This paper presents a novel approach integrating Large Language Models (LLMs) with Formal Verification for automatic software vulnerability repair. Initially, we employ Bounded Model Checking (BMC) to identify vulnerabilities and extract counterexamples. Mathematical proofs and the stack trace of the vulnerabilities support these counterexamples. Using a specially designed prompt, we combine the source code with the identified vulnerability, including its stack trace and counterexample that specifies the line number and error type. This combined information is then fed into an LLM, which is instructed to attempt to fix the code. The new code is subsequently verified again using BMC to ensure the fix succeeded. We present the ESBMC-AI framework as a proof of concept, leveraging the well-recognized and industry-adopted Efficient SMT-based Context-Bounded Model Checker (ESBMC) and a pre-trained transformer model to detect and fix errors in C programs, particularly in critical software components. We evaluated our approach on 50, 000 C programs randomly selected from the FormAI dataset with their respective vulnerability classifications. Our results demonstrate ESBMC-AI’s capability to automate the detection and repair of issues such as buffer overflow, arithmetic overflow, and pointer dereference failures with high accuracy. ESBMC-AI is a pioneering initiative, integrating LLMs with BMC techniques, offering potential integration into the continuous integration and deployment (CI/CD) process within the software development lifecycle.

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models might memorize or guess. To address these limitations, we introduce Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines such as mathematics, cryptography, cybersecurity, and computer science. The accompanying dataset, DIA-Bench, contains a diverse collection of challenge templates with mutable parameters presented in various formats, including text, PDFs, compiled binaries, visual puzzles, and CTF-style cybersecurity challenges. Our framework introduces four new metrics to assess a model’s reliability and confidence across multiple attempts. These metrics revealed that even simple questions are frequently answered incorrectly when posed in varying forms, highlighting significant gaps in models’ reliability. Notably, API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. In self-assessment OpenAI’s o1-mini proved to have the best judgement on what tasks it should attempt to solve. We evaluated 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence, even with simpler questions. The DIA framework sets a new standard for assessing not only problem-solving, but also a model’s adaptive intelligence and ability to assess its limitations. The dataset is publicly available on the project’s page: https://github.com/DIA-Bench.

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

International audience

The connectivity and intelligence of Internet of Things (IoT) equipment offer improved services, but several technical challenges have emerged in recent years that hinder the widespread application of IoT, e.g., security and safety. Cyber-security and privacy countermeasures are widely used in IoT equipment, and many studies have been conducted. However, an important aspect that is often overlooked in security literature is IoT equipment’s physical security and safety, namely, preventing IoT equipment from vandalism and theft. Therefore, this article provides an overview of IoT equipment’s physical security and safety to draw attention to new research opportunities in this area. Afterward, we discuss, among other aspects, antitheft and antivandalism schemes along with circuit and system design, additional sensing devices, biometry and behavior analysis, and tracking methods. Besides, we summarize the artificial intelligence solutions for the physical security and safety of IoT equipment. Finally, we conclude with four future research opportunities.

The majority of systems rely on user authentication on passwords, but passwords have so many weaknesses and widespread use that easily raise significant security concerns, regardless of their encrypted form. Users hold the same password for different accounts, administrators never check password files for flaws that might lead to a successful cracking, and the lack of a tight security policy regarding regular password replacement are a few problems that need to be addressed. The proposed research work aims at enhancing this security mechanism, prevent penetrations, password theft, and attempted break-ins towards securing computing systems. The selected solution approach is two-folded; it implements a two-factor authentication scheme to prevent unauthorized access, accompanied by Honeyword principles to detect corrupted or stolen tokens. Both can be integrated into any platform or web application with the use of QR codes and a mobile phone.

This article presents research challenges and a tutorial on performance evaluation of blockchain-based security and privacy systems for the Internet of Things (IoT). We start by summarizing the existing surveys that deal with blockchain security for IoT networks. Then, we review the blockchain-based security and privacy systems for seventeen types of IoT applications, e.g., Industry 4.0, software-defined networking, edge computing, Internet of Drones, Internet of Cloud, Internet of Energy, Internet of Vehicles, etc. We also review various consensus algorithms and provide a comparison with respect to the nine properties, such as latency, throughput, computation, storage, and communication costs, scalability, attack model, advantage, disadvantage, etc. Moreover, we present the security analysis techniques and provide a classification into four categories, including Burrows, Abadi, and Needham (BAN) logic, game theory, theory analysis, and AVISPA tool. In addition, we analyze the performance metrics, blockchain testbeds, and cryptography libraries used in the performance evaluation of blockchain-based security and privacy systems for the IoT networks. Based on the current survey, we discuss the major steps to follow for building and evaluating blockchain-based security and privacy systems. Finally, we discuss and highlight open challenges and future research opportunities.

This paper proposes a novel intrusion detection system (IDS) that combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.

This paper presents a comprehensive survey of the existing blockchain protocols for the Internet of Things (IoT) networks. We start by describing the blockchains and summarizing the existing surveys that deal with blockchain technologies. Then, we provide an overview of the application domains of blockchain technologies in IoT, e.g., Internet of Vehicles, Internet of Energy, Internet of Cloud, Edge computing, etc. Moreover, we provide a classification of threat models, which are considered by blockchain protocols in IoT networks, into five main categories, namely identity-based attacks, manipulation-based attacks, cryptanalytic attacks, reputation-based attacks, and service-based attacks. In addition, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving blockchain technologies with respect to the blockchain model, specific security goals, performance, limitations, computation complexity, and communication overhead. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the blockchain technologies for IoT.

Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.

In this paper, a comprehensive survey of authentication protocols for Internet of Things (IoT) is presented. Specifically more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail. These protocols are categorized based on the target environment: (1) Machine to Machine Communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). Threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT are presented. In addition a taxonomy and comparison of authentication protocols that are developed for the IoT in terms of network model, specific security goals, main processes, computation complexity, and communication overhead are provided. Based on the current survey, open issues are identified and future research directions are proposed.

In this paper, we propose an intelligent secure detection scheme with strong privacy-preserving, called SDPP, for improving routing security and achieving privacy preservation of message in mobile peer-to-peer social network (MP2PSN). Specifically, in the proposed SDPP scheme, each user is granted with a pseudo-ID, a certificate, and its private key corresponding to his similar interests. Based on the cooperative neighbour technique and the homomorphic encryption method, the proposed SDPP scheme cannot only detect and avoid black hole attacks but it also can preserve the message privacy. Through security analysis, we show that the proposed scheme is secure in the MP2PSN scenarios. Moreover, we also discuss how SDPP can achieve the privacy-preservation of message and the evolution of users’ certificates. Both theoretical and simulation results are given to demonstrate the effectiveness of the proposed scheme in terms of black hole detection rate, average Detectreq reporting, and average transmission delay under various scenarios.