Ali A. Ghorbani

Yaser Baseri, Vikas Chouhan, Ali Ghorbani

The emergence of quantum computing presents a double-edged sword for cybersecurity. While its immense power holds promise for advancements in various fields, it also threatens to crack the foundation of current encryption methods. This analysis explores the impact of quantum computing on critical infrastructure and cloud services, meticulously evaluating potential vulnerabilities across various layers, including applications, data, runtime, middleware, operating systems, virtualization, hardware, storage, and networks. We advocate for proactive security strategies and collaboration between sectors to develop and implement quantum-resistant cryptography. This crucial shift necessitates a comprehensive approach, and the paper introduces a tailored security blueprint encompassing nine critical infrastructure components. This blueprint strengthens each area's defenses against potential quantum-induced cyber threats. Our strategic vulnerability and risk assessment equips stakeholders with the knowledge to navigate the complex quantum threat landscape. This empowers them to make informed decisions about design, implementation, and policy formulation, ultimately bolstering the resilience of critical infrastructure. In essence, this analysis not only forecasts quantum threats but also offers a sophisticated, actionable framework for fortifying infrastructure and cloud environments against the multifaceted challenges of the quantum era. This proactive approach will ensure continued data security and a thriving digital landscape in the years to come

Hossein Shokouhinejad, Roozbeh Razavi-Far, Hesamodin Mohammadian et al.

The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques have emerged as powerful tools for modeling and analyzing the complex relationships inherent in malware behavior, leveraging advancements in Graph Neural Networks (GNNs) and related methods. This survey provides a comprehensive exploration of recent advances in malware detection, focusing on the interplay between graph learning and explainability. It begins by reviewing malware analysis techniques and datasets, emphasizing their foundational role in understanding malware behavior and supporting detection strategies. The survey then discusses feature engineering, graph reduction, and graph embedding methods, highlighting their significance in transforming raw data into actionable insights, while ensuring scalability and efficiency. Furthermore, this survey focuses on explainability techniques and their applications in malware detection, ensuring transparency and trustworthiness. By integrating these components, this survey demonstrates how graph learning and explainability contribute to building robust, interpretable, and scalable malware detection systems. Future research directions are outlined to address existing challenges and unlock new opportunities in this critical area of cybersecurity.

Hesamodin Mohammadian, Griffin Higgins, Samuel Ansong et al.

Control Flow Graphs and Function Call Graphs have become pivotal in providing a detailed understanding of program execution and effectively characterizing the behavior of malware. These graph-based representations, when combined with Graph Neural Networks (GNN), have shown promise in developing high-performance malware detectors. However, challenges remain due to the large size of these graphs and the inherent opacity in the decision-making process of GNNs. This paper addresses these issues by developing several graph reduction techniques to reduce graph size and applying the state-of-the-art GNNExplainer to enhance the interpretability of GNN outputs. The analysis demonstrates that integrating our proposed graph reduction technique along with GNNExplainer in the malware detection framework significantly reduces graph size while preserving high performance, providing an effective balance between efficiency and transparency in malware detection.

Hossein Shokouhinejad, Griffin Higgins, Roozbeh Razavi-Far et al.

Control Flow Graphs (CFGs) are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks (GNNs), CFG-based representations have proven highly effective for malware detection. This study proposes a novel framework that dynamically constructs CFGs and embeds node features using a hybrid approach combining rule-based encoding and autoencoder-based embedding. A GNN-based classifier is then constructed to detect malicious behavior from the resulting graph representations. To improve model interpretability, we apply state-of-the-art explainability techniques, including GNNExplainer, PGExplainer, and CaptumExplainer, the latter is utilized three attribution methods: Integrated Gradients, Guided Backpropagation, and Saliency. In addition, we introduce a novel aggregation method, called RankFusion, that integrates the outputs of the top-performing explainers to enhance the explanation quality. We also evaluate explanations using two subgraph extraction strategies, including the proposed Greedy Edge-wise Composition (GEC) method for improved structural coherence. A comprehensive evaluation using accuracy, fidelity, and consistency metrics demonstrates the effectiveness of the proposed framework in terms of accurate identification of malware samples and generating reliable and interpretable explanations.

Hamed Jelodar, Mohammad Meymani, Parisa Hamedi et al.

Natural Language Description (NLD) is a Natural Language Processing (NLP) task that requires models to generate structured and meaningful outputs from natural language inputs. In this work, we propose NLD-LLM, a systematic NLP framework to evaluate the performance of language models to generate accurate and concise source code descriptions. This framework incorporates a diverse set of transformer models, including Qwen, DeepSeek, Phi, LLaMA, and Mistral, spanning various sizes, architectures, and training approaches. Central to NLD-LLM is a comprehensive prompt design strategy that includes standardized formatting, clear task guidance, and NLD prompting, ensuring fair and consistent evaluation. Additionally, we apply an iterative refinement process to improve output's quality and assess the model's adaptability. Using semantic and structural metrics, our analysis demonstrates that prompt engineering significantly impacts the effectiveness of the model such that smaller models often performing competitively when supported by well-crafted prompts.

Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins et al.

Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Networks (GNNs) often highlight important regions within a graph but fail to associate them with known benign or malicious behavioral patterns. This limitation reduces their utility in security contexts, where alignment with verified prototypes is essential. In this work, we introduce a novel dual prototype-driven explainable framework that interprets GNN-based malware detection decisions. This dual explainable framework integrates a base explainer (a state-of-the-art explainer) with a novel second-level explainer which is designed by subgraph matching technique, called SubMatch explainer. The proposed explainer assigns interpretable scores to nodes based on their association with matched subgraphs, offering a fine-grained distinction between benign and malicious regions. This prototype-guided scoring mechanism enables more interpretable, behavior-aligned explanations. Experimental results demonstrate that our method preserves high detection performance while significantly improving interpretability in malware analysis.

Parisa Hamedi, Hamed Jelodar, Samita Bai et al.

Assembly-to-source code translation is a critical task in reverse engineering, cybersecurity, and software maintenance, yet systematic benchmarks for evaluating large language models on this problem remain scarce. In this work, we present the first comprehensive evaluation of five state-of-the-art large language models on assembly-to-source translation. We assess model performance using a diverse set of metrics capturing lexical similarity (BLEU, ROUGE, and METEOR), semantic alignment (BERTScore), fluency (Perplexity), and efficiency (time prediction). Our results reveal clear trade-offs: while certain models excel in text similarity metrics, others demonstrate lower perplexity or faster inference times. We further provide qualitative analyses of typical model successes and failure cases, highlighting challenges such as control flow recovery and identifier reconstruction. Taken together, our benchmark offers actionable insights into the strengths and limitations of current large language models for program translation, establishing a foundation for future research in combining accuracy with efficiency for real-world applications.

Duc-Phong Le, Rongxing Lu, Ali A. Ghorbani

The advances of the Internet of Things (IoT) have had a fundamental impact and influence in sharping our rich living experiences. However, since IoT devices are usually resource-constrained, lightweight block ciphers have played a major role in serving as a building block for secure IoT protocols. In CHES 2015, SIMECK, a family of block ciphers, was designed for resource-constrained IoT devices. Since its publication, there have been many analyses on its security. In this paper, under the one bit-flip model, we propose a new efficient fault analysis attack on SIMECK ciphers. Compared to those previously reported attacks, our attack can recover the full master key by injecting faults into only a single round of all SIMECK family members. This property is crucial, as it is infeasible for an attacker to inject faults into different rounds of a SIMECK implementation on IoT devices in the real world. Specifically, our attack is characterized by exercising a deep analysis of differential trail between the correct and faulty immediate ciphertexts. Extensive simulation evaluations are conducted, and the results demonstrate the effectiveness and correctness of our proposed attack.