Smart grids rely on advanced metering infrastructure (AMI) for real-time usage data, yet this connectivity introduces vulnerabilities. One critical threat is grid overloading cyberattacks, where an adversary manipulates demand to exceed safe limits and trigger blackouts. Such attacks can harness compromised smart meters to drastically raise the load and destabilize the network. Despite their severity, AMI data manipulation for overloading attacks has received limited attention. We propose a novel anomaly detection framework that combines household and neighborhood-level load predictions. Using deep learning models, our system predicts the electricity usage of each customer, as well as the aggregate community load, allowing detection of sudden deviations that signal an attack. To quantify anomalies, we introduce two metrics: an Abnormality Index (capturing the magnitude of peak deviations) and a Regularity Index (measuring energy consumption pattern consistency). These indices are fed into an ensemble model to detect threats. Crucially, our ensemble model combines multiple classifiers to mitigate adversarial manipulation, making it robust to data poisoning and evasion attempts. Extensive evaluation with a real dataset demonstrates our proposed model achieves a detection rate of 93% and a false alarm rate of 9% in the most challenging baseline conditions, and maintains 81% detection even under the hardest adversarial setting using white-box AutoAttack scenarios, outperforming existing contemporary methods. This integrated solution advances smart grid cybersecurity by combining predictive load modeling, novel anomaly metrics, and robust classification.

Generative Artificial Intelligence (GenAI) is transforming the workplace, but its adoption introduces significant risks to data security and privacy. Recent incidents underscore the urgency of addressing these issues. This comprehensive survey investigates the implications of GenAI integration in workplaces, focusing on its impact on organizational operations and security. We analyze vulnerabilities within GenAI systems, threats they face, and repercussions of AI-driven workplace monitoring. By examining diverse attack vectors like model attacks and automated cyberattacks, we expose their potential to undermine data integrity and privacy. Unlike previous works, this survey specifically focuses on the security and privacy implications of GenAI within workplace settings, addressing issues like employee monitoring, deepfakes , and regulatory compliance. We delve into emerging threats during model training and usage phases, proposing countermeasures such as differential privacy for training data and robust authentication for access control. Additionally, we provide a comprehensive analysis of evolving regulatory frameworks governing AI tools globally. Based on our comprehensive analysis, we propose targeted recommendations for future research and policy-making to promote responsible and secure adoption of GenAI in the workplace, such as incentivizing the development of explainable AI (XAI) and establishing clear guidelines for ethical data usage. This survey equips stakeholders with a comprehensive understanding of GenAI’s complex workplace landscape, empowering them to harness its benefits responsibly while mitigating risks.

Space anomaly detection plays a critical role in safeguarding the integrity and reliability of space systems amid the rising tide of threats. This survey aims to deepen comprehension of space cyber threats through space threat modeling, and meticulously examine the unique challenges of space anomaly detection. The survey identifies scalability, real-time detection, limited labeled data availability, concept drift, and adversarial attacks as key challenges based on thorough literature analysis and synthesis. By extensively exploring state-of-the-art anomaly detection techniques, the study evaluates their applicability, strengths, and limitations within space networks. Going beyond analysis, a notable contribution of this work involves integrating stream-based and graph-based methods, tailored to capture the intricate temporal and structural relationships inherent in space networks. This innovative hybrid approach holds promise for heightened detection accuracy and sets the stage for future research endeavors. As space threats continue evolving in both number and sophistication, this survey timely provides insights, recommendations, and a clear roadmap for researchers, engineers, and practitioners to fortify space anomaly detection mechanisms.

Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced security, immutability, and traceability, it simultaneously raises pertinent concerns surrounding privacy and transparency. The integration of zero-knowledge proof (ZKP) technology has emerged as a promising solution, particularly in enhancing privacy within the transparent blockchain ecosystem. Our paper conducts an exhaustive survey of the existing literature, with a particular focus on the assimilation of ZKP technology into blockchain for the secure sharing of user identities. We undertake a critical evaluation of the advancements achieved in this domain, pinpoint the formidable challenges that must be confronted, and uncover nascent opportunities for further exploration. Our contribution transcends the realms of mere summarization and analysis; we go a step further by offering recommendations drawn from real-world case studies and delineating future research directions.

Smart home applications are becoming increasingly popular due to their ability to provide safety, comfort, and remote assistance. These applications are usually controlled using a smart home controller, which is often the target of malware attacks. A successful attack may result in financial loss, disclosure of personal and/or sensitive information, or even loss of human lives. Although existing research has employed machine learning models to detect various malware attacks in smart home systems, they haven’t directly tackled the issue of class imbalance in this domain. In addition, the use of ensemble learners is expected to provide improved performance. To address this, we investigated different oversampling techniques to increase the number of samples in the minority classes and incorporated ensemble learners to see their impact on the prediction performance. Experimental evaluation indicates a marked enhancement of 4-5% across metrics, encompassing accuracy, precision, recall, and the F-1 score.

Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents' demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7 ∼ 32%) delivery performance.

Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach.

Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships.

Smart mobile devices have become immensely popular among the people worldwide and provide a new platform for generating and sharing contents. The centralized and hybrid architectures for content sharing require constant Internet connection, increase traffic and incur costs. To address these issues several content sharing approaches have been proposed using the decentralized architecture. Most of the proposed approaches uses patio-temporal regularity and pre-existing social relationships of the users to predict their movements and facilitate content sharing. However, there are scenarios such as visiting hotspots where regular movement patterns or established social relationships among people might not exist. Content sharing in such scenarios has not been addressed yet in literature and existing prediction based approaches are ineffectual. This study focuses on facilitating content sharing in the afore-mentioned scenarios. We take account of user interests, recommendations from online social networks, hotspot specific activities and other relevant information to construct communities which facilitate content sharing. For each community an administrator, who maintains content and member lists and render directory services, is selected based on stay probability, interest score, battery lifetime and device configuration. Simulation results show that our proposed approach attains high content hit and success rate and low latency in delivery which is nearly comparable to those proposed for scenarios with regular predictable movement patterns reported in literature.