The ISC International Journal of Information Security

Abstract The growing popularity of the fabless manufacturing model and the resulting threats have increased the importance of Logic locking as a key-based method for intellectual property (IP) protection. Recently, machine learning (ML)-based attacks have broken most existing locks by exploiting structural traces or undoing optimizations that obfuscate them. A common limitation of these attacks, however, is their reliance on the correlation between the locked circuit structure and the correct key value. In this paper, we introduce structural fuzzing as a simple, nondeterministic, non-optimizing heuristic algorithm that can obfuscate the lock against learning-based attacks, preventing the attacker from predicting the key. We proceed to apply structural fuzzing to multiplexer-based logic locking and propose HyLock, a logic lock with improved resilience against learning-based attacks. In common benchmarks, when compared with a state of the art logic lock, there is on average a 17% decrease in the number of correctly predicted key bits.

Abstract A Chatbot is a smart software that responds to natural language input and attempts to hold a conversation in a way that simulates humans. Chatbots have the potential to save any individual’s time, hassle, and tedium by automating mundane tasks. The idea of this research is that to investigate how to help the user efficiently interact with the robot receptionist through an Intelligent Assistant dialogue. Chatbots are an effective way to improve services with their 24 /7 uptime, and their cost efficiency, and their multi-user quality. Despite the chatbots reduce human errors and give more answers that are accurate. Successful implementation of a chatbot requires correct analysis of the user’s query by the bot and ensures the correct response that should be given to the user. This research develops a chatbot for the Airports, which provides the visitors to the SWE chatbot Relevant information about the department. Throughout our extensive search since the very begin- ning of our project, we have been through multiple re- sources and endured a strenuous vetting process.

Abstract The use of NoSQL data and its storage in the Cloud is growing rapidly. Due to the accumulation of data in the Cloud, data security against untrusted service providers as well as external attackers becomes a more serious problem. Over the past few years, there are some efforts to secure the outsourcing of NoSQL data, especially column-based and document-based models. However, practical solutions for secure outsourcing of key-value databases have not been identified. This paper attempts to introduce SecureKV as a secure method for outsourcing key-value databases. This method employs a multi-Cloud storage scenario to preserve outsourced data confidentiality. Besides security issues, the proposed method supports executing major key-value queries directly on outsourced data. A prototype of the Redis database management system has
been implemented to show the efficiency and effectiveness of the proposed method. The results imply that, besides security issues, it is efficient and scalable enough in executing key-value-specific queries.

Abstract In a world full of many ideas turning to various kinds of products that need to be protected and here comes the importance of intellectual property rights. Intellectual property has many types however, our interest is in trademarks. The Madrid system is a system used by a group of countries that were in the Madrid level of the agreement so they authorize it and they that has the agreement with them to use but the problem with it that it is a text-based system because of that we proposed a reverse image engine and that is because the reverse search image is better than the text-based system. we have discussed all of the terms and terminology that we need in our project. Along with reviewing the famous reverse-image search engines and the first systems of trademark image retrieval (TIR) and some of the related papers. Introducing our project with all the system analysis phases.
The project approach is a reverse image search engine, it will be designed using a CBIR system with deep neural networks. This project will be implemented in the second semester of the 2020 year.

Abstract Today, passive RFID tags have many applications in various fields such as healthcare, transportation, asset management, and supply chain management. In some of these applications, a group of tags need to prove they are present in the same place at the same time. To solve this problem, many protocols have been proposed so far, and each of them has been able to solve some security and performance problems, but unfortunately, many of these protocols have security vulnerabilities or do not have the necessary performance to run on passive RFID tags. In this study, a secure and lightweight protocol for RFID tags grouping proof called LSGPP is proposed. In this protocol, the reader is an untrusted entity, in other words, the protocol is secure even if the reader is hijacked by an attacker. This study shows that the LSGPP protocol is secure against tracking, eavesdropping, replay, concurrency, impersonation, desynchronization, denial of service (DoS), proof forgery, message integrity, man-in-the-middle, secret disclosure, denial of proof (DoP), and unlinkability attacks, and supports anonymity and forward secrecy features. Also, in this study, the notion of RFID reader compromised attack is introduced, and it is shown that, unlike its predecessors, the LSGPP protocol is also secure against this attack. Also, using the Proverif tool, it is shown that the proposed protocol provides confidentiality and authentication features. The LSGPP protocol uses lightweight operations affordable for passive RFID tags and is shown to be compliant with the EPC C1G2 standard.

Abstract Voting is a fundamental mechanism used by many human societies, organizations and nations to make collective decisions. There has been a tremendous effort on making this mechanism fairer, error-free and secure. Electronic voting aims to be a solution to some deficiencies of existing paper-based voting systems. While there have been excellent technical and practical advances in e-voting, and some of them were great in defining the needs and musts of an ideal voting system, there are also severe critics of existing solutions mostly related to end-to-end verifiability and software independence. In this paper, we use blockchain and zero-knowledge proofs for a secure e-voting scheme that satisfies these requirements while preserving the privacy of the voters. We also evaluate
our scheme from security and performance aspects.

Abstract The diffusion layer plays an important role in a block cipher. Some block ciphers, such as ARIA, Camellia, and Skinny use binary matrices as diffusion layers which can be efficiently implemented in hardware and software. In this paper, the goal is to propose some new binary matrices with suitable values for the active S-boxes for R rounds. Firstly, some new $16 \times 16$ matrices are proposed whose software implementations are better than the corresponding one for the ARIA block cipher. Also, the values for the minimum active S-boxes for these matrices are greater than the corresponding values for the ARIA block cipher for $R>5$.
To design $32 \times 32$ matrices, a structure with a special form is proposed. Using this structure, a $32\times 32$ binary matrix is proposed which guarantees at least 48 active S-boxes for 8 rounds of an SPN structure with this matrix as its diffusion layer. By extending this structure, a $32\times 32$ non-binary matrix is presented which results in at least 60 active S-boxes after 8 rounds.

Abstract Reliable access control is a major challenge of cloud storage services. This paper presents a cloud-based file-sharing architecture with ciphertext-policy attribute-based encryption (CP-ABE) access control mechanism. In CP-ABE, the data owner can specify the ciphertext access structure, and if the user key satisfies this access structure, the user can decrypt the ciphertext. The trusted authority embeds the private key of each attribute in a so-called attribute access polynomial and stores its coefficients publicly on the cloud. By means of the access polynomial, each authorized user will be able to retrieve the private key of the attribute by using her/his owned pre-shard key. In contrast, the data owner encrypts the file with a randomly selected key, namely the cipher key. The data owner encrypts the cipher key by CP-ABE scheme with the desired policies. Further, the data owner can create a different polynomial called query access polynomial for multi-keyword searching. Finally, the data owner places the encrypted file along the encrypted cipher key and query access polynomial in the cloud. The proposed scheme supports fast attribute revocation using updating the corresponding access polynomial and re-encrypting the affected cipher keys by the cloud server. Moreover, most of the calculations at the decryption and searching phases are outsourced to the cloud server, thereby allowing the lightweight nodes with limited resources to act as data users. Our analysis shows that the proposed scheme is both secure and efficient.

Abstract Conventional Bit-based Division Property (CBDP), as a generalization of integral property, has been a powerful tool for integral cryptanalysis of many block ciphers. Exploiting a Mixed Integral Linear Programming (MILP) optimizer, an alternative approach to searching integral distinguishers was proposed, which has overcome the bottleneck of the cipher block length. The MILP-aided method starts by modeling CBDP propagation by a system of linear inequalities. Then by choosing an appropriate objective function, the problem of searching distinguisher transforms into an MILP problem. As an application of this technique, we focused on a newly proposed lightweight block cipher SAND. SAND is a family of two AND-RX block ciphers SAND-64 and SAND-128, which was designed to overcome the difficulty regarding security
evaluation. For SAND-64, we found a 12-round distinguisher with 23 balanced bits and a data complexity of 263, with the superiority of a higher number of balanced bits than the designers’ one. Furthermore, we applied an integral attack on a 15 and 16-round SAND-64, including the key recovery step which resulted in time complexity of 2105 and 2109.91 and memory complexity of 252 and 285 bytes, respectively.

Abstract Side-channel Analysis (SCA) attacks are effective methods for extracting encryption keys, and with deep learning (DL) techniques, much stronger attacks have been carried out on victim devices. However, carrying out this kind of attack is much more challenging in cross-device attacks when the profiling device and target device are similar but not the same, which can cause the attack to fail. We also reached this conclusion when using only DL-SCA attack on our cross-devise (Atmega microcontroller devices). Due to different processes that lead to significant device-to-device variations, the accuracy of the attack was, on average, only 23%. In this paper, we proposed a method for a real attack on cross-devices using pre-processing methods based on a combination of DL-based Autoencoder and Gaussian low-pass filter (GLPF). According to our analysis results, the accuracy of the attack using only deep learning-based Autoencoder increased to 70% on average, and it improved up to 82% by adding the GLPF technique. The results also showed that combining DL-based autoencoder and GLPF can lead to a successful attack with a maximum of 300 power traces from the victim device.

Abstract Side-channel analysis methods can reveal the secret information of digital electronic systems by analyzing the dependency between the power consumption of implemented cryptographic algorithms and the secret data. Recent studies show that it is possible to gather information about power consumption from FPGAs without any physical access. High flexibilities of modern FPGAs cause that they are used for cloud accelerator in Platform as a Service (PaaS) system; however, new serious vulnerabilities emerged for these platforms. Although there are some reports about how switching activities from one region of FPGA affect other regions, details of this technique are not analyzed. In this paper, we analyzed the strength of this kind of attack and examined the impact of geometrical and electrical parameters of the victim/attacker modules on the efficiency of this attack. We utilized a Zynq-based Xilinx platform as the device under attack. Experimental results and analyses show that the distance between the victim module and the sensor modules is not the only effective parameter on the quality of attack; the influence of the relational location of victim/attacker modules could be more considerable on the quality of attack.

Abstract The exact manner of BKZ algorithm for higher block sizes cannot be studied by practical running, so simulation of BKZ can be used to predict the total cost and output quality of BKZ algorithm. Sampling method of enumeration solution vector v is one of the main components of designing BKZ-simulation and can be divided into two phases: sampling norm of solution vector v and sampling corresponding coefficient vectors. This paper introduces a simple and efficient idea for sampling the norm of enumeration solution v for any success probability of enumeration bounding functions, while to the best of our knowledge, no such sampling method for norm of enumeration solution is proposed in former studies. Next, this paper analyzes the structure and probability distribution of coefficient vectors (corresponding with enumeration solution v), and consequently introduces the sampling methods for these coefficient vectors which are verified by our test results, while no such a deep analysis for sampling coefficient vectors is considered in design of former BKZ-simulations. Moreover, this paper proposes an approximation for cost of enumerations pruned by optimal bounding functions.

Abstract In recent years technology and management information system has been an excellent response too many global challenges, technology innovation has expanded over almost all the sectors of, and it made many processes more accurate and very faster than before. Technology systems playeda big role part in election processes in many democratic countries nowadays. The commission, in Iraq, suffers from many problems such as fraud, time-consuming and delays in the election processes that take a long time and also witness a delay in revealing the results. This research paper focuses on adapting the biometric system in Iraq; there are several different perspectives to specify the IHEC’s employees and manager’s attitude towards technology in general and Biometric system specifically. Most of the staff members feel confident about transforming into a technology system. In their responses to the questionnaires, most of them focused on getting trained before they start using the system. In this research, the data is collected by using survey technique from the independent high electoral commission managers and staff members, and the data is analyzed by using SPSS.

Abstract Large Language Models (LLMs), trained on extensive datasets using advanced deeplearning architectures, have demonstrated remarkable performance across a wide range of language tasks, becoming a cornerstone of modern AI technologies. However, ensuring their trustworthiness remains a critical challenge, asreliability is essential not only for accurate performance but also for upholding ethical, cultural, and social values. Careful alignment of training data and culturally grounded evaluation criteria is vital for developing responsible AI systems. In this study, we introduce the EPT (Evaluation of Persian Trustworthiness) metric, a culturally informed benchmark specifically designed to assess the trustworthiness of LLMs across six key aspects: Truthfulness, Safety, Fairness, Robustness, privacy, and ethical alignment. We curated a labelled dataset and evaluated the performance of several leading models—including ChatGPT, Claude, DeepSeek, Gemini, Grok, LLaMA, Mistral, and Qwen—using both automated LLM-based and human assessments. Our results reveal significant deficiencies in the safety dimension, underscoring the urgent need for focused attention on this critical aspect of model behaviour. Furthermore, our findings offer valuable insights into the alignment of these models with Persian ethical-cultural values and highlight critical gaps and opportunities for advancing trustworthy and culturally responsible AI. The dataset is publicly available at: https://github.com/Rezamirbagheri110/EPT-Benchmark.

Abstract This study evaluates three encoding methods for automated differential cryptanalysis: (1) SMT formulations (using CVC), (2) standard CNF, and (3) size-optimised CNF (via Logic Friday). We assess these using four SAT/SMT solver types: single-core (CryptoMiniSat-v5, CaDiCaL), multicore (Treengeling), and massively parallel Mallob—novel to cryptanalysis. Encoding-solver combinations are tested on seven lightweight block ciphers representing distinct design philosophies: SPECK-32 and CHAM-64 (ARX structure), SIMON-32 (AND-RX structure), PRESENT, GIFT-128, and MIDORI-64 (4-bit S-box in SPN structure), and LBLOCK (Feistel structure). For each cipher, SAT/SMT instances targeting specific rounds and differential weights were generated, with wall-clock solving time, parallel efficiency, and modelling effort recorded. Our results establish criteria for optimal encoding-solver pairings that strike a balance between modelling simplicity and computational performance. Crucially, Mallob emerges as the state-of-the-art framework for large-scale automated differential cryptanalysis.

Abstract Intrusion Detection Systems (IDS) are vital for defending modern networks against emerging cyber threats, including zero-day attacks. In this article, we introduce GAT-AID (Graph Attention-based Anomaly and Intrusion Detection), an IDS architecture that integrates Graph Attention Networks (GATs), Multi-Layer Perceptron (MLP) classifiers, and Autoencoders. The proposed methodology represents network traffic as a graph, allowing GAT to extract complex node-wise associations across traffic flows. The embeddings generated are further processed through a dual-branch architecture, an MLP-based classifier for identifying known attack types, and an Autoencoder-based anomaly detector for flagging zero-day intrusions. The proposed GAT-AID methodology is evaluated on two widely used benchmark datasets, namely CICIDS2017 and UNSW-NB15. The experiment results demonstrate that it outperforms conventional IDS baselines, including SVM, Random Forest, CNN, and GCN models, achieving higher detection rates, improved robustness against unseen threats, and greater adaptability to evolving network environments. These findings suggest that GAT-AID is an effective and scalable solution for intelligent, real-time intrusion detection. 

Abstract Machine learning systems, despite exhibiting high inference accuracy in practical applications, are susceptible to security and reliability concerns both during the training phase and the inference phase. In this paper, we have demonstrated that it is possible to extract internal information from a neural network without physical access. This attack was executed through the utilization of a power sensor. This sensor enables remote sampling. Thus far, the sensor has been employed to extract power samples from cryptographic circuits, and its functionality and correctness have been thoroughly tested. Now, in this paper, the same power sensor is used to extract power samples from a neural network, allowing us to assess the supervisor’s performance for applications beyond cryptographic algorithms. In this paper, we demonstrate that the power sensor accurately extracts power samples from neural networks. This paper reveals that between 20,000 and 50,000 power samples of a 16-bit neural network weight can be retrieved. The final step involved hardening the neural network against side-channel attacks. Test results in this section demonstrate that it is possible to make the neural network resistant to first-order side-channel attacks with an area overhead of about 6%. The degree of reinforcement was measured using the assumption test method, revealing that the attack has become eight times more challenging.

Abstract Mobile phones are among the most significant technological advancements, offering unmatched convenience and seamlessly integrating into modern lifestyles. However, their widespread use also facilitates both beneficial and harmful practices. The absence of comprehensive datasets with reliable app descriptions undermines user confidence in Google Play as a trustworthy platform for software. To address this gap, we introduce a new dataset, GP-FACL, in this study. This dataset contains "fake apps” that make false claims in their descriptions and pretend to offer features that do not actually exist. Applications were first manually collected, after which keywords were extracted to generate 2-gram key phrases. These key phrases were then used to automate the collection of additional applications. The final dataset provides a systematic approach for identifying false-claim applications across a variety of app categories. Our approach resulted in 117 applications being verified as containing erroneous or misleading claims. This dataset offers researchers and practitioners a valuable resource for advancing fraud detection and mitigating deceptive applications on mobile platforms.

Abstract This paper describes a fast exhaustive search preimage attack on AIM2, an improved version of the one-way function AIM, proposed to address algebraic vulnerabilities found in its predecessor. Our attack transforms the polynomial system describing AIM2 over F2λ to a boolean polynomial system over F2, allowing for an exhaustive search by guessing input bits and solving a resulting linear system. Solving the whole system is not necessary for most incorrect guesses, and use of Gray code helps optimizing the iteration over all possible guesses. Our results show that the complexity of exhaustive search on AIM2, especially AIM2-I and AIM2-III is lower than previously estimated, though still higher than that of AES.