Uncovering Vulnerable Industrial Control Systems from the Internet Core

Industrial control systems (ICS) are managed remotely with the help of dedicated protocols that were originally designed to work in walled gardens. Many of these protocols have been adapted to Internet transport and support wide-area communication. ICS now exchange insecure traffic on an inter-domain level, putting at risk not only common critical infrastructure but also the Internet ecosystem (e.g., DRDoS~attacks). In this paper, we uncover unprotected inter-domain ICS traffic at two central Internet vantage points, an IXP and an ISP. This traffic analysis is correlated with data from honeypots and Internet-wide scans to separate industrial from non-industrial ICS traffic. We provide an in-depth view on Internet-wide ICS communication. Our results can be used i) to create precise filters for potentially harmful non-industrial ICS traffic, and ii) to detect ICS sending unprotected inter-domain ICS traffic, being vulnerable to eavesdropping and traffic manipulation attacks

Industrial control protocols in the Internet core: Dismantling operational practices

Industrial control systems (ICS) are managed remotely with the help of dedicated protocols that were originally designed to work in walled gardens. Many of these protocols have been adapted to Internet transport and support wide-area communication. ICS now exchange insecure traffic on an inter-domain level, putting at risk not only common critical infrastructure but also the Internet ecosystem (e.g., by DRDoS attacks). In this paper, we measure and analyze inter-domain ICS traffic at two central Internet vantage points, an IXP and an ISP. These traffic observations are correlated with data from honeypots and Internet-wide scans to separate industrial from non-industrial ICS traffic. We uncover mainly unprotected inter-domain ICS traffic and provide an in-depth view on Internet-wide ICS communication. Our results can be used (i) to create precise filters for potentially harmful non-industrial ICS traffic and (ii) to detect ICS sending unprotected inter-domain ICS traffic, being vulnerable to eavesdropping and traffic manipulation attacks. Additionally, we survey recent security extensions of ICS protocols, of which we find very little deployment. We estimate an upper bound of the deployment status for ICS security protocols in the Internet core

Wide spectrum attribution: Using deception for attribution intelligence in cyber attacks

Modern cyber attacks have evolved considerably. The skill level required to conduct a cyber attack is low. Computing power is cheap, targets are diverse and plentiful. Point-and-click crimeware kits are widely circulated in the underground economy, while source code for sophisticated malware such as Stuxnet is available for all to download and repurpose. Despite decades of research into defensive techniques, such as firewalls, intrusion detection systems, anti-virus, code auditing, etc, the quantity of successful cyber attacks continues to increase, as does the number of vulnerabilities identified. Measures to identify perpetrators, known as attribution, have existed for as long as there have been cyber attacks. The most actively researched technical attribution techniques involve the marking and logging of network packets. These techniques are performed by network devices along the packet journey, which most often requires modification of existing router hardware and/or software, or the inclusion of additional devices. These modifications require wide-scale infrastructure changes that are not only complex and costly, but invoke legal, ethical and governance issues. The usefulness of these techniques is also often questioned, as attack actors use multiple stepping stones, often innocent systems that have been compromised, to mask the true source. As such, this thesis identifies that no publicly known previous work has been deployed on a wide-scale basis in the Internet infrastructure. This research investigates the use of an often overlooked tool for attribution: cyber de- ception. The main contribution of this work is a significant advancement in the field of deception and honeypots as technical attribution techniques. Specifically, the design and implementation of two novel honeypot approaches; i) Deception Inside Credential Engine (DICE), that uses policy and honeytokens to identify adversaries returning from different origins and ii) Adaptive Honeynet Framework (AHFW), an introspection and adaptive honeynet framework that uses actor-dependent triggers to modify the honeynet envi- ronment, to engage the adversary, increasing the quantity and diversity of interactions. The two approaches are based on a systematic review of the technical attribution litera- ture that was used to derive a set of requirements for honeypots as technical attribution techniques. Both approaches lead the way for further research in this field

ICSrank: A Security Assessment Framework for Industrial Control Systems (ICS)

This thesis joins a lively dialogue in the technological arena on the issue of cybersecurity and specifically, the issue of infrastructure cybersecurity as related to Industrial Control Systems. Infrastructure cybersecurity is concerned with issues on the security of the critical infrastructure that have significant value to the physical infrastructure of a country, and infrastructure that is heavily reliant on IT and the security of such technology. It is an undeniable fact that key infrastructure such as the electricity grid, gas, air and rail transport control, and even water and sewerage services rely heavily on technology. Threats to such infrastructure have never been as serious as they are today. The most sensitive of them is the reliance on infrastructure that requires cybersecurity in the energy sector. The call to smart technology and automation is happening nowadays. The Internet is witnessing an increase number of connected industrial control system (ICS). Many of which don’t follow security guidelines. Privacy and sensitive data are also an issue. Sensitive leaked information is being manipulated by adversaries to accomplish certain agendas. Open Source intelligence (OSINT) is adopted by defenders to improve protection and safeguard data. This research presented in thesis, proposes “ICSrank” a novel security risk assessment for ICS devices based on OSINT. ICSrank ranks the risk level of online and offline ICS devices. This framework categorizes, assesses and ranks OSINT data using ICSrank framework. ICSrank provides an additional layer of defence and mitigation in ICS security, by identification of risky OSINT and devices. Security best practices always begin with identification of risk as a first step prior to security implementation. Risk is evaluated using mathematical algorithms to assess the OSINT data. The subsequent results achieved during the assessment and ranking process were informative and realistic. ICSrank framework proved that security and risk levels were more accurate and informative than traditional existing methods

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

A formalised ontology for network attack classification

One of the most popular attack vectors against computers are their network connections. Attacks on computers through their networks are commonplace and have various levels of complexity. This research formally describes network-based computer attacks in the form of a story, formally and within an ontology. The ontology categorises network attacks where attack scenarios are the focal class. This class consists of: Denial-of- Service, Industrial Espionage, Web Defacement, Unauthorised Data Access, Financial Theft, Industrial Sabotage, Cyber-Warfare, Resource Theft, System Compromise, and Runaway Malware. This ontology was developed by building a taxonomy and a temporal network attack model. Network attack instances (also know as individuals) are classified according to their respective attack scenarios, with the use of an automated reasoner within the ontology. The automated reasoner deductions are verified formally; and via the automated reasoner, a relaxed set of scenarios is determined, which is relevant in a near real-time environment. A prototype system (called Aeneas) was developed to classify network-based attacks. Aeneas integrates the sensors into a detection system that can classify network attacks in a near real-time environment. To verify the ontology and the prototype Aeneas, a virtual test bed was developed in which network-based attacks were generated to verify the detection system. Aeneas was able to detect incoming attacks and classify them according to their scenario. The novel part of this research is the attack scenarios that are described in the form of a story, as well as formally and in an ontology. The ontology is used in a novel way to determine to which class attack instances belong and how the network attack ontology is affected in a near real-time environment

On Collaborative Intrusion Detection

Cyber-attacks have nowadays become more frightening than ever before. The growing dependency of our society on networked systems aggravates these threats; from interconnected corporate networks and Industrial Control Systems (ICSs) to smart households, the attack surface for the adversaries is increasing. At the same time, it is becoming evident that the utilization of classic fields of security research alone, e.g., cryptography, or the usage of isolated traditional defense mechanisms, e.g., firewalls and Intrusion Detection Systems ( IDSs ), is not enough to cope with the imminent security challenges. To move beyond monolithic approaches and concepts that follow a “cat and mouse” paradigm between the defender and the attacker, cyber-security research requires novel schemes. One such promis- ing approach is collaborative intrusion detection. Driven by the lessons learned from cyber-security research over the years, the aforesaid notion attempts to connect two instinctive questions: “if we acknowledge the fact that no security mechanism can detect all attacks, can we beneficially combine multiple approaches to operate together?” and “as the adversaries increasingly collaborate (e.g., Distributed Denial of Service (DDoS) attacks from whichever larger botnets) to achieve their goals, can the defenders beneficially collude too?”. Collabora- tive intrusion detection attempts to address the emerging security challenges by providing methods for IDSs and other security mech- anisms (e.g., firewalls and honeypots) to combine their knowledge towards generating a more holistic view of the monitored network. This thesis improves the state of the art in collaborative intrusion detection in several areas. In particular, the dissertation proposes methods for the detection of complex attacks and the generation of the corresponding intrusion detection signatures. Moreover, a novel approach for the generation of alert datasets is given, which can assist researchers in evaluating intrusion detection algorithms and systems. Furthermore, a method for the construction of communities of collab- orative monitoring sensors is given, along with a domain-awareness approach that incorporates an efficient data correlation mechanism. With regard to attacks and countermeasures, a detailed methodology is presented that is focusing on sensor-disclosure attacks in the con- text of collaborative intrusion detection. The scientific contributions can be structured into the following categories: Alert data generation: This thesis deals with the topic of alert data generation in a twofold manner: first it presents novel approaches for detecting complex attacks towards generating alert signatures for IDSs ; second a method for the synthetic generation of alert data is pro- posed. In particular, a novel security mechanism for mobile devices is proposed that is able to support users in assessing the security status of their networks. The system can detect sophisticated attacks and generate signatures to be utilized by IDSs . The dissertation also touches the topic of synthetic, yet realistic, dataset generation for the evaluation of intrusion detection algorithms and systems; it proposes a novel dynamic dataset generation concept that overcomes the short- comings of the related work. Collaborative intrusion detection: As a first step, the the- sis proposes a novel taxonomy for collaborative intrusion detection ac- companied with building blocks for Collaborative IDSs ( CIDSs ). More- over, the dissertation deals with the topics of (alert) data correlation and aggregation in the context of CIDSs . For this, a number of novel methods are proposed that aim at improving the clustering of mon- itoring sensors that exhibit similar traffic patterns. Furthermore, a novel alert correlation approach is presented that can minimize the messaging overhead of a CIDS. Attacks on CIDSs: It is common for research on cyber-defense to switch its perspective, taking on the viewpoint of attackers, trying to anticipate their remedies against novel defense approaches. The the- sis follows such an approach by focusing on a certain class of attacks on CIDSs that aim at identifying the network location of the monitor- ing sensors. In particular, the state of the art is advanced by proposing a novel scheme for the improvement of such attacks. Furthermore, the dissertation proposes novel mitigation techniques to overcome both the state of art and the proposed improved attacks. Evaluation: All the proposals and methods introduced in the dis- sertation were evaluated qualitatively, quantitatively and empirically. A comprehensive study of the state of the art in collaborative intru- sion detection was conducted via a qualitative approach, identifying research gaps and surveying the related work. To study the effective- ness of the proposed algorithms and systems extensive simulations were utilized. Moreover, the applicability and usability of some of the contributions in the area of alert data generation was additionally supported via Proof of Concepts (PoCs) and prototypes. The majority of the contributions were published in peer-reviewed journal articles, in book chapters, and in the proceedings of interna- tional conferences and workshops

What Ukraine Taught NATO about Hybrid Warfare

Russia’s invasion of Ukraine in 2022 forced the United States and its NATO partners to be confronted with the impact of hybrid warfare far beyond the battlefield. Targeting Europe’s energy security, Russia’s malign influence campaigns and malicious cyber intrusions are affecting global gas prices, driving up food costs, disrupting supply chains and grids, and testing US and Allied military mobility. This study examines how hybrid warfare is being used by NATO’s adversaries, what vulnerabilities in energy security exist across the Alliance, and what mitigation strategies are available to the member states. Cyberattacks targeting the renewable energy landscape during Europe’s green transition are increasing, making it urgent that new tools are developed to protect these emerging technologies. No less significant are the cyber and information operations targeting energy security in Eastern Europe as it seeks to become independent from Russia. Economic coercion is being used against Western and Central Europe to stop gas from flowing. China’s malign investments in Southern and Mediterranean Europe are enabling Beijing to control several NATO member states’ critical energy infrastructure at a critical moment in the global balance of power. What Ukraine Taught NATO about Hybrid Warfare will be an important reference for NATO officials and US installations operating in the European theater.https://press.armywarcollege.edu/monographs/1952/thumbnail.jp

U.S. strategic cyber deterrence options

The U.S. government appears incapable of creating an adequate strategy to alter the behavior of the wide variety of malicious actors seeking to inflict harm or damage through cyberspace. This thesis provides a systematic analysis of contemporary deterrence strategies and offers the U.S. the strategic option of active cyber defense designed for continuous cybered conflict. It examines the methods and motivations of the wide array of malicious actors operating in the cyber domain. The thesis explores how the theories of strategy and deterrence underpin the creation of strategic deterrence options and what role deterrence plays with respect to strategies, as a subset, a backup, an element of one or another strategic choice. It looks at what the government and industry are doing to convince malicious actors that their attacks will fail and that risk of consequences exists. The thesis finds that contemporary deterrence strategies of retaliation, denial and entanglement lack the conditions of capability, credibility, and communications that are necessary to change the behavior of malicious actors in cyberspace. This research offers a midrange theory of active cyber defense as a way to compensate for these failings through internal systemic resilience and tailored disruption capacities that both frustrate and punish the wide range of malicious actors regardless of origin or intentions. The thesis shows how active cyber defense is technically capable and legally viable as an alternative strategy in the U.S. to strengthen the deterrence of cyber attacks