Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots

As autonomous robots become increasingly ubiquitous, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems that transverse the virtual realm and operate in the human dimension. As a consequence, securing the operation of autonomous robots goes beyond securing data, from sensor input to mission instructions, towards securing the interaction with their environment. There is a lack of research towards methods that would allow a robot to ensure that both its sensors and actuators are operating correctly without external feedback. This paper introduces a robotic mission encoding method that serves as an end-to-end validation framework for autonomous robots. In particular, we put our framework into practice with a proof of concept describing a novel map encoding method that allows robots to navigate an objective environment with almost-zero a priori knowledge of it, and to validate operational instructions. We also demonstrate the applicability of our framework through experiments with real robots for two different map encoding methods. The encoded maps inherit all the advantages of traditional landmark-based navigation, with the addition of cryptographic hashes that enable end-to-end information validation. This end-to-end validation can be applied to virtually any aspect of robotic operation where there is a predefined set of operations or instructions given to the robot

Cybersecurity Information Exchange with Privacy (CYBEX-P) and TAHOE – A Cyberthreat Language

Cybersecurity information sharing (CIS) is envisioned to protect organizations more effectively from advanced cyberattacks. However, a completely automated CIS platform is not widely adopted. The major challenges are: (1) the absence of advanced data analytics capabilities and (2) the absence of a robust cyberthreat language (CTL). This work introduces Cybersecurity Information Exchange with Privacy (CYBEX-P), as a CIS framework, to tackle these challenges. CYBEX-P allows organizations to share heterogeneous data from various sources. It correlates the data to automatically generate intuitive reports and defensive rules. To achieve such versatility, we have developed TAHOE - a graph-based CTL. TAHOE is a structure for storing, sharing, and analyzing threat data. It also intrinsically correlates the data. We have further developed a universal Threat Data Query Language (TDQL). In this work, we propose the system architecture for CYBEX-P. We then discuss its scalability along with a protocol to correlate attributes of threat data. We further introduce TAHOE & TDQL as better alternatives to existing CTLs and formulate ThreatRank - an algorithm to detect new malicious events.We have developed CYBEX-P as a complete CIS platform for not only data sharing but also for advanced threat data analysis. To that end, we have developed two frameworks that use CYBEX-P infrastructure as a service (IaaS). The first work is a phishing URL detector that uses machine learning to detect new phishing URLs. This real-time system adapts to the ever-changing landscape of phishing URLs and maintains an accuracy of 86%. The second work models attacker behavior in a botnet. It combines heterogeneous threat data and analyses them together to predict the behavior of an attacker in a host infected by a bot malware. We have achieved a prediction accuracy of 85-97% using our methodology. These two frameworks establish the feasibility of CYBEX-P for advanced threat data analysis for future researchers

Enhancing cyber assets visibility for effective attack surface management : Cyber Asset Attack Surface Management based on Knowledge Graph

The contemporary digital landscape is filled with challenges, chief among them being the management and security of cyber assets, including the ever-growing shadow IT. The evolving nature of the technology landscape has resulted in an expansive system of solutions, making it challenging to select and deploy compatible solutions in a structured manner. This thesis explores the critical role of Cyber Asset Attack Surface Management (CAASM) technologies in managing cyber attack surfaces, focusing on the open-source CAASM tool, Starbase, by JupiterOne. It starts by underlining the importance of comprehending the cyber assets that need defending. It acknowledges the Cyber Defense Matrix as a methodical and flexible approach to understanding and addressing cyber security challenges. A comprehensive analysis of market trends and business needs validated the necessity of asset security management tools as fundamental components in firms' security journeys. CAASM has been selected as a promising solution among various tools due to its capabilities, ease of use, and seamless integration with cloud environments using APIs, addressing shadow IT challenges. A practical use case involving the integration of Starbase with GitHub was developed to demonstrate the CAASM's usability and flexibility in managing cyber assets in organizations of varying sizes. The use case enhanced the knowledge graph's aesthetics and usability using Neo4j Desktop and Neo4j Bloom, making it accessible and insightful even for non-technical users. The thesis concludes with practical guidelines in the appendices and on GitHub for reproducing the use case