and Piccolo-80 covering 9 rounds have been accomplished, which shows notable improvement over the existing best results.

Phishing attacks exploit human vulnerabilities to breach security defenses by either stealing a victim’s credentials or by luring a victim to give away their security details. These attacks have emerged as a primary threat, making the networked world increasingly vulnerable. The evolution of phishing from rudimentary schemes to highly targeted and sophisticated tactics can lead to operational disruptions and even catastrophic failures. In response to this threat, the paper proposes a comprehensive framework for mitigating phishing attacks on email. This includes email filtering and multi-factor authentication wherein the user’s credentials are safeguarded using a custom lightweight hash function. The security of the proposed hash function has also been analysed.

Deep learning-based cryptanalysis is one of the new ideas that has emerged in recent years. By using deep learning-based methodologies, researchers are currently modeling conventional differential cryptanalysis. We use deep learning models, CNN, LSTM, LGBM, DenseNet, and LeNet, to generate deep learning-based differential distinguishers that can reveal weaknesses in the encryption schemes. We focus on National Institute of Standards and Technology (NIST) standard lightweight authenticated encryption (AE), such as TGIF-TBC and LIMDOLEN-128, along with permutation methods like SPARKLE-256, ACE-128, and SPONGENT-160. Our research has led us to find that deep learning techniques can generate differential distinguishers for these cryptographic elements. Specifically, we were able to develop differential distinguishers for the SPONGENT-160 permutation up to 7 rounds, for the SPARKLE-256 permutation up to 3 rounds, for the ACE-128 permutation up to 4 rounds, for the TGIF-TBC AE up to 5 rounds, and for the LIMDOLEN-128 AE up to 14 rounds. Notably, this marks the first instance of a deep learning-based differential classifier for the authenticated encryptions TGIF-TBC, LIMDOLEN-128, as well as the permutations SPARKLE-256, ACE-128, and SPONGENT-160, based on our current understanding. When considering various models, both DenseNet and CNN demonstrate strong performance. However, it is the LightGBM (LGBM) model that truly shines as the optimal choice, primarily attributed to its minimal parameter requirements and rapid response speed.

This work explores the successful integration of machine learning and cryptography, wherein distinguishers have been designed with machine learning techniques. The primary focus is how effectively the deep learning models can be leveraged to uncover intricate patterns in data. Three Neural Distinguishers have been proposed using LightGBM, CNN, and LSTM algorithms to analyze Lightweight Block Ciphers. These models have a simpler architecture than most other related models previously used in the literature. The proposed methods have been applied to the ciphers, LEA, PRESENT, Piccolo-80, and MIDORI. This has led us to achieve an improved distinguisher for LEA covering 14 rounds and PRESENT covering 16 rounds. For the first time, new distinguishers have been achieved for Piccolo-80, covering 9 rounds. A new idea of employing deep learning-aided related key and weak key attacks has been used to obtain a distinguisher covering full rounds of MIDORI.

Symmetric key cryptography stands as a fundamental cornerstone in ensuring security within contemporary electronic communication frameworks. The cryptanalysis of classical symmetric key ciphers involves traditional methods and techniques aimed at breaking or analyzing these cryptographic systems. In the evaluation of new ciphers, the resistance against linear and differential cryptanalysis is commonly a key design criterion. The wide trail design technique for block ciphers facilitates the demonstration of security against linear and differential cryptanalysis. Assessing the scheme's security against differential attacks often involves determining the minimum number of active SBoxes for all rounds of a cipher. The propagation characteristics of a cryptographic component, such as an SBox, can be expressed using Boolean functions. Mixed Integer Linear Programming (MILP) proves to be a valuable technique for solving Boolean functions. We formulate a set of inequalities to model a Boolean function, which is subsequently solved by an MILP solver. To efficiently model a Boolean function and select a minimal set of inequalities, two key challenges must be addressed. We propose algorithms to address the second challenge, aiming to find more optimized linear and non-linear components. Our approaches are applied to modeling SBoxes (up to six bits) and EXOR operations with any number of inputs. Additionally, we introduce an MILP-based automatic tool for exploring differential and impossible differential propagations within a cipher. The tool is successfully applied to five lightweight block ciphers: Lilliput, GIFT64, SKINNY64, Klein, and MIBS.

The core strength of a block cipher lies in its nonlinear substitution operation, known as the SBox. However, the presence of static parameters within the SBox can potentially lead to the exposure of certain information in the ciphertext. In this paper, we present a methodology for the construction of key-dependent SBoxes. These key-dependent SBoxes exhibit resistance against linear and differential crypt-analysis. In this paper, we generate key-dependent 7-bit and 9-bit SBoxes for the KASUMI block cipher. Furthermore, we demonstrate their resistance against known differential fault attacks

Implantable Medical Devices (IMDs) have revolutionized the treatment of critical diseases. However, the increasing reliance on these life-saving devices' wireless functionality has made them vulnerable to cyber-attacks. Implantable Cardioverter Defibrillator (ICD) has emerged as a leading IMD owing to the worldwide surge in cardiac diseases. Given the resource-constrained ICD environment, there's a pressing need to develop tailored security measures for protection, moving beyond traditional approaches. In this paper, we present resource depletion attack scenarios in an ICD environment where attackers can exploit the ICD's wireless connectivity function. We propose some comprehensive approaches to mitigate such attacks, offering a significant step forward in safeguarding the well-being of patients. This research contributes to the ongoing efforts to secure the Internet of Medical Things (IoMT) ecosystem and underscores the importance of cyber-security in modern healthcare.

Non-malleable codes (NMC) are used as a relaxation of error correction and error detection codes to guarantee strong privacy where correctness is not the main concern. Usually, a coding scheme is said to be non-malleable with respect to a class of tampering function if any tampering with the codeword, the underlying function changes the codeword to a completely unrelated one, i.e., or same, in case of unsuccessful tampering. The real life application of such codeword is to provide security against leakage and tampering attacks on the memory, which is also called active physical attacks or hardware attacks. Standard version of non-malleable codes are used to protect highly sensitive data (i.e., secret key of any cryptographic scheme) on private memory of the device. In literature, leakage resilient authenticated encryptions (AE) are used to design such codeword. We show a generic framework to design leakage resilient authenticated encryption and prove it non-malleable with respect to one-time tampering attack. The instantiation of such codeword is based on leakage resilient IV-based encryption scheme along with leakage resilient CBC-MAC and 1-more weakly extractable leakage-resilient hash function (wECRH). When the tampering experiment of our strong NMC returns , the security is reduced to the security of authenticated encryption and 1-more weakly extractable leakage-resilient hash function.

In digital media forensics, detecting manipulated facial images is a crucial topic. With the advancement of synthetic face generation technologies, different types of face morphing have raised serious concerns in social media implications. Hence, we have worked on detecting the manipulated face image thoroughly. Despite just using a multi-task learning approach, we propose a facial image manipulation detection scheme using cellular automata (CA) and transfer learning (TL) as feature extractors to enhance the classifier model’s feature maps. The extracted features point out the informative regions to refine the binary classification power. We have collected datasets that contain diverse facial forgeries. We have also built our dataset, keeping every possible morphing way in mind. We have analyzed the datasets thoroughly and demonstrated that using a combination of CA and TL improves manipulated fake detection on different morphing techniques.

Cryptographic circuits contain various confidential information and are susceptible to fraudulent manipulations, commonly called attacks, performed by ill-intentioned person. The primary goal of the attacker is to retrieve the sensitive information when the device is executing some task. One of the most efficient attack is Differential Fault Analysis attack that exploits the physical or implementation weakness of the device by injecting faults, for example with a laser beam, overheating, etc. AES is vulnerable against Differential Fault Analysis attack. The adversary can form a system of linear equations with a pair of ciphertexts to break AES cryptosystem. In the literature, it is shown that AES key can be recovered using this kind of fault attack with an exhaustive search of 2^32 , which is further improved to 2^8 . Using a 32 cores processor with 2.1 GHz clock speed each, the AES-128 key can be retrieved within 17.5 s. Ghosal et al. as reported by Ghosal (in: Yuan, Bai, Alcaraz, Majumdar (eds) International Conference on Network and System Security, Springer, Cham, 2022) propose an extra diffusion layer to AES cryptosystem, MixColumn - Plus , to strengthen the security of AES against such attack. With the addition of an extra diffusion layer, an attacker has to search exhaustively 2^84 keys. In this work, we propose another matrix for MixColumn - Plus and further, we implement MixColumn - Plus layer with both matrices in hardware platform and compare the delay, LUT, gate count, frequency and execution time with original AES. The complexity of the byte fault attack is improved to 2^116 with the proposed matrix. The proposed hardware implementation of AES with MixColumn-Plus can be called as DFA attack-tolerant module.