Software Engineering Challenges for Investigating Cyber-Physical Incidents

Cyber-Physical Systems (CPS) are characterized by the interplay between digital and physical spaces. This characteristic has extended the attack surface that could be exploited by an offender to cause harm. An increasing number of cyber-physical incidents may occur depending on the configuration of the physical and digital spaces and their interplay. Traditional investigation processes are not adequate to investigate these incidents, as they may overlook the extended attack surface resulting from such interplay, leading to relevant evidence being missed and testing flawed hypotheses explaining the incidents. The software engineering research community can contribute to addressing this problem, by deploying existing formalisms to model digital and physical spaces, and using analysis techniques to reason about their interplay and evolution. In this paper, supported by a motivating example, we describe some emerging software engineering challenges to support investigations of cyber-physical incidents. We review and critique existing research proposed to address these challenges, and sketch an initial solution based on a meta-model to represent cyber-physical incidents and a representation of the topology of digital and physical spaces that supports reasoning about their interplay

Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling

Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of social spambots has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the digital DNA technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such characterization, we design the Social Fingerprinting technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We finally evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection algorithms. Among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics

INTEGRATION OF INTELLIGENCE TECHNIQUES ON THE EXECUTION OF PENETRATION TESTS (iPENTEST)

Penetration Tests (Pentests) identify potential vulnerabilities in the security of computer systems via security assessment. However, it should also benefit from widely recognized methodologies and recommendations within this field, as the Penetration Testing Execution Standard (PTES). The objective of this research is to explore PTES, particularly the three initial phases: 1. Pre-Engagement Interactions; 2. Intelligence Gathering; 3. Threat Modeling; and ultimately to apply Intelligence techniques to the Threat Modeling phase. To achieve this, we will use open-source and/or commercial tools to structure a process to clarify how the results were reached using the research inductive methodology. The following steps were implemented: i) critical review of the “Penetration Testing Execution Standard (PTES)”; ii) critical review of Intelligence Production Process; iii) specification and classification of contexts in which Intelligence could be applied; iv) definition of a methodology to apply Intelligence Techniques to the specified contexts; v) application and evaluation of the proposed methodology to real case study as proof of concept. This research has the ambition to develop a model grounded on Intelligence techniques to be applied on PTES Threat Modeling phase

Computer Criminal Profiling applied to Digital Investigations

This PhD thesis aims to contribute to the Cyber Security body of knowledge and its Computer Forensic field, still in its infancy when comparing with other forensic sciences. With the advancements of computer technology and the proliferation of cyber crime, offenders making use of computers range from state-sponsored cyber squads to organized crime rings; from cyber paedophiles to crypto miners abusing third-party computer resources. Cyber crime is not only impacting the global economy in billions of dollars annually; it is also a life-threatening risk as society is increasingly dependent on critical systems like those in air traffic control, hospitals or connected cars. Achieving cyber attribution is a step towards to identify, deter and prosecute offenders in the cyberspace, a domain among the top priorities for the UK National Security Strategy. However, the rapid evolution of cyber crime may be an unprecedented challenge in the forensic science history. Attempts to keep up with this pace often result in computer forensic practices limited to technical outcomes, like user accounts or IP addresses used by the offenders. Limitations are intensified when the current cyber security skill shortage contrasts with the vastness of digital crime scenes presented by cloud providers and extensive storage capacities or with the wide range of available anonymizing mechanisms. Quite often, offenders are remaining unidentified, unpunished, and unstoppable. As these anonymising mechanisms conceal offenders from a technological perspective, it was considered that they would not offer the same level of concealment from a behavioural standpoint. Therefore, in addition to the analysis of the state-of-theart of cyber crimes and anonymising mechanisms, the literature of traditional crimes and criminal psychology was reviewed, in an attempt to known what traits of human behaviour could be revealed by the evidence at a crime scene and how to recognize them. It was identified that the subdiscipline of criminology called criminal profiling helps providing these answers. Observing its success rate and benefits as a support tool in traditional investigations, it was hypothesized that a similar outcome could be achieved while investigating cyber crimes, providing that a framework could enable digital investigators to apply criminal profiling concepts in digital investigations. 2 Before developing the framework, the scope of this thesis was delimited to a subset of cyber crimes, consisting exclusively of computer intrusions cases. Also, among potential criminal profiling benefits, the reduction of the suspect pool, case linkage and optimization of investigative efforts were included in the scope. A SSH honeypot experiment based on Cowrie was designed and deployed in a public cloud infrastructure. In its first phase, a single honeypot instance was launched, protected by username and password and accepting connection attempts from any Internet address. Users that were able to guess a valid pair of credentials, after a random number of attempts providing strong passwords, were presented to a simple file system, in which all their interactions within the system were recorded and all downloaded attack tools were isolated and securely stored for their posterior analysis. In the second phase of the experiment, the honeypot infrastructure was expanded to a honeynet with 18 (eighteen) nodes, running in a total of 6 (six) geographic regions and making it possible the analysis of additional variables like location of the “victim” system, perceived influence from directory/file structure/contents and resistance levels to password attacks. After a period of approximately 18 (eighteen) months, more than 7 million connection attempts and 12 million authentication attempts were received by the honeynet, where more than 85,000 were able to successfully log into one of the honeynet servers. Offenders were able to interact with the simulated operating systems and their files, while enabling this research to identify behavioural patterns that proved to be useful not only to group offenders, but also to enrich individual offender profiles. Among these behavioural patterns, the choice of which commands and which parameters to run, the basis of the attack on automated versus manual means, the pairs of usernames and passwords that were provided to try to break the honeypot authentication, their response once a command was not successful, their intent on using specific attack tools and the motivation behind it, any level of caution presented and, finally, preferences for naming tools, temporary files or customized ports were some of the most relevant attributes. Based on the collected data set, such attributes successfully make it possible to narrow down the pools of suspects, to link different honeypot breakins to a same offender and to optimize investigative efforts by enabling the researcher to focus the analysis in a reduced area while searching for evidence. 3 In times when cyber security skills shortage is a concerning challenge and where profiling can play a critical role, it is believed that such a structured framework for criminal profiling within cyber investigations can help to make investigation of cyber crimes quicker, cheaper and more effective

Lateral Movement in Windows Systems and Detecting the Undetected ShadowMove

Lateral Movement is a pervasive threat that exists because modern networked systems that provide access to multiple users are far more efficient than their non-networked counterparts. It is a well-known attack methodology with extensive research completed into preventing lateral movement in enterprise systems. However, attackers are using more sophisticated methods to move laterally that bypass typical detection systems. This research comprehensively reviews the problems in lateral movement detection and outlines common defenses to protect modern systems from lateral movement attacks. A literature review is conducted, outlining new techniques for automatic detection of malicious lateral movement, explaining common attack methods utilized by Advanced Persistent Threats, and components built into the Windows operating system that can assist with discovering malicious lateral movement. Finally, a novel method for moving laterally is introduced and studied, and an original method for detecting this method of lateral movement is proposed

Integration of Cost andWork Breakdown Structures in the Management of Construction Projects

Scope management allows project managers to react when a project underperforms regarding schedule, budget, and/or quality at the execution stage. Scope management can also minimize project changes and budget omissions, as well as improve the accuracy of project cost estimates and risk responses. For scope management to be effective, though, it needs to rely on a robust work breakdown structure (WBS). A robust WBS hierarchically and faithfully reflects all project tasks and work packages so that projects are easier to manage. If done properly, the WBS also allows meeting the project objectives while delivering the project on time, on budget, and with the required quality. This paper analyzes whether the integration of a cost breakdown structure (CBS) can lead to the generation of more robust WBSs in construction projects. Over the last years, some international organizations have standardized and harmonized different cost classification systems (e.g., ISO 12006-2, ISO 81346-12, OmniClass, CoClass, UniClass). These cost databases have also been introduced into building information modeling (BIM) frameworks. We hypothesize that in BIM environments, if these CBSs are used to generate the project WBS, several advantages are gained such as sharper project definition. This enhanced project definition reduces project contradictions at both planning and execution stages, anticipates potential schedule and budget deviations, improves resource allocation, and overall it allows a better response to potential project risks. The hypothesis that the use of CBSs can generate more robust WBSs is tested by the response analysis of a questionnaire survey distributed among construction practitioners and project managers. By means of structural equation modeling (SEM), the correlation (agreement) and perception differences between two 250-respondent subsamples (technical project staff vs. project management staff) are also discussed. Results of this research support the use of CBSs by construction professionals as a basis to generate WBSs for enhanced project management (PM)

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged