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Our most recent findings on multi-hop LoRa networks imply that earlier developed multi-hop routing methods, such as our previously suggested Extended-DRESG scheme, had overlooked some essential information. Although the researchers employed uniform node distributions for their multi-hop models, a full capacity LoRa network may be treated as a Gaussian distribution. Extended-DRESG performs better with a Gaussian distribution than with a uniform distribution of nodes. In contrast to optimal ring-hop combinations created earlier, this observation led to the suggested algorithm for optimal ring positioning and ring-hop combinations in Extended-DRESG. The Extended-DRESG cluster developing technique, which may produce much superior performance in terms of energy efficiency when uniform node distribution is encountered, is further suggested in light of the observations made in this research. Future multi-hop models can benefit from the current observations in two ways: (i) a better understanding of the packet forwarding structure, and (ii) a good cluster formation technique when clusters are established based on their distances from the gateway.

Recent reports indicate that electric vehicle charging stations (EVCSs) are susceptible to remote exploitation through their vulnerable software/cyber components. More importantly, compromised EVCS can be leveraged to perform coordinated oscillatory load attacks against the interconnected power grid, leading to power grid instability, increased operational costs, and power line tripping. In this paper, an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid is investigated. The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. The proposed detection approach was evaluated by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4%). Moreover, this analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, the detection mechanism of oscillatory load attacks is decentralized and created an effective alternative for operator-centric mechanisms to mitigate multi-operator and Man-in-the-Middle (MitM) oscillatory load attacks against the power grid. Additionally, the created test bed is leveraged to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead. Finally, a security metric is createdto evaluate the resiliency of the ecosystem based on its design and architecture.

The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputational, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized Machine Learning methods. This study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware, and two issues specific to Machine Learning models. Adversarial machine learning exploitation and concept drift have been identified as under-researched areas in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.

Undoubtedly, these days our telecommunication networks are witnessing not only a major spike is data volumes, but also a shift in the mode of communications. Employees, news anchor and students are conducting their daily business and learning activities through online platforms as they shelter homes during this pandemic and this is expected to continue for some time. An overwhelming shift to one-to-many and many-to-many communications is observed and end users expect from their providers efficient, secure and reliable services. Operators of digital platforms are challenged to respond quickly to the rising demand, by enhancing deployability and manageability of their service. Virtualization is a key enabler for enhanced deployability and manageability where virtual functions can be automatically deployed on demand. Another challenge that providers deal with is the individualized requirements by services offered to users which may vary between high reliabilities, low latency, robust security and any combination thereof. This paper considers the problem of provisioning multi-source multicast services where each service consists of a set of in-network virtual functions that must be chained in a particular order to meet the quality of service demanded by end users. We deal with a reliable service where reliability is attained by provisioning backup functions for the service. We first calculate the requirements of VNF backups which account for fewer computing resource consumption. Next, we formulate the multi-source multicast hybrid routing as a Mixed Integer Linear Programming (MILP) and find a solution with optimal VNF placement and traffic routing. We also proposed a K-shortest path-based greedy algorithm to reduce the complexity for solving MILP. Numerical analysis and simulations are conducted to validate the proposed algorithms. Our results show multi-source multicast has a better routing selection compared to single-source multicast due to the more options of multicast sources for providing a reliable network service.

A fully connected vehicular ad hoc network (VANET) establishes a strong foundation for the development of smart cities, where one of the main objectives is the improvement of the welfare of commuting passengers. The availability of a multi-hop path across a VANET system, through vehicle-to-vehicle communication, depends mainly on the vehicular density and the willingness of vehicles to cooperate with one another. This paper proposes to minimize the path availability's dependence on vehicular density and cooperation, by utilizing unmanned aerial vehicles (UAVs). Particularly, this paper explores, both mathematically as well as through an extensive simulation study, the advantages of exploiting UAVs as store-carry-forward nodes so as to enhance the availability of a connectivity path as well as to reduce the end-to-end packet delivery delay. The obtained results shed clear light on the benefits emanating from the coupling of UAVs with vehicles in the context of a highly promising, innovative, and hybrid vehicular networking architecture.