The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.

Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.

Proper security solutions in the cyber world are crucial for enforcing network security by providing real-time network protection against network vulnerabilities and data exploitation. An effective intrusion detection strategy is capable of taking a holistic approach for protecting critical systems against unauthorized access or attack. In this paper, we describe a machine learning (ML) based comprehensive security solution for network intrusion detection using ensemble supervised ML framework and ensemble feature selection methods. In addition, we provide a comparative analysis of several ML models and feature selection methods. The goal of this research is to design a generic detection mechanism and achieve higher accuracy with minimal false positive rates (FPR). NSL-KDD, UNSW-NB15, and CICIDS2017 datasets are used in the experiment, and results show that our detection model can identify 99.3% of intrusions successfully with the lowest 0.5% of false alarms, which depicts better performance metrics compared to existing solutions.

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

The purpose of this article is to clarify current and widespread misconceptions about the properties of blockchain technologies and to describe challenges and avenues for correct and trustworthy design and implementation of distributed ledger system (DLS) or Technology (DLT). The authors contrast the properties of a blockchain with desired, however emergent, properties of a DLS, which is a complex and distributed system. They point out and justify, with facts and analysis, current misconceptions about the blockchain and DLSs. They describe challenges that these systems will need to address and possible solution avenues for achieving trustworthiness. Many of the statements that have appeared on the internet, news and academic articles, such as immutable ledger and exact copies, may be misleading. These are desired emergent properties of a complex system, not assured properties. It is well-known within the distributed systems and critical software community that it is extremely hard to prove that a complex system correctly and completely implements emergent properties. Further research and development for trustworthy DLS design and implementation is needed, both practical and theoretical. This is the first known published attempt at describing current misconceptions about blockchain technologies. Further collaborative work, discussions, potential solutions, evaluations, resulting publications and verified reference implementations are needed to ensure DLTs are safe, secure, and trustworthy. Interdisciplinary teams with members from academia, business and industry, and from disciplines such as business, entrepreneurship, theoretical and practical computer science, cybersecurity, finance, mathematics and statistics, must be formed. Such teams must collaborate with the objective of developing strategies and techniques for ensuring the correctness and security of future DLSs in which our society may become dependent. The value and originality of this article is twofold: the disproving, through fact collection and systematic analysis, of current misconceptions about the properties of the blockchain and DLSs, and the discussion of challenges to achieving adequate trustworthiness along with the proposal of general avenues for possible solutions.

The confluence of the Internet of Things (IoT) and cloud computing heralds a paradigm shift in data-driven applications, promising unprecedented insights and automation across critical sectors like healthcare, smart cities, and industrial automation. However, this transformative synergy introduces a complex tapestry of security vulnerabilities stemming from the intrinsic resource limitations of IoT devices and the inherent complexities of cloud infrastructures. This survey delves into the escalating threats—from conventional data breaches and Application programming interface (API) exploits to emerging vectors such as adversarial artificial intelligence (AI), quantum-resistant attacks, and sophisticated insider threats—that imperil the integrity and resilience of IoT–cloud ecosystems. We critically evaluated existing security paradigms, including encryption, access control, and service-level agreements, juxtaposed with cutting-edge approaches like AI-driven anomaly detection, blockchain-secured frameworks, and lightweight cryptographic solutions. By systematically mapping the landscape of security challenges and mitigation strategies, this work identified the following critical research imperatives: the development of standardized, end-to-end security architectures, the integration of post-quantum cryptography for resource-constrained IoT devices, and the fortification of resource isolation in multi-tenant cloud environments. A comprehensive comparative analysis of prior research, coupled with an in-depth case study on IoT–cloud security within the healthcare domain, illuminates the practical challenges and innovative solutions crucial for real-world deployment. Ultimately, this survey advocates for the development of scalable, adaptive security frameworks that leverage the synergistic power of AI and blockchain, ensuring the secure and efficient evolution of IoT–cloud ecosystems in the face of evolving cyber threats.