Visible to the public Biblio

Filters: Keyword is cybersecurity threats  [Clear All Filters]
2021-01-25
Merouane, E. M., Escudero, C., Sicard, F., Zamai, E..  2020.  Aging Attacks against Electro-Mechanical Actuators from Control Signal Manipulation. 2020 IEEE International Conference on Industrial Technology (ICIT). :133–138.
The progress made in terms of controller technologies with the introduction of remotely-accessibility capacity in the digital controllers has opened the door to new cybersecurity threats on the Industrial Control Systems (ICSs). Among them, some aim at damaging the ICS's physical system. In this paper, a corrupted controller emitting a non-legitimate Pulse Width Modulation control signal to an Electro-Mechanical Actuator (EMA) is considered. The attacker's capabilities for accelerating the EMA's aging by inducing Partial Discharges (PDs) are investigated. A simplified model is considered for highlighting the influence of the carrier frequency of the control signal over the amplitude and the repetition of the PDs involved in the EMA's aging.
2020-12-17
Promyslov, V., Semenkov, K..  2020.  Security Threats for Autonomous and Remotely Controlled Vehicles in Smart City. 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). :1—5.

The paper presents a comprehensive model of cybersecurity threats for a system of autonomous and remotely controlled vehicles (AV) in the environment of a smart city. The main focus in the security context is given to the “integrity” property. That property is of higher importance for industrial control systems in comparison with other security properties (availability and confidentiality). The security graph, which is part of the model, is dynamic, and, in real cases, its analysis may require significant computing resources for AV systems with a large number of assets and connections. The simplified example of the security graph for the AV system is presented.

2020-11-04
Rajamäki, J., Nevmerzhitskaya, J., Virág, C..  2018.  Cybersecurity education and training in hospitals: Proactive resilience educational framework (Prosilience EF). 2018 IEEE Global Engineering Education Conference (EDUCON). :2042—2046.

Healthcare is a vital component of every nation's critical infrastructure, yet it is one of the most vulnerable sector for cyber-attacks. To enforce the knowledge on information security processes and data protection procedures, educational and training schemes should be establishedfor information technology (IT) staff working in healthcare settings. However, only training IT staff is not enough, as many of cybersecurity threats are caused by human errors or lack of awareness. Current awareness and training schemes are often implemented in silos, concentrating on one aspect of cybersecurity at a time. Proactive Resilience Educational Framework (Prosilience EF) provides a holistic cyber resilience and security framework for developing and delivering a multilateral educational and training scheme based on a proactive approach to cybersecurity. The framework is built on the principle that education and training must be interactive, guided, meaningful and directly relevant to the user' operational environment. The framework addresses capacity mapping, cyber resilience level measuring, utilizing available and mapping missing resources, adaptive learning technologies and dynamic content delivery. Prosilience EF launches an iterative process of awareness and training development with relevant stakeholders (end users - hospitals, healthcare authorities, cybersecurity training providers, industry members), evaluating the framework via joint exercises/workshops andfurther developing the framework.

2020-10-12
Foroughi, Farhad, Luksch, Peter.  2018.  Observation Measures to Profile User Security Behaviour. 2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). :1–6.
Recognising user behaviour in real time is an important element of providing appropriate information and help to take suitable action or decision regarding cybersecurity threats. A user's security behaviour profile is a set of structured data and information to describe a user in an interactive environment between the user and computer. The first step for behaviour profiling is user behaviour model development including data collection. The data collection should be transparent as much as possible with minimum user interaction. Monitoring individual actions to obtain labelled training data is less costly and more effective in creating a behaviour profile. The most challenging issue in computer user security can be identifying suitable data. This research aims to determine required observation measures to capture user-system interactions to understand user's behaviour and create a user profile for cybersecurity purposes.
2020-02-26
Diahovchenko, Illia, Kandaperumal, Gowtham, Srivastava, Anurag.  2019.  Distribution Power System Resiliency Improvement Using Distributed Generation and Automated Switching. 2019 IEEE 6th International Conference on Energy Smart Systems (ESS). :126–131.

The contemporary power distribution system is facing an increase in extreme weather events, cybersecurity threats and even physical threats such as terrorism. Therefore there is a growing interest towards resiliency estimation and improvement. In this paper the resiliency enhancement strategy by means of Distributed Energy Resources and Automated Switches is presented. Resiliency scores are calculated using Analytical Hierarchy Process. The developed algorithm was validated on the modified IEEE 123 node system. It provides the most resiliency feasible network that satisfies the primary goal of serving the critical loads.

Crouch, Alfred L, Ley, Adam W.  2019.  A Role for Embedded Instrumentation in Real-Time Hardware Assurance and Online Monitoring against Cybersecurity Threats. 2019 IEEE AUTOTESTCON. :1–9.

Jeopardy to cybersecurity threats in electronic systems is persistent and growing. Such threats present in hardware, by means such as Trojans and counterfeits, and in software, by means such as viruses and other malware. Against such threats, we propose a range of embedded instruments that are capable of real-time hardware assurance and online monitoring.

Sokolov, S. A., Iliev, T. B., Stoyanov, I. S..  2019.  Analysis of Cybersecurity Threats in Cloud Applications Using Deep Learning Techniques. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). :441–446.

In this paper we present techniques based on machine learning techniques on monitoring data for analysis of cybersecurity threats in cloud environments that incorporate enterprise applications from the fields of telecommunications and IoT. Cybersecurity is a term describing techniques for protecting computers, telecommunications equipment, applications, environments and data. In modern networks enormous volume of generated traffic can be observed. We propose several techniques such as Support Vector Machines, Neural networks and Deep Neural Networks in combination for analysis of monitoring data. An approach for combining classifier results based on performance weights is proposed. The proposed approach delivers promising results comparable to existing algorithms and is suitable for enterprise grade security applications.

2019-02-13
Mamun, A. Al, Mamun, M. Abdullah Al, Shikfa, A..  2018.  Challenges and Mitigation of Cyber Threat in Automated Vehicle: An Integrated Approach. 2018 International Conference of Electrical and Electronic Technologies for Automotive. :1–6.
The technological development of automated vehicles opens novel cybersecurity threats and risks for road safety. Increased connectivity often results in increased risks of a cyber-security attacks, which is one of the biggest challenges for the automotive industry that undergoes a profound transformation. State of the art studies evaluated potential attacks and recommended possible measures, from technical and organizational perspective to face these challenges. In this position paper, we review these techniques and methods and show that some of the different solutions complement each other while others overlap or are even incompatible or contradictory. Based on this gap analysis, we advocate for the need of a comprehensive framework that integrates technical and organizational mitigation measures to enhance the cybersecurity of automotive vehicles.
2019-02-08
Sisiaridis, D., Markowitch, O..  2018.  Reducing Data Complexity in Feature Extraction and Feature Selection for Big Data Security Analytics. 2018 1st International Conference on Data Intelligence and Security (ICDIS). :43-48.

Feature extraction and feature selection are the first tasks in pre-processing of input logs in order to detect cybersecurity threats and attacks by utilizing data mining techniques in the field of Artificial Intelligence. When it comes to the analysis of heterogeneous data derived from different sources, these tasks are found to be time-consuming and difficult to be managed efficiently. In this paper, we present an approach for handling feature extraction and feature selection utilizing machine learning algorithms for security analytics of heterogeneous data derived from different network sensors. The approach is implemented in Apache Spark, using its python API, named pyspark.

2017-12-12
Gamachchi, A., Boztas, S..  2017.  Insider Threat Detection Through Attributed Graph Clustering. 2017 IEEE Trustcom/BigDataSE/ICESS. :112–119.

While most organizations continue to invest in traditional network defences, a formidable security challenge has been brewing within their own boundaries. Malicious insiders with privileged access in the guise of a trusted source have carried out many attacks causing far reaching damage to financial stability, national security and brand reputation for both public and private sector organizations. Growing exposure and impact of the whistleblower community and concerns about job security with changing organizational dynamics has further aggravated this situation. The unpredictability of malicious attackers, as well as the complexity of malicious actions, necessitates the careful analysis of network, system and user parameters correlated with insider threat problem. Thus it creates a high dimensional, heterogeneous data analysis problem in isolating suspicious users. This research work proposes an insider threat detection framework, which utilizes the attributed graph clustering techniques and outlier ranking mechanism for enterprise users. Empirical results also confirm the effectiveness of the method by achieving the best area under curve value of 0.7648 for the receiver operating characteristic curve.