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Susukailo, Vitalii, Opirskyy, Ivan, Vasylyshyn, Sviatoslav.  2020.  Analysis of the attack vectors used by threat actors during the pandemic. 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT). 2:261—264.

This article describes attacks methods, vectors and technics used by threat actors during pandemic situations in the world. Identifies common targets of threat actors and cyber-attack tactics. The article analyzes cybersecurity challenges and specifies possible solutions and improvements in cybersecurity. Defines cybersecurity controls, which should be taken against analyzed attack vectors.

Kushal, T. R. B., Gao, Z., Wang, J., Illindala, M. S..  2020.  Causal Chain of Time Delay Attack on Synchronous Generator Control. 2020 IEEE Power Energy Society General Meeting (PESGM). :1—5.

Wide integration of information and communication technology (ICT) in modern power grids has brought many benefits as well as the risk of cyber attacks. A critical step towards defending grid cyber security is to understand the cyber-physical causal chain, which describes the progression of intrusion in cyber-space leading to the formation of consequences on the physical power grid. In this paper, we develop an attack vector for a time delay attack at load frequency control in the power grid. Distinct from existing works, which are separately focused on cyber intrusion, grid response, or testbed validation, the proposed attack vector for the first time provides a full cyber-physical causal chain. It targets specific vulnerabilities in the protocols, performs a denial-of-service (DoS) attack, induces the delays in control loop, and destabilizes grid frequency. The proposed attack vector is proved in theory, presented as an attack tree, and validated in an experimental environment. The results will provide valuable insights to develop security measures and robust controls against time delay attacks.

Rodriguez, German, Torres, Jenny, Flores, Pamela, Benavides, Eduardo, Nuñez-Agurto, Daniel.  2019.  XSStudent: Proposal to Avoid Cross-Site Scripting (XSS) Attacks in Universities. 2019 3rd Cyber Security in Networking Conference (CSNet). :142–149.
QR codes are the means to offer more direct and instant access to information. However, QR codes have shown their deficiency, being a very powerful attack vector, for example, to execute phishing attacks. In this study, we have proposed a solution that allows controlling access to the information offered by QR codes. Through a scanner designed in APP Inventor which has been called XSStudent, a system has been built that analyzes the URLs obtained and compares them with a previously trained system. This study was executed by means of a controlled attack to the users of the university who through a flyer with a QR code and a fictional link accessed an infected page with JavaScript code that allowed a successful cross-site scripting attack. The results indicate that 100% of the users are vulnerable to this type of attacks, so also, with our proposal, an attack executed in the universities using the Beef software would be totally blocked.
Gayathri, Bhimavarapu, Yammani, Chandrasekhar.  2019.  Multi-Attacking Strategy on Smart Grid with Incomplete Network Information. 2019 8th International Conference on Power Systems (ICPS). :1—5.

The chances of cyber-attacks have been increased because of incorporation of communication networks and information technology in power system. Main objective of the paper is to prove that attacker can launch the attack vector without the knowledge of complete network information and the injected false data can't be detected by power system operator. This paper also deals with analyzing the impact of multi-attacking strategy on the power system. This false data attacks incurs lot of damage to power system, as it misguides the power system operator. Here, we demonstrate the construction of attack vector and later we have demonstrated multiple attacking regions in IEEE 14 bus system. Impact of attack vector on the power system can be observed and it is proved that the attack cannot be detected by power system operator with the help of residue check method.

Duncan, Adrian, Creese, Sadie, Goldsmith, Michael.  2019.  A Combined Attack-Tree and Kill-Chain Approach to Designing Attack-Detection Strategies for Malicious Insiders in Cloud Computing. 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1—9.

Attacks on cloud-computing services are becoming more prevalent with recent victims including Tesla, Aviva Insurance and SIM-card manufacturer Gemalto[1]. The risk posed to organisations from malicious insiders is becoming more widely known about and consequently many are now investing in hardware, software and new processes to try to detect these attacks. As for all types of attack vector, there will always be those which are not known about and those which are known about but remain exceptionally difficult to detect - particularly in a timely manner. We believe that insider attacks are of particular concern in a cloud-computing environment, and that cloud-service providers should enhance their ability to detect them by means of indirect detection. We propose a combined attack-tree and kill-chain based method for identifying multiple indirect detection measures. Specifically, the use of attack trees enables us to encapsulate all detection opportunities for insider attacks in cloud-service environments. Overlaying the attack tree on top of a kill chain in turn facilitates indirect detection opportunities higher-up the tree as well as allowing the provider to determine how far an attack has progressed once suspicious activity is detected. We demonstrate the method through consideration of a specific type of insider attack - that of attempting to capture virtual machines in transit within a cloud cluster via use of a network tap, however, the process discussed here applies equally to all cloud paradigms.

Mishra, Narendra, Singh, R K.  2019.  Taxonomy Analysis of Cloud Computing Vulnerabilities through Attack Vector, CVSS and Complexity Parameter. 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). 1:1—8.

The world is witnessing an exceptional expansion in the cloud enabled services which is further growing day by day due to advancement & requirement of technology. However, the identification of vulnerabilities & its exploitation in the cloud computing will always be the major challenge and concern for any cloud computing system. To understand the challenges and its consequences and further provide mitigation techniques for the vulnerabilities, the identification of cloud specific vulnerabilities needs to be examined first and after identification of vulnerabilities a detailed taxonomy must be positioned. In this paper several cloud specific identified vulnerabilities have been studied which is listed by the NVD, ENISA CSA etc accordingly a unified taxonomy for security vulnerabilities has been prepared. In this paper we proposed a comprehensive taxonomy for cloud specific vulnerabilities on the basis of several parameters like attack vector, CVSS score, complexity etc which will be further act as input for the analysis and mitigation of cloud vulnerabilities. Scheming of Taxonomy of vulnerabilities is an effective way for cloud administrators, cloud mangers, cloud consumers and other stakeholders for identifying, understanding and addressing security risks.

Efendy, Rezky Aulia, Almaarif, Ahmad, Budiono, Avon, Saputra, Muhardi, Puspitasari, Warih, Sutoyo, Edi.  2019.  Exploring the Possibility of USB based Fork Bomb Attack on Windows Environment. 2019 International Conference on ICT for Smart Society (ICISS). 7:1—4.

The need for data exchange and storage is currently increasing. The increased need for data exchange and storage also increases the need for data exchange devices and media. One of the most commonly used media exchanges and data storage is the USB Flash Drive. USB Flash Drive are widely used because they are easy to carry and have a fairly large storage. Unfortunately, this increased need is not directly proportional to an increase in awareness of device security, both for USB flash drive devices and computer devices that are used as primary storage devices. This research shows the threats that can arise from the use of USB Flash Drive devices. The threat that is used in this research is the fork bomb implemented on an Arduino Pro Micro device that is converted to a USB Flash drive. The purpose of the Fork Bomb is to damage the memory performance of the affected devices. As a result, memory performance to execute the process will slow down. The use of a USB Flash drive as an attack vector with the fork bomb method causes users to not be able to access the operating system that was attacked. The results obtained indicate that the USB Flash Drive can be used as a medium of Fork Bomb attack on the Windows operating system.

Robic-Butez, Pierrick, Win, Thu Yein.  2019.  Detection of Phishing websites using Generative Adversarial Network. 2019 IEEE International Conference on Big Data (Big Data). :3216—3221.

Phishing is typically deployed as an attack vector in the initial stages of a hacking endeavour. Due to it low-risk rightreward nature it has seen a widespread adoption, and detecting it has become a challenge in recent times. This paper proposes a novel means of detecting phishing websites using a Generative Adversarial Network. Taking into account the internal structure and external metadata of a website, the proposed approach uses a generator network which generates both legitimate as well as synthetic phishing features to train a discriminator network. The latter then determines if the features are either normal or phishing websites, before improving its detection accuracy based on the classification error. The proposed approach is evaluated using two different phishing datasets and is found to achieve a detection accuracy of up to 94%.

Tychalas, Dimitrios, Keliris, Anastasis, Maniatakos, Michail.  2019.  LED Alert: Supply Chain Threats for Stealthy Data Exfiltration in Industrial Control Systems. 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS). :194–199.

Industrial Internet-of-Things has been touted as the next revolution in the industrial domain, offering interconnectivity, independence, real-time operation, and self-optimization. Integration of smart systems, however, bridges the gap between information and operation technology, creating new avenues for attacks from the cyber domain. The dismantling of this air-gap, in conjunction with the devices' long lifespan -in the range of 20-30 years-, motivates us to bring the attention of the community to emerging advanced persistent threats. We demonstrate a threat that bridges the air-gap by leaking data from memory to analog peripherals through Direct Memory Access (DMA), delivered as a firmware modification through the supply chain. The attack automatically adapts to a target device by leveraging the Device Tree and resides solely in the peripherals, completely transparent to the main CPU, by judiciously short-circuiting specific components. We implement this attack on a commercial Programmable Logic Controller, leaking information over the available LEDs. We evaluate the presented attack vector in terms of stealthiness, and demonstrate no observable overhead on both CPU performance and DMA transfer speed. Since traditional anomaly detection techniques would fail to detect this firmware trojan, this work highlights the need for industrial control system-appropriate techniques that can be applied promptly to installed devices.

Yin, Mingyong, Wang, Qixu, Cao, Mingsheng.  2019.  An Attack Vector Evaluation Method for Smart City Security Protection. 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :1–7.

In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.

Fujdiak, Radek, Blazek, Petr, Mlynek, Petr, Misurec, Jiri.  2019.  Developing Battery of Vulnerability Tests for Industrial Control Systems. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1–5.

Nowadays, the industrial control systems (ICS) face many challenges, where security is becoming one of the most crucial. This fact is caused by new connected environment, which brings among new possibilities also new vulnerabilities, threats, or possible attacks. The criminal acts in the ICS area increased over the past years exponentially, which caused the loss of billions of dollars. This also caused classical Intrusion Detection Systems and Intrusion Prevention Systems to evolve in order to protect among IT also ICS networks. However, these systems need sufficient data such as traffic logs, protocol information, attack patterns, anomaly behavior marks and many others. To provide such data, the requirements for the test environment are summarized in this paper. Moreover, we also introduce more than twenty common vulnerabilities across the ICS together with information about possible risk, attack vector (point), possible detection methods and communication layer occurrence. Therefore, the paper might be used as a base-ground for building sufficient data generator for machine learning and artificial intelligence algorithms often used in ICS/IDS systems.

Warzyński, A., Kołaczek, G..  2018.  Intrusion detection systems vulnerability on adversarial examples. 2018 Innovations in Intelligent Systems and Applications (INISTA). :1–4.

Intrusion detection systems define an important and dynamic research area for cybersecurity. The role of Intrusion Detection System within security architecture is to improve a security level by identification of all malicious and also suspicious events that could be observed in computer or network system. One of the more specific research areas related to intrusion detection is anomaly detection. Anomaly-based intrusion detection in networks refers to the problem of finding untypical events in the observed network traffic that do not conform to the expected normal patterns. It is assumed that everything that is untypical/anomalous could be dangerous and related to some security events. To detect anomalies many security systems implements a classification or clustering algorithms. However, recent research proved that machine learning models might misclassify adversarial events, e.g. observations which were created by applying intentionally non-random perturbations to the dataset. Such weakness could increase of false negative rate which implies undetected attacks. This fact can lead to one of the most dangerous vulnerabilities of intrusion detection systems. The goal of the research performed was verification of the anomaly detection systems ability to resist this type of attack. This paper presents the preliminary results of tests taken to investigate existence of attack vector, which can use adversarial examples to conceal a real attack from being detected by intrusion detection systems.

Dutta, R. G., Guo, Xiaolong, Zhang, Teng, Kwiat, K., Kamhoua, C., Njilla, L., Jin, Y..  2017.  Estimation of safe sensor measurements of autonomous system under attack. 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC). :1–6.
The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.
Rao, K. S., Jain, N., Limaje, N., Gupta, A., Jain, M., Menezes, B..  2016.  Two for the price of one: A combined browser defense against XSS and clickjacking. 2016 International Conference on Computing, Networking and Communications (ICNC). :1–6.
Cross Site Scripting (XSS) and clickjacking have been ranked among the top web application threats in recent times. This paper introduces XBuster - our client-side defence against XSS, implemented as an extension to the Mozilla Firefox browser. XBuster splits each HTTP request parameter into HTML and JavaScript contexts and stores them separately. It searches for both contexts in the HTTP response and handles each context type differently. It defends against all XSS attack vectors including partial script injection, attribute injection and HTML injection. Also, existing XSS filters may inadvertently disable frame busting code used in web pages as a defence against clickjacking. However, XBuster has been designed to detect and neutralize such attempts.