Next-Generation Industrial Control System (ICS) Security:Towards ICS Honeypots for Defence-in-Depth Security

The advent of Industry 4.0 and smart manufacturing has led to an increased convergence of traditional manufacturing and production technologies with IP communications. Legacy Industrial Control System (ICS) devices are now exposed to a wide range of previously unconsidered threats, which must be considered to ensure the safe operation of industrial processes. Especially as cyberspace is presenting itself as a popular domain for nation-state operations, including against critical infrastructure. Honeypots are a well-known concept within traditional IT security, and they can enable a more proactive approach to security, unlike traditional systems. More work needs to be done to understand their usefulness within OT and critical infrastructure. This thesis advances beyond current honeypot implementations and furthers the current state-of-the-art by delivering novel ways of deploying ICS honeypots and delivering concrete answers to key research questions within the area. This is done by answering the question previously raised from a multitude of perspectives. We discuss relevant legislation, such as the UK Cyber Assessment Framework, the US NIST Framework for Improving Critical Infrastructure Cybersecurity, and associated industry-based standards and guidelines supporting operator compliance. Standards and guidance are used to frame a discussion on our survey of existing ICS honeypot implementations in the literature and their role in supporting regulatory objectives. However, these deployments are not always correctly configured and might differ from a real ICS. Based on these insights, we propose a novel framework towards the classification and implementation of ICS honeypots. This is underpinned by a study into the passive identification of ICS honeypots using Internet scanner data to identify honeypot characteristics. We also present how honeypots can be leveraged to identify when bespoke ICS vulnerabilities are exploited within the organisational network—further strengthening the case for honeypot usage within critical infrastructure environments. Additionally, we demonstrate a fundamentally different approach to the deployment of honeypots. By deploying it as a deterrent, to reduce the likelihood that an adversary interacts with a real system. This is important as skilled attackers are now adept at fingerprinting and avoiding honeypots. The results presented in this thesis demonstrate that honeypots can provide several benefits to the cyber security of and alignment to regulations within the critical infrastructure environment

NeuralPot: an industrial honeypot implementation based on convolutional neural networks

Honeypots are powerful security tools, which are developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network by attracting malicious activity away from critical network devices. Given that the security incidents against industrial and critical infrastructure are getting sophisticated and persistent, advanced security systems are needed. In this paper, a novel industrial honeypot implementation is presented, which is based on the Modbus protocol, entitled NeuralPot. The presented NeuralPot honeypot is able to emulate industrial Modbus entities in order to actively confuse the intruders. It achieves this by introducing two distinct deep neural networks, a Generative Adversarial Network and an Autoencoder Network, which learn Modbus device behavior and generate realistic-looking traffic behavior. Based on the evaluation results, the proposed industrial honeypot performs well in terms of accuracy, similarity, and elapsed time of data generation

A neural-visualization IDS for honeynet data

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzedRegional Government of Gipuzkoa, the Department of Research, Education and Universities of the Basque Government, and the Spanish Ministry of Science and Innovation (MICINN) under projects TIN2010-21272-C02-01 and CIT-020000-2009-12 (funded by the European Regional Development Fund). This work was also supported in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070 supported by the Operational Program 'Research and Development for Innovations' funded through the Structural Funds of the European Union and the state budget of the Czech RepublicElectronic version of an article published as International Journal of Neural Systems, Volume 22, Issue 02, April 2012 10.1142/S0129065712500050 ©copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/ijn

To Deceive or not Deceive: Unveiling The Adoption Determinants Of Defensive Cyber Deception in Norwegian Organizations

Due to the prevailing threat landscape in Norway, it is imperative for organizations to safe- guard their infrastructures against cyber threats. One of the technologies that is advan- tageous against these threats is defensive cyber deception, which is an approach in cyber security that aims to be proactive, to interact with the attackers, trick them, deceive them and use this to the defenders advantage. This type of technology can help organizations defend against sophisticated threat actors that are able to avoid more traditional defensive mechanisms, such as Intrusion Detection Systems (IDS) or Intrusion Prevention Systems (IPS). In order to aid the adoption of defensive cyber deception in Norway, we asked the question: "What affects the adoption of defensive cyber deception in organizations in Nor- way?". To answer this question, we utilized the Technology, Organization, and Environment (TOE) Framework to identity what factors affect an organization’s adoption of defensive cyber deception. Through our use of the framework, we identified eighteen different factors which affect an organization’s adoption of defensive cyber deception. These factors are the product of the empirical data analysis from eight different semi-structured interview with individuals from six different organizations in Norway. The main theoretical implications of our research is the introduction of a TOE model for defensive cyber deception, focusing specifically on organizations in Norway as well as contributing with a maturity estimate model for defensive cyber deception. For the practical implications of our research, we have identified seven different benefits that defensive cyber deception provides. We are also con- tributing to raising the awareness of defensive cyber deception in Norwegian research and we hope that our TOE model can aid organizations that are considering adopting the tech- nology. We hope that these implications and contributions can act as a spark for both the adoption of defensive cyber deception in organizations as well as the start of a new wave for the cyber security researchers within Norway. Keywords: Cyber Security, Defensive Cyber Deception, TOE Framework, Adoptio

To Deceive or not Deceive: Unveiling The Adoption Determinants Of Defensive Cyber Deception in Norwegian Organizations

Due to the prevailing threat landscape in Norway, it is imperative for organizations to safeguard their infrastructures against cyber threats. One of the technologies that is advantageous against these threats is defensive cyber deception, which is an approach in cyber security that aims to be proactive, to interact with the attackers, trick them, deceive them and use this to the defenders advantage. This type of technology can help organizations defend against sophisticated threat actors that are able to avoid more traditional defensive mechanisms, such as Intrusion Detection Systems (IDS) or Intrusion Prevention Systems (IPS). In order to aid the adoption of defensive cyber deception in Norway, we asked the question: "What affects the adoption of defensive cyber deception in organizations in Norway?". To answer this question, we utilized the Technology, Organization, and Environment (TOE) Framework to identity what factors affect an organization's adoption of defensive cyber deception. Through our use of the framework, we identified eighteen different factors which affect an organization's adoption of defensive cyber deception. These factors are the product of the empirical data analysis from eight different semi-structured interview with individuals from six different organizations in Norway. The main theoretical implications of our research is the introduction of a TOE model for defensive cyber deception, focusing specifically on organizations in Norway as well as contributing with a maturity estimate model for defensive cyber deception. For the practical implications of our research, we have identified seven different benefits that defensive cyber deception provides. We are also contributing to raising the awareness of defensive cyber deception in Norwegian research and we hope that our TOE model can aid organizations that are considering adopting the technology. We hope that these implications and contributions can act as a spark for both the adoption of defensive cyber deception in organizations as well as the start of a new wave for the cyber security researchers within Norway. Keywords: Cyber Security, Defensive Cyber Deception, TOE Framework, Adoptio

Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation

The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics