Relevant bibliographies by topics / ICS malware

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'ICS malware'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "ICS malware"

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Myung, Jin-woo, and Sunghyuck Hong. "ICS malware Triton attack and countermeasures." IJEMR 3, no. 2 (2019): 13–17. http://dx.doi.org/10.22662/ijemr.2019.3.2.013.

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Camargo, Otávio Augusto Maciel, Julio Cesar Duarte, Anderson Fernandes Pereira Dos Santos, and Cesar Augusto Borges. "A Review of Testbeds on SCADA Systems with Malware Analysis." Revista de Informática Teórica e Aplicada 29, no. 2 (2022): 84–94. http://dx.doi.org/10.22456/2175-2745.112813.

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Supervisory control and data acquisition (SCADA) systems are among the major types of Industrial Control Systems (ICS) and are responsible for monitoring and controlling essential infrastructures such as power generation, water treatment, and transportation. Very common and with high added-value, these systems have malware as one of their main threats, and due to their characteristics, it is practically impossible to test the security of a system without compromising it, requiring simulated test platforms to verify their cyber resilience. This review will discuss the most recent studies on ICS
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Miraglia, Armando, and Matteo Casenove. "Fight fire with fire: the ultimate active defence." Information & Computer Security 24, no. 3 (2016): 288–96. http://dx.doi.org/10.1108/ics-01-2015-0004.

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Purpose This paper proposes an approach to deal with malware and botnets, which in recent years have become one of the major threats in the cyber world. These malicious pieces of software can cause harm not only to the infected victims but also to actors at a much larger scale. For this reason, defenders, namely, security researchers and analysts, and law enforcement have fought back and contained the spreading infections. However, the fight is fundamentally asymmetric. Design/methodology/approach In this paper, the authors argue the need to equip defenders with more powerful active defence to
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Huang, Yuchen, Jingwen Liu, Xuanyi Xiang, et al. "Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN." Sensors 24, no. 17 (2024): 5518. http://dx.doi.org/10.3390/s24175518.

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Industrial Control Systems (ICSs) have faced a significant increase in malware threats since their integration with the Internet. However, existing machine learning-based malware identification methods are not specifically optimized for ICS environments, resulting in suboptimal identification performance. In this work, we propose an innovative method explicitly tailored for ICSs to enhance the performance of malware classifiers within these systems. Our method integrates the opcode2vec method based on preprocessed features with a conditional variational autoencoder–generative adversarial netwo
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Tari Yvonne Elete. "Impact of ransomware on industrial control systems in the oil and gas sector: Security challenges and strategic mitigations." Computer Science & IT Research Journal 5, no. 12 (2024): 2664–81. https://doi.org/10.51594/csitrj.v5i12.1759.

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Ransomware attacks significantly threaten Industrial Control Systems (ICS) within the oil and gas industry, endangering operational continuity and cybersecurity. This research investigates the vulnerabilities that allow ransomware to infiltrate ICS, focusing on common attack vectors, delivery mechanisms, and the subsequent impacts on operations and financial stability. The analysis draws from notable case studies, including the Colonial Pipeline and TRITON malware incidents, highlighting how interconnected IT and Operational Technology (OT) systems exacerbate risks. The findings underscore the
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Zhao, Jianming, Ziwen Jin, Peng Zeng, Chuan Sheng, and Tianyu Wang. "An Anomaly Detection Method for Oilfield Industrial Control Systems Fine-Tuned Using the Llama3 Model." Applied Sciences 14, no. 20 (2024): 9169. http://dx.doi.org/10.3390/app14209169.

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The device anomaly detection in an industrial control system (ICS) is essential for identifying devices with abnormal operating states or unauthorized access, aiming to protect the ICS from unauthorized access, malware, operational errors, and hardware failures. This paper addresses the issues of numerous manufacturers, complex models, and incomplete information by proposing a fingerprint extraction method based on ICS protocol communication models, applied to an anomaly detection model fine-tuned using the Llama3 model. By considering both hardware and software characteristics of ICS devices,
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Kirishikesan, Kannan, Gayakantha Jayakody, Ayesh Hallawaarachchi, and Chandana Gamage. "A High-interaction Physics-aware ICS Honeypot for Industrial Environments." International Journal on Advances in ICT for Emerging Regions (ICTer) 16, no. 2 (2023): 31–39. http://dx.doi.org/10.4038/icter.v16i2.7265.

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Industrial Control Systems (ICSs) are control systems that automate and control industrial processes. ICSs have a high-security risk since most of them are connected to the Internet for remote monitoring and controlling purposes. Compromising ICS can disrupt critical infrastructure supplies, such as water supply, power supply, transportation systems, and manufacturing systems. Programmable Logic Controllers (PLCs) are special computers used in ICSs. Many PLCs do not have built-in security systems. Many ICS application layer protocols are not designed with security in mind. Therefore, external
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Bajwa, Ammar, Aleem Al Razee Tonoy, Sohel Rana, and Ishtiaque Ahmed. "CYBERSECURITY IN INDUSTRIAL CONTROL SYSTEMS: A SYSTEMATIC LITERATURE REVIEW ON AI-BASED THREAT DETECTION FOR SCADA AND IOT NETWORKS." ASRC Procedia: Global Perspectives in Science and Scholarship 01, no. 01 (2025): 01–15. https://doi.org/10.63125/1cr1kj17.

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The increasing integration of Industrial Control Systems (ICS) with Internet of Things (IoT) technologies and Supervisory Control and Data Acquisition (SCADA) networks has brought unparalleled efficiency and automation to critical infrastructure sectors, including energy, water, manufacturing, and transportation. However, this digital convergence has also significantly expanded the cyber threat landscape, making ICS more vulnerable to sophisticated cyberattacks. This systematic literature review critically examines the role of Artificial Intelligence (AI)-based techniques in enhancing threat d
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Gokhale, Sheetal, Ashwini Dalvi, and Irfan Siddavatam. "Industrial Control Systems Honeypot: A Formal Analysis of Conpot." International Journal of Computer Network and Information Security 12, no. 6 (2020): 44–56. http://dx.doi.org/10.5815/ijcnis.2020.06.04.

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Technologies used in ICS and Smart Grid are overlapping. The most discussed attacks on ICSs are Stuxnet and Black energy malware. The anatomy of these attacks not only pointed out that the security of ICS is of prime concern but also demanded to execute a proactive approach in practicing ICS security. Honeypot is used to implement defensive measures for security. The Honeynet group released Honeypot for ICS labelled as Conpot in 2013. Though the Conpot is low interactive Honeypot, it emulates processes of different cyber-physical systems, typically Smart Grid. In the literature, the effectiven
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Jain, Kratika. "Cybersecurity in Industrial Control Systems: Strategies for Protecting Critical Infrastructure." International Journal of Research in Modern Engineering & Emerging Technology 10, no. 11 (2022): 25–32. https://doi.org/10.63345/ijrmeet.org.v10.i11.4.

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Industrial Control Systems (ICS) underpin the operation of critical infrastructure such as power grids, water treatment plants, and manufacturing facilities. The increasing convergence of Operational Technology (OT) and Information Technology (IT) has exposed ICS to sophisticated cyber threats, necessitating robust defense strategies. This manuscript presents a comprehensive analysis of cybersecurity challenges in ICS, reviews existing protective measures up to 2022, and evaluates their effectiveness through a mixed‑methods approach combining expert surveys with incident data analysis. A stati
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Dissertations / Theses on the topic "ICS malware"

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Eriksson, Alma, and Oskar Lindh. "Modellering av en cyberattack på ett industriellt säkerhetssystem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295620.

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Stuxnet, Havex, BlackEnergy, Crashoverride, and now Triton/Trisis are all examples of cyber security incidents where industrial systems were targeted. The incident Triton/Trisis is new in it’s kind, as the attacker got all the way into the safety industrial system of an oil and gas refinery. Even if the final goal of the attack is still unknown the attacker had the power to put human life directly at risk. Details of the attack are still unknown and research and reverse engineering is still going on of the attack. The purpose of this study is to create an attack graph of the case. By collectin
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Elish, Karim Omar Mahmoud. "User-Intention Based Program Analysis for Android Security." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/54943.

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The number of mobile applications (i.e., apps) is rapidly growing, as the mobile computing becomes an integral part of the modern user experience. Malicious apps have infiltrated open marketplaces for mobile platforms. These malicious apps can exfiltrate user's private data, abuse of system resources, or disrupting regular services. Despite the recent advances on mobile security, the problem of detecting vulnerable and malicious mobile apps with high detection accuracy remains an open problem. In this thesis, we address the problem of Android security by presenting a new quantitative program
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Book chapters on the topic "ICS malware"

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Cardwell, Kevin. "Analyzing Encoding, Obfuscated, and ICS Malware Traffic." In Tactical Wireshark. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9291-4_9.

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Mekdad, Yassine, Giuseppe Bernieri, Mauro Conti, and Abdeslam El Fergougui. "The Rise of ICS Malware: A Comparative Analysis." In Computer Security. ESORICS 2021 International Workshops. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95484-0_29.

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Hole, Kjell Jørgen. "Robustness to Malware Spreading." In Anti-fragile ICT Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30070-2_8.

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Hole, Kjell Jørgen. "Robustness to Malware Reinfections." In Anti-fragile ICT Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30070-2_9.

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Hole, Kjell Jørgen. "Anti-fragility to Malware Spreading." In Anti-fragile ICT Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30070-2_10.

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Rathore, Hemant, Praneeth B, Sundaraja Sitharama Iyengar, and Sanjay K. Sahay. "Breaking the Anti-malware: EvoAAttack Based on Genetic Algorithm Against Android Malware Detection Systems." In Computational Science – ICCS 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36030-5_43.

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Sehrawat, Navdeep, Piyush Shandilya, Prajjwal Kumar, and Rahul Gupta. "Malware Family Classification Using Music Information Retrieval Techniques." In ICT with Intelligent Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3571-8_23.

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Nandanwar, Adarsh, Hemant Rathore, Sanjay K. Sahay, and Mohit Sewak. "RL-MAGE: Strengthening Malware Detectors Against Smart Adversaries." In Computational Science – ICCS 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36021-3_6.

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Peiser, Stefan Carl, Ludwig Friborg, and Riccardo Scandariato. "JavaScript Malware Detection Using Locality Sensitive Hashing." In ICT Systems Security and Privacy Protection. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58201-2_10.

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Hăjmăşan, Gheorghe, Alexandra Mondoc, Radu Portase, and Octavian Creţ. "Evasive Malware Detection Using Groups of Processes." In ICT Systems Security and Privacy Protection. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58469-0_3.

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Conference papers on the topic "ICS malware"

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Al Balawi, Mohammad, Mohammad Alnabhan, and Mohammed S. Atoum. "Generative AI for Advanced Malware Detection." In 2024 4th Intelligent Cybersecurity Conference (ICSC). IEEE, 2024. https://doi.org/10.1109/icsc63108.2024.10895965.

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Kumar, Sanjeev, Yudhishthira Sapru, Sugandha Sapru, and B. Janet. "Intelligent Malware Detection Using Sentence Transformer and Hybrid Deep Learning Architecture." In 2025 10th International Conference on Signal Processing and Communication (ICSC). IEEE, 2025. https://doi.org/10.1109/icsc64553.2025.10968099.

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El ghabri, Nadia, Elmostafa Belmekki, and Mostafa Bellafkih. "Pre-Trained Deep Learning Models for Malware Image Based Classification and Detection." In 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2024. http://dx.doi.org/10.1109/icds62089.2024.10756501.

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Mekdad, Yassine, Giuseppe Bernieri, Mauro Conti, and Abdeslam El Fergougui. "A threat model method for ICS malware." In CF '21: Computing Frontiers Conference. ACM, 2021. http://dx.doi.org/10.1145/3457388.3458868.

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Hung, Shih-Hao, Chia-Heng Tu, and Chi Wei Yeh. "A Cloud-Assisted Malware Detection Framework for Mobile Devices." In 2016 International Computer Symposium (ICS). IEEE, 2016. http://dx.doi.org/10.1109/ics.2016.0112.

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Schoinianakis, Dimitrios, Norbert Goetze, and Gerald Lehmann. "MDiET: Malware Detection in Encrypted Traffic." In 6th International Symposium for ICS & SCADA Cyber Security Research 2019. BCS Learning & Development, 2019. http://dx.doi.org/10.14236/ewic/icscsr19.4.

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Halvorsen, James, and Julian L. Rrushi. "Target Discovery Differentials for 0-Knowledge Detection of ICS Malware." In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2017. http://dx.doi.org/10.1109/dasc-picom-datacom-cyberscitec.2017.99.

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Suciu, George, Cristiana-Ioana Istrate, Ruxandra Ioana Răducanu, Maria-Cristina Diţu, Octavian Fratu, and Alexandru Vulpe. "Mobile devices forensic platform for malware detection." In 6th International Symposium for ICS & SCADA Cyber Security Research 2019. BCS Learning & Development, 2019. http://dx.doi.org/10.14236/ewic/icscsr19.8.

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Chen, Yi-Ming, An-Chi He, Guo-Chung Chen, and Yu-Chi Liu. "Android malware detection system integrating block feature extraction and multi-head attention mechanism." In 2020 International Computer Symposium (ICS). IEEE, 2020. http://dx.doi.org/10.1109/ics51289.2020.00087.

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Khan, Rafiullah, Peter Maynard, Kieran McLaughlin, David M. Laverty, and Sakir Sezer. "Threat Analysis of BlackEnergy Malware for Synchrophasor based Real-time Control and Monitoring in Smart Grid." In 4th International Symposium for ICS & SCADA Cyber Security Research 2016. BCS Learning & Development, 2016. http://dx.doi.org/10.14236/ewic/ics2016.7.

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