The operation and control of the next generation electrical grids will depend on a complex network of computers, software, and communication technologies. Being compromised by a malicious adversary would cause significant damage, including extended power outages and destruction of electrical equipment. Moreover, the implementation of the smart grid will include the deployment of many new enabling technologies such as advanced sensors and metering, and the integration of distributed generation resources. Such technologies and various others will require the addition and utilization of multiple communication mechanisms and infrastructures that may suffer from serious cyber vulnerabilities. These need to be addressed in order to increase the security and thus the greatest adoption and success of the smart grid. In this article, we focus on the communication security aspect, which deals with the distribution component of the smart grid. Consequently, we target the network security of the advanced metering infrastructure coupled with the data communication toward the transmission infrastructure. We discuss the security and feasibility aspects of possible communication mechanisms that could be adopted on that subpart of the grid. By accomplishing this, the correlated vulnerabilities in these systems could be remediated, and associated risks may be mitigated for the purpose of enhancing the cyber security of the future electric grid.

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. We further discuss the challenges found such as the ones related to obtaining ransomware datasets. In addition, our study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware. Adversarial machine learning exploitation has been identified as an under-researched area in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.

Today, the Internet security community largely emphasizes cyberspace monitoring for the purpose of generating cyber intelligence. In this paper, we present a survey on darknet. The latter is an effective approach to observe Internet activities and cyber attacks via passive monitoring. We primarily define and characterize darknet and indicate its alternative names. We further list other trap-based monitoring systems and compare them to darknet. Moreover, in order to provide realistic measures and analysis of darknet information, we report case studies, namely, Conficker worm in 2008 and 2009, Sality SIP scan botnet in 2011, and the largest amplification attack in 2014. Finally, we provide a taxonomy in relation to darknet technologies and identify research gaps that are related to three main darknet categories: deployment, traffic analysis, and visualization. Darknet projects are found to monitor various cyber threat activities and are distributed in one third of the global Internet. We further identify that Honeyd is probably the most practical tool to implement darknet sensors, and future deployment of darknet will include mobile-based VOIP technology. In addition, as far as darknet analysis is considered, computer worms and scanning activities are found to be the most common threats that can be investigated throughout darknet; Code Red and Slammer/Sapphire are the most analyzed worms. Furthermore, our study uncovers various lacks in darknet research. For instance, less than 1% of the contributions tackled distributed reflection denial of service (DRDoS) amplification investigations, and at most 2% of research works pinpointed spoofing activities. Last but not least, our survey identifies specific darknet areas, such as IPv6 darknet, event monitoring, and game engine visualization methods that require a significantly greater amount of attention from the research community.

Distributed Denial of Service (DDoS) activities continue to dominate today's attack landscape. This work proposes a DDoS forecasting model to provide significant insights to organizations, security operators and emergency response teams during and after a targeted DDoS attack. Specifically, the work strives to predict, within minutes, the attacks' impact features, namely, intensity/rate (packets/sec) and size (estimated number of used compromised machines/bots). The goal is to understand the future short term trend of the ongoing DDoS attack in terms of those features and thus provide the capability to recognize the current as well as future similar situations and hence appropriately respond to the threat. Our analysis employs real dark net data to explore the feasibility of applying the forecasting model on targeted DDoS attacks and subsequently evaluate the accuracy of the predictions. To achieve its tasks, our proposed approach leverages a number of time series fluctuation analysis and forecasting methods. The extracted inferences from various DDoS case studies exhibit promising accuracy reaching at some points less than 1% error rate. Further, our model could lead to better understanding of the scale and speed of DDoS attacks and should generate inferences that could be adopted for immediate response and hence mitigation as well as accumulated for the purpose of long term large-scale DDoS analysis.