An increasing number of embedded systems include dedicated neural hardware. To benefit from this specialized hardware, deep learning techniques to discover malware on embedded systems are needed. This effort evaluated candidate machine learning detection techniques for distinguishing exploited from nonexploited RISC-V program behavior using execution traces. We first developed a dataset of execution traces containing Return Oriented Programming (ROP) exploitation on the RISC-V Instruction Set Architecture (ISA) and then developed several deep learning bidirectional Long Short-Term Memory (LSTM) models capable of distinguishing exploited traces from non-exploited traces, each using subsets of features from the execution traces. An objective of this effort was to evaluate which features (instruction addresses and immediate values) from an execution trace are application-specific, which features (opcodes and operands) are application-agnostic, and how these subsets of features affect model performance. Applicationagnostic features allow a model to generalize its detection capability to detecting ROP in previously unseen applications. The model using opcode and operand sequences obtained 98.21% cross validation accuracy and 97.94% test accuracy. In contrast, a model using address values obtained 92.79% cross validation accuracy with 99.59% test set accuracy. This research also analyzed whether ROPs exploitation significantly affects branch prediction; experimental evidence suggests that it does. Thus, branch prediction behavior could be a valuable feature in detecting ROPs exploits.
Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of subscribers and publisher nodes from three to eighteen. By categorizing the network traffic from these nodes into either data message, security, or discovery+ with Quality of Service (QoS) best effort and reliable, the mean security traffic from DDS-C has minimal impact to Data Distribution Service (DDS) operations compared to other network traffic. The results reveal that applying DDS-C to a representative distributed network robotics operating system network does not impact performance.
The increased reliance on commercial satellites for military operations has made it essential for the Department of Defense (DoD) to adopt a supply chain framework to address cybersecurity threats in space. This paper presents a satellite supply chain framework, the Cybersecurity Supply Chain (CSSC) Framework, for the DoD in the evaluation and selection of commercial satellite contracts. The proposed strategy is informed by research on cybersecurity threats to commercial satellites, national security concerns, current DoD policy, and previous cybersecurity frameworks. This paper aims to provide a comprehensive approach for safeguarding commercial satellites used by the DoD and ensuring the security of their supporting components. Inspired by the National Institute of Standards and Technology (NIST) 800-171 requirements and the DoD’s future Cybersecurity Maturity Model Certification (CMMC) process, the two-part framework significantly streamlines the NIST requirements to accommodate small businesses. It also extends key NIST requirements to commercial-off-the-shelf (COTS) suppliers. The CSSC Framework complements the CMMC certification process by addressing the need for cybersecurity requirements for all subcontractors supporting a commercial space asset. The framework incorporates a scoring process similar to CMMC scoring, granting points to a subcontractor for meeting the cybersecurity requirements outlined by the framework. In addition, the framework creates a space architecture overview that details the overall bid score and establishes a matrix based on individual requirements. This model and matrix allow DoD acquisition personnel to closely analyze each contract bid, comparing the subcontractor's strengths and weaknesses to other bidders. The CSSC Framework will allow the DoD to apply NIST standards to subcontractors who do not meet the requirements for CMMC certification.
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