The application of emerging technologies, such as Artificial Intelligence (AI), entails risks that need to be addressed to ensure secure and trustworthy socio-technical infrastructures. Machine ...Learning (ML), the most developed subfield of AI, allows for improved decision-making processes. However, ML models exhibit specific vulnerabilities that conventional IT systems are not subject to. As systems incorporating ML components become increasingly pervasive, the need to provide security practitioners with threat modeling tailored to the specific AI-ML pipeline is of paramount importance. Currently, there exist no well-established approach accounting for the entire ML life-cycle in the identification and analysis of threats targeting ML techniques. In this paper, we propose an asset-centered methodology—STRIDE-AI—for assessing the security of AI-ML-based systems. We discuss how to apply the FMEA process to identify how assets generated and used at different stages of the ML life-cycle may fail. By adapting Microsoft’s STRIDE approach to the AI-ML domain, we map potential ML failure modes to threats and security properties these threats may endanger. The proposed methodology can assist ML practitioners in choosing the most effective security controls to protect ML assets. We illustrate STRIDE-AI with the help of a real-world use case selected from the TOREADOR H2020 project.
Distributed Ledger Technologies (DLTs), like Blockchain, are characterized by features such as transparency, traceability, and security by design. These features make the adoption of Blockchain ...attractive to enhance information security, privacy, and trustworthiness in very different contexts. This paper provides a comprehensive survey and aims at analyzing and assessing the use of Blockchain in the context of Distributed Trust and Reputation Management Systems (DTRMS). The analysis includes academic research as well as initiatives undertaken in the business domain. The paper defines two taxonomies for both Blockchain and DTRMS and applies a Formal Concept Analysis. Such an approach allowed us to identify the most recurrent and stable features in the current scientific landscape and several important implications among the two taxonomies. The results of the analysis have revealed significant trends and emerging practices in the current implementations that have been distilled into recommendations to guide Blockchain's adoption in DTRMS systems.
An increasing number of data demonstrate the utility of ketogenic diets in a variety of metabolic diseases as obesity, metabolic syndrome, and diabetes. In regard to neurological disorders, ketogenic ...diet is recognized as an effective treatment for pharmacoresistant epilepsy but emerging data suggests that ketogenic diet could be also useful in amyotrophic lateral sclerosis, Alzheimer, Parkinson’s disease, and some mitochondriopathies. Although these diseases have different pathogenesis and features, there are some common mechanisms that could explain the effects of ketogenic diets. These mechanisms are to provide an efficient source of energy for the treatment of certain types of neurodegenerative diseases characterized by focal brain hypometabolism; to decrease the oxidative damage associated with various kinds of metabolic stress; to increase the mitochondrial biogenesis pathways; and to take advantage of the capacity of ketones to bypass the defect in complex I activity implicated in some neurological diseases. These mechanisms will be discussed in this review.
IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious ...target for malicious actors. Attacks such as botnets and malware injection usually start with a phase of reconnaissance to gather information about the target IoT device before exploitation. In this paper, we introduce a machine-learning-based detection system for reconnaissance attacks based on an explainable ensemble model. Our proposed system aims to detect scanning and reconnaissance activity of IoT devices and counter these attacks at an early stage of the attack campaign. The proposed system is designed to be efficient and lightweight to operate in severely resource-constrained environments. When tested, the implementation of the proposed system delivered an accuracy of 99%. Furthermore, the proposed system showed low false positive and false negative rates at 0.6% and 0.05%, respectively, while maintaining high efficiency and low resource consumption.
Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic ...process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.
Traditionally, cyber-attack detection relies on reactive, assistive techniques, where pattern-matching algorithms help human experts to scan system logs and network traffic for known virus or malware ...signatures. Recent research has introduced effective Machine Learning (ML) models for cyber-attack detection, promising to automate the task of detecting, tracking and blocking malware and intruders. Much less effort has been devoted to cyber-attack prediction, especially beyond the short-term time scale of hours and days. Approaches that can forecast attacks likely to happen in the longer term are desirable, as this gives defenders more time to develop and share defensive actions and tools. Today, long-term predictions of attack waves are mostly based on the subjective perceptiveness of experienced human experts, which can be impaired by the scarcity of cyber-security expertise. This paper introduces a novel ML-based approach that leverages unstructured big data and logs to forecast the trend of cyber-attacks at a large scale, years in advance. To this end, we put forward a framework that utilises a monthly dataset of major cyber incidents in 36 countries over the past 11 years, with new features extracted from three major categories of big data sources, namely the scientific research literature, news, blogs, and tweets. Our framework not only identifies future attack trends in an automated fashion, but also generates a threat cycle that drills down into five key phases that constitute the life cycle of all 42 known cyber threats.
This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under ...harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate-using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards-that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.
This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of ...Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the "black-box" manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.
Augmented Reality (AR) has experienced a significant resurgence in popularity and interest in recent years. Despite numerous surveys and reviews in the field, information remains scattered and ...challenging to consolidate for newcomers. This paper addresses this gap with a comprehensive study of AR's state of the art. We begin with an introduction to AR and related terminologies. We then describe three enabling of AR and four enhancing technologies. A survey then covers different types of commercial AR hardware with detailed specifications and elaborations. We then address AR software and discuss its basic and advanced features, mapping them to software with feature matrices. Based on a thematic literature analysis, we extract a comprehensive set of guidelines for developing AR solutions into seven themes, from ideation to best practices for implementation and deployment. Finally, we explore some essential challenges in the field of AR. This paper serves as an essential resource for those looking to understand what AR is, what is needed to get started, how to approach development, and what the future holds for AR. By consolidating essential information and providing a solid foundation, this paper aims to help researchers and developers capitalize on the current cycle of interest in AR.
This study discusses the general overview of Timed Efficient Stream Loss-tolerant Authentication (TESLA) protocol, including its properties, key setups, and improvement protocols. The discussion ...includes a new proposed two-level infinite <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>TESLA (TLI <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>TESLA) protocol that solves the authentication delay and synchronization issues. We theoretically compared TLI <inline-formula> <tex-math notation="LaTeX">\mu </tex-math></inline-formula>TESLA with the previously proposed protocols in terms of security services and showed that the new protocol prevents excessive use of the buffer in the sensor node and reduces the DoS attacks on the network. In addition, it accelerates the authentication process of the broadcasted message with less delay and assures continuous receipt of packets compared to previous TESLA Protocols. We also addressed the challenges faced during the implementation of TESLA protocol and presented the recent solutions and parameter choices for improving the efficiency of the TESLA protocol. Moreover, we focused on utilizing biometric authentication as a promising approach to replace public cryptography in the authentication process.
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