Defacement Detection with Passive Adversaries

A novel approach to defacement detection is proposed in this paper, addressing explicitly the possible presence of a passive adversary. Defacement detection is an important security measure for Web Sites and Applications, aimed at avoiding unwanted modifications that would result in significant reputational damage. As in many other anomaly detection contexts, the algorithm used to identify possible defacements is obtained via an Adversarial Machine Learning process. We consider an exploratory setting, where the adversary can observe the detector’s alarm-generating behaviour, with the purpose of devising and injecting defacements that will pass undetected. It is then necessary to make to learning process unpredictable, so that the adversary will be unable to replicate it and predict the classifier’s behaviour. We achieve this goal by introducing a secret key—a key that our adversary does not know. The key will influence the learning process in a number of different ways, that are precisely defined in this paper. This includes the subset of examples and features that are actually used, the time of learning and testing, as well as the learning algorithm’s hyper-parameters. This learning methodology is successfully applied in this context, by using the system with both real and artificially modified Web sites. A year-long experimentation is also described, referred to the monitoring of the new Web Site of a major manufacturing company

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

CNA Tactics and Techniques: A Structure Proposal

[EN] Destructive and control operations are today a major threat for cyber physical systems. These operations, known as Computer Network Attack (CNA), and usually linked to state-sponsored actors, are much less analyzed than Computer Network Exploitation activities (CNE), those related to intelligence gathering. While in CNE operations the main tactics and techniques are defined and well structured, in CNA there is a lack of such consensuated approaches. This situation hinders the modeling of threat actors, which prevents an accurate definition of control to identify and to neutralize malicious activities. In this paper, we propose the first global approach for CNA operations that can be used to map real-world activities. The proposal significantly reduces the amount of effort need to identify, analyze, and neutralize advanced threat actors targeting cyber physical systems. It follows a logical structure that can be easy to expand and adapt.Villalón-Huerta, A.; Ripoll-Ripoll, I.; Marco-Gisbert, H. (2021). CNA Tactics and Techniques: A Structure Proposal. Journal of Sensor and Actuator Networks. 10(1):1-23. https://doi.org/10.3390/jsan10010014S12310

The World of Defacers: Looking through the Lens of Their Activities on Twitter

Many web-based attacks have been studied to understand how web hackers behave, but web site defacement attacks (malicious content manipulations of victim web sites) and defacers’ behaviors have received less attention from researchers. This paper fills this research gap via a computational data-driven analysis of a public database of defacers and defacement attacks and activities of 96 selected defacers who were active on Twitter. We conducted a comprehensive analysis of the data: an analysis of a friendship graph with 10,360 nodes, an analysis on how sentiments of defacers related to attack patterns, and a topical modelling based analysis to study what defacers discussed publicly on Twitter. Our analysis revealed a number of key findings: a modular and hierarchical clustering method can help discover interesting sub-communities of defacers; sentiment analysis can help categorize behaviors of defacers in terms of attack patterns; and topic modelling revealed some focus topics (politics, country-specific topics, and technical discussions) among defacers on Twitter and also geographic links of defacers sharing similar topics. We believe that these findings are useful for a better understanding of defacers' behaviors, which could help design and development of better solutions for detecting defacers and even preventing impeding defacement attacks

Cyber security of smart building ecosystems

Abstract. Building automation systems are used to create energy-efficient and customisable commercial and residential buildings. During the last two decades, these systems have become more and more interconnected to reduce expenses and expand their capabilities by allowing vendors to perform maintenance and by letting building users to control the machines remotely. This interconnectivity has brought new opportunities on how building data can be collected and put to use, but it has also increased the attack surface of smart buildings by introducing security challenges that need to be addressed. Traditional building automation systems with their proprietary communication protocols and interfaces are giving way to interoperable systems utilising open technologies. This interoperability is an important aspect in streamlining the data collection process by ensuring that different components of the environment are able to exchange information and operate in a coordinated manner. Turning these opportunities into actual products and platforms requires multi-sector collaboration and joint research projects, so that the buildings of tomorrow can become reality with as few compromises as possible. This work examines one of these experimental project platforms, KEKO ecosystem, with the focus on assessing the cyber security challenges faced by the platform by using the well-recognised MITRE ATT&CK knowledge base of adversary tactics and techniques. The assessment provides a detailed categorisation of identified challenges and recommendations on how they should be addressed. This work also presents one possible solution for improving the detection of offensive techniques targeting building automation by implementing a monitoring pipeline within the experimental platform, and a security event API that can be integrated to a remote SIEM system to increase visibility on the platform’s data processing operations