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We present a new model for finding the optimal placement of electric vehicle charging stations across a multi-period time frame so as to maximise electric vehicle adoption. Via the use of advanced discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. Instead of embedding an analytical probability model in the formulation, we adopt a simulation approach and pre-compute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Using the pre-computed error terms to calculate the users covered by each charging station allows for a maximum covering model, for which solutions can be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and GRASP heuristics to obtain good quality solutions more efficiently. Extensive computational results are provided, which compare the maximum covering formulation with the current state-of-the-art, both for exact solutions and the heuristic methods. Keywords: Electric vehicle charging stations, facility location, integer programming, discrete choice models, maximum covering, v3. Accepted-as manuscript. Minor alterations, including a link to the data instances. v2.Major revision, but core results unchanged; Revised models, but Greedy and GRASP are still best methods. Merging and reduction of the section for bilevel model. New sections with additional computational results. Minor notation changes and sentence clarifications

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

Industrial Control Systems (ICSs) are cyber-physical systems that offer attractive targets to threat actors due to the scale of damages, both physical and cyber, that successful exploitation can cause. As such, ICSs often find themselves victims to reconnaissance campaigns - coordinated scanning activity that targets a wide subset of the Internet - that aim to discover vulnerable systems. As these campaigns likely scan broad netblocks of the Internet, some traffic is directed to network telescopes, which are routable, allocated, and unused IP space. In this paper, we explore the threat landscape of ICS devices by analyzing and investigating network telescope traffic. Our network traffic analysis tool takes darknet traffic and generates threat intelligence on scanning campaigns targeting ICSs in the form of campaign fragments, which we leverage in new ways to get more in-depth knowledge of the cybersecurity threats. We investigate the payloads of the identified campaigns using a custom Deep Packet Inspection (DPI) technique to dissect and analyze the packets. We found 13 distinct payload templates and deduced their purpose, and by extension the campaign goals. We use machine learning to classify the sources behind the campaigns and identify threat actors such as botnets, malicious attackers, or researchers, and establish a methodology to rank our campaigns to prioritize our analysis. To conduct our analysis of the threats targeting ICSs, we have leveraged 12.85 TB (330 days) of network traffic received by our observed darknet IP space. Combining these investigative threads, we provide a thorough overview of the threat landscape targeting ICS systems.

Security metric deployment is of immense importance for power utilities especially in future smart grids, which are being actively pursued in smart cities initiatives around the world. Through these metrics, utilities attain real-time awareness of the grid’s security posture. In this paper, we propose a contingency analysis based security evaluation framework for smart grid systems - CASeS. Based on the power flow equations of the grid, communication network characteristics, and concurrent power contingencies, CASeS leverages Markovian Decison Processes (MDP) to quantify the smart grid security. The security index delivered through CASeS emerges as a novel solution for concurrent power contingency consideration, and allows for the preparation of corrective actions to address the quantified grid criticality level. We evaluate CASeS on the standard IEEE 14-Bus and 39-Bus systems. The collected results demonstrate the usefulness of CASeS in quantifying the security of those systems.

The drive for efficient, reliable, green, and connected smart cities has promoted the use of electric vehicles (EVs) as the main future means of transportation. This resulted in a breakthrough in the anticipated number of adopted EVs by the year 2020, and consequently an urge for an available and trustworthy EV charging infrastructure. The diversity of the involved players, the used technologies, the bulk data exchange, and the widespread nature of the charging network give rise to security concerns in the form of message tampering, spoofing, or delaying among others to disconcert the charging service along with the underlying power layer. Furthermore, confidentiality and privacy of user information (i.e. identity, location, payment information, etc.) is another major concern associated with the deployment and use of the charging infrastructure. Thus, there is a need to identify and classify such concerns, and devise suitable solutions for a secure charging infrastructure. In this paper, we present a security assessment of the EV charging infrastructure. We highlight and categorize cyber threats targeting different players in a charging system, along with the security solutions presented in the literature. Finally, we present a gap analysis and insights into future research directions for EV charging system security.

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions.

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. In an effort to satisfy the driver’s well-being and demand for continuous connectivity in the IoV era, this paper addresses both safety and quality-of-service (QoS) concerns in a green, balanced, connected, and efficient vehicular network. Using the recent advances in training deep neural networks, we exploit the deep reinforcement learning model, namely deep Q-network, which learns a scheduling policy from high-dimensional inputs corresponding to the current characteristics of the underlying model. The realized policy serves to extend the lifetime of the battery-powered vehicular network while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform several scheduling benchmarks in terms of completed request percentage (10–25%), mean request delay (10–15%), and total network lifetime (5–65%).

The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures, raising multiple questions related to the power grid security and vulnerability. In this paper, we offer a complete examination of the EV ecosystem from the vulnerability to the attacks and finally the solutions. We start by examining the existing vulnerabilities in the EV ecosystem that can be exploited to control the EV charging and launch attacks against the power grid. We then discuss the non-linear nature of the EV charging load and simulate multiple attacks that can be launched against the power grid using these EVs. EV loads have high reactive power demand which can have a larger impact on the grid compared to residential loads. We perform simulations on two power grids and demonstrate that while the grid can recover after a 48 MW attack utilizing traditional residential loads, a smaller 30 MW EV load attack can completely destabilize the system. Finally, we suggest several patches for the existing vulnerabilities and discuss two methods aimed at detecting EV attacks., Comment: International Journal of Electrical Power & Energy Systems, Available online 21 November 2021, 107784, Full citation details will be added when the final version becomes available

Electric Vehicles (EV) are coming with a stupendous load demand that raises enough concerns for the power sector. The backlash of such increased demand is notable at the distribution side with different aspects of EV usage. During winter, EV users favor preconditioning their vehicles before leaving their houses, such as heating the cabin and battery compartment to make the operation of EVs more comfortable. Consequently, such behavior along with a higher penetration of level 2 smart chargers prompt the presence of a new peak in the residential load profile. This new unexpected peak that operators have to face can disturb the performance of the network. To forsee the impact of preconditioning, we simulate multiple scenarios to assess the network’s quality metrics (voltage level and power losses). We expose that preconditioning poses risks on the network in its current state. Furthermore, we evaluate the competencies of network reconfiguration to handle the new imposed preconditioning demand. We find out that reconfiguration will be able to aid the performance of the network to an average EV penetration rate.