Recently, the growing popularity of the Internet of Things (IoT) presents a promising opportunity not only for the expansion of various home automation systems but also for diverse industrial applications. By leveraging these benefits, automation is being implemented in industries, leading to the Industrial Internet of Things (IIoT). Although IoT simplifies daily activities that benefit human operations, it poses significant security challenges that warrant attention. Consequently, implementing an Intrusion Detection System (IDS) is a vital and effective solution. IDS aims to address the security and privacy challenges by detecting various IoT attacks. Various IDS methodologies, including those using Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLMs), are employed to identify intrusions within the data; however, improvements to the detection systems are still needed. A literature survey on IDS in the IoT domain is provided, focusing primarily on the recent approaches used in the field. The survey aims to evaluate the literature, identify current trends, retest these approaches on recent data, and highlight open problems and future directions.

The present research has found that container virtualization systems are more effective and more straightforward to handle in contrast to traditional virtualization systems. The live migration processes can be substantially reduced by using lightweight containers developed on virtualization techniques. It shrinks the suspension of service by transmitting minimum memory of the source without stopping the migration process. Therefore, transferring many memory pages leads to long migration time and downtime, which affects the performance of containerized applications and cost is a major concern. In this paper, the pre-copy container live migration is analyzed in a detailed manner to trace out the possibilities to improve the performance in various applications and introduce a probability-based migration technique to overcome the memory transmission limitations. For continual learning, we used containers to run and manage the AI-based system on the cloud to overcome the delay in service during migration. The pre-dump is the first phase of sending the container file system to the destination host. Additionally, dirty pages are predicted during a migration process using the Meta-Heuristic approach. After that, the active set of pages and their update rate have also been identified. In addition, based on the threshold level of the maximum update rate, the pages have been shortlisted to be discarded from pre-dump. The proposed technique is implemented and tested on different scenarios, and results show that it reduces the size of the pre-dump by 26.48% compared to the traditional pre-copy migration technique, which leads to less downtime for cloud services. The results reveal that the suggested technique surpasses existing alternatives in overall migration time and amount of data transmitted. There is 31.04% reduction in the amount of data transferred during the iterative phase.

Electric vehicles (EVs) and their ecosystem have unquestionably made significant technological strides. Indeed, EVs have evolved into sophisticated computer systems with extensive internal and external communication capabilities. This interconnection raises concerns about security, privacy, and the expanding risk of cyber-attacks within the electric vehicle landscape. In particular, the charging infrastructure plays a crucial role in the electric mobility ecosystem. With the proliferation of charging points, new attack vectors are opened up for cybercriminals. The threat landscape targeting charging systems encompasses various types of attacks ranging from physical attacks to data breaches including customer information. In this paper, we aim to leverage the power of model-driven engineering to model and analyze EV charging systems at early stages. We employ domain-specific modeling language (DSML) techniques for the early security modeling and analysis of EV charging infrastructure. We accomplish this by integrating the established EMSA model for electric mobility, which encapsulates all key stakeholders in the ecosystem. To our knowledge, this represents the first instance in the literature of applying DSML within the electric mobility ecosystem, highlighting its innovative nature. Moreover, as our formalization based on DSML is an iterative, continuous, and evolving process, this approach guarantees that our proposed framework adeptly tackles the evolving cyber threats confronting the EV industry. Specifically, we use the Object Constraint Language (OCL) for precise specification and verification of security threats as properties of a modeled system. To validate our framework, we explore a set of representative threats targeting EV charging systems from real-world scenarios. To the best of our knowledge, this is the first attempt to provide a comprehensive security modeling framework for the electric mobility ecosystem.

Extracting structured intelligence via Named Entity Recognition (NER) is critical for cybersecurity, but the proliferation of datasets with incompatible annotation schemas hinders the development of comprehensive models. While combining these resources is desirable, we empirically demonstrate that naively concatenating them results in a noisy label space that severely degrades model performance. To overcome this critical limitation, we introduce CyberNER, a large-scale, unified corpus created by systematically harmonizing four prominent datasets (CyNER, DNRTI, APTNER, and Attacker) onto the STIX 2.1 standard. Our principled methodology resolves semantic ambiguities and consolidates over 50 disparate source tags into 21 coherent entity types. Our experiments show that models trained on CyberNER achieve a substantial performance gain, with a relative F1-score improvement of approximately 30% over the naive concatenation baseline. By publicly releasing the CyberNER corpus, we provide a crucial, standardized benchmark that enables the creation and rigorous comparison of more robust and generalizable entity extraction models for the cybersecurity domain.