Disrupting the power grid via EV charging: The impact of the SMS Phishing attacks

Soykan, Elif Üstündağ; Bağrıyanık, Mustafa; Soykan, Gürkan

doi:10.1016/j.segan.2021.100477

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…Concerning the former, several papers use real LVDN topology data from different end countries [14][15][16][19][20][21][22]24,32]. However, many research papers lack data and use either synthesized or benchmark LVDNs [17,18,23,[25][26][27]33] or only consider certain LVDN components [34,35]. Furthermore, a wide range of geographical areas are investigated in the existing literature.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Implementation of Low-Cost Electric Vehicles (EVs) Supercharger: A Comprehensive Review

Rahman,

Tanvir,

Islam

et al. 2024

IJANS

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This article presents a probabilistic modeling method utilizing smart meter data and an innovative agentbased simulator for electric vehicles (EVs). The aim is to assess the effects of different cost-driven EV charging strategies on the power distribution network (PDN). We investigate the effects of a 40% EV adoption on three parts of Frederiksberg's low voltage distribution network (LVDN), a densely urbanized municipality in Denmark. Our findings indicate that cable and transformer overloading especially pose a challenge. However, the impact of EVs varies significantly between each LVDN area and charging scenario. Across scenarios and LVDNs, the share of cables facing congestion ranges between 5% and 60%. It is also revealed that time-of-use (ToU)-based and single-day cost-minimized charging could be beneficial for LVDNs with moderate EV adoption rates. In contrast, multiple-day optimization will likely lead to severe congestion, as such strategies concentrate demand on a single day that would otherwise be distributed over several days, thus raising concerns about how to prevent it. The broader implications of our research suggest that, despite initial worries primarily centered on congestion due to unregulated charging during peak hours, a transition to cost-based smart charging, propelled by an increasing awareness of time-dependent electricity prices, may lead to a significant rise in charging synchronization, bringing about undesirable consequences for the power distribution network (PDN).

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Section: Literature Reviewmentioning

confidence: 99%

Design and Implementation of Low-Cost Electric Vehicles (EVs) Supercharger: A Comprehensive Review

Rahman,

Tanvir,

Islam

et al. 2024

IJANS

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“…For example, an attacker impersonates a trusted sender, such as a bank, to send important messages such as a notice that personal information has been stolen. Smishing has affected the electric vehicle transition [50] and sustainable power grid program [51]. Some of the reasons for the recent worldwide prevalence of smishing are as follows: (1) The number of mobile device users has increased.…”

Section: Background a Smishingmentioning

confidence: 99%

Smishing Strategy Dynamics and Evolving Botnet Activities in Japan

Saeki

Kitayama

Koga³

et al. 2022

IEEE Access

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XLoader and FakeSpy, the two major smishing botnets targeting Japan, change their attack strategies over various timescales. Based on recent observations of the botnets and Twitter data, we present empirical facts about their strategies and activity patterns and applied some of these strategic and activity patterns to malware detection and malicious domain detection. All the proposed methods yielded small false positive and negative rates, and are expected to run on user devices owing to their small computational cost. Recent malware detection methods based on traffic analysis extract TCP/IP traffic features if the upper layers of TCP are encrypted. In this study, Frida's hooking capability was employed to decode the upper layers (WebSocket and JSON-RPC) to create a list of all commands flowing over the botnet channel. The command-level traffic analysis presents decisive attack features because commands are transmitted according to strategies developed by the attackers. The proposed malicious domain detection method, on the other hand, exploited the tendency of the attackers to create domains in batches. Previous researchers focused on how benign and malicious domains were registered and used on the name servers. The proposed method, on the other hand, focuses on the arrival rate of SMS messages with URL links. The error rates become significantly small when users do not receive such messages very often.

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“…In [12,13], the authors highlighted phishing attacks targeting smart grids to launch a phishing attack and compromise the system data to roll back the system. e authors proposed a data-mining technique to identify and counter phishing attacks and falsify fake pages and messages.…”

Section: Literature Reviewmentioning

confidence: 99%

Cluster-Based Antiphishing (CAP) Model for Smart Phones

Faisal

Abed

2021

Scientific Programming

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Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

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Disrupting the power grid via EV charging: The impact of the SMS Phishing attacks

Cited by 17 publications

References 14 publications

Design and Implementation of Low-Cost Electric Vehicles (EVs) Supercharger: A Comprehensive Review

Design and Implementation of Low-Cost Electric Vehicles (EVs) Supercharger: A Comprehensive Review

Smishing Strategy Dynamics and Evolving Botnet Activities in Japan

Cluster-Based Antiphishing (CAP) Model for Smart Phones

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