Securing the Internet of Things (IoT) against cyber threats is a formidable challenge, and Intrusion Detection Systems (IDS) play a critical role in this effort. However, the lack of transparent explanations for IDS decisions remains a significant concern. In response, we introduce a novel approach that leverages a blending model for attack classification and integrates counterfactual and Local Interpretable Model-Agnostic Explanations (LIME) techniques to enhance explanations. To assess the effectiveness of our approach, we conducted experiments using the recently introduced CICIoT2023 and IoTID20 datasets. These datasets are real-time and large-scale benchmark datasets for IoT environment attacks, offering a realistic and challenging scenario that captures the intricacies of intrusion detection in dynamic IoT environments. Our experimental results demonstrate significant improvements in attack detection accuracy compared to conventional IDS methods. Furthermore, our proposed approach provides clear and interpretable insights into the factors influencing classification decisions, empowering users to make informed security choices. Integrating blending model classification and explanation techniques enhances the security and reliability of IoT systems. Therefore, this work represents a significant advancement in IoT intrusion detection, offering a robust and transparent defense against large-scale cyber-attacks of IoT environment data.

This paper examines the asymptotic performance of multiplication and the cost of quantum implementation for the Naive schoolbook, Karatsuba, and Toom-Cook methods in the classical and quantum cases and provides insights into multiplication roles in the post-quantum cryptography (PQC) era. Further, considering that the lattice-based PQC algorithm is based on polynomial multiplication algorithms, including the Toom-Cook 4-way multiplier as its fundamental building block, we propose a higher-degree multiplier, the Toom-Cook 8-way multiplier, which has the lowest asymptotic performance and implementation cost. Additionally, the designed multiplication will include additional sub-operations to complete the multiplication of large integers in order to prevent side-channel attacks. To design our Toom-Cook 8-way in detail, we employ detailed step computations such as splitting, evaluation, point-wise multiplication, interpolation, and recomposition, as well as several strategies to reduce space and time requirements. Existing asymptotic performance and quantum implementation cost multipliers are compared with our 2-way, 4-way, and 8-way Toom-Cook multiplier designs. Our Toom-Cook 8-way quantum multiplier has the lowest asymptotic performance analysis or qubit count in terms of space efficiency, with $n\left({\frac {15}{8}}\right)^{\frac {\log 15}{(2\log 15 - \log 8)} \log _{8} n}$ or asymptotically $\mathcal {O} (n^{1.245})$ . The design has the lowest logical Toffoli counts bound at $112n^{\log _{8} 15}-128n $ and Toffoli depth of $n\left({\frac {15}{8}}\right)^{1-\frac {\log 15}{(2\log 15 - \log 8)} \log _{8} n }$ , asymptotically close to $\mathcal {O}(n^{1.0569})$ , which corresponds to a space- and time-efficient multiplication.

This paper presents quantum cryptanalysis for binary elliptic curves from a time-efficient implementation perspective (i.e., reducing the circuit depth), complementing the previous research that focuses on the space-efficiency perspective (i.e., reducing the circuit width). To achieve depth optimization, we propose an improvement to the existing circuit implementation of the Karatsuba multiplier and FLT-based inversion, then construct and analyze the resource in the Qiskit quantum computer simulator. The proposed multiplier architecture, which improves the quantum Karatsuba multiplier from the previous study, reduces the depth and yields a lower number of CNOT gates that bound to O ( n ^log₂(3) ) while maintaining a similar number of Toffoli gates and qubits. Furthermore, our improved FLT-based inversion reduces CNOT count and overall depth, with a tradeoff of higher qubit size. Finally, we employ the proposed multiplier and FLT-based inversion for performing quantum cryptanalysis of binary point addition as well as the complete Shor’s algorithm for the elliptic curve discrete logarithm problem (ECDLP). As a result, apart from depth reduction, we are also able to reduce up to 90% of the Toffoli gates required in a single-step point addition compared to prior work, leading to significant improvements and giving new insights on quantum cryptanalysis for a depth-optimized implementation.

Quantum technology has garnered considerable attention for its transformative potential across various industries since the early 2000s, precipitating a surge in quantum start-ups and substantial investments worldwide. However, comprehensive research on quantum technology in industries, particularly start-ups in the quantum field, remains limited. This study analyzes a dataset of 1,200 quantum start-ups from 40 countries, specifically focusing on 430 notable active start-ups. The analysis provides a thorough perspective on the industry’s progress, including trends in growth, geographical distribution, industry categorization, and investment patterns. The study categorizes start-ups into five domains: quantum computing, communication, sensing, hardware, and software. The analysis further captures the progress made by ten prominent quantum start-ups, six major technology companies, and quantum consortia regarding their technological innovations and strategic partnerships. We provide a comparative analysis of prior research to identify current conditions and future implications validated by notable studies, focusing on key parameters such as dataset scope, geographical distribution, growth trends, industry categorization, taxonomy, main interests in quantum fields, key players, and ecosystem identification. This approach underscores the dynamic growth of the quantum industry, which will drive future advancements.

This paper explores the potential of the hybrid classical deep learning quantum in addressing contemporary information security challenges, including cyber-attack detection and prevention, botnet detection, IP-theft, hardware defect detection, and more, with a specific emphasis on steganalysis. We begin by offering a succinct overview of current information security research trends, focusing on the hybrid classical deep learning-quantum paradigm. Subsequently, we introduce a classical-quantum hybrid framework tailored for uncovering concealed information within a significant public steganalysis dataset, ALASKA2. To enable image extraction and classification, we present a two-layer architecture that integrates convolutional neural network layers into both the classical deep learning and quantum layers. Notably, the proposed approach has improved steganalysis accuracy by achieving a 97% implementation accuracy rate.

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.