71,687 research outputs found
Pilot interaction with automated airborne decision making systems
Two project areas were pursued: the intelligent cockpit and human problem solving. The first area involves an investigation of the use of advanced software engineering methods to aid aircraft crews in procedure selection and execution. The second area is focused on human problem solving in dynamic environments, particulary in terms of identification of rule-based models land alternative approaches to training and aiding. Progress in each area is discussed
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
How explicit are the barriers to failure in safety arguments?
Safety cases embody arguments that demonstrate how safety properties of a system are upheld. Such cases implicitly document the barriers that must exist between hazards and vulnerable components of a system. For safety certification, it is the analysis of these barriers that provide confidence in the safety of the system. The explicit representation of hazard barriers can provide additional insight for the design and evaluation of system safety. They can be identified in a hazard analysis to allow analysts to reflect on particular design choices. Barrier existence in a live system can be mapped to abstract barrier representations to provide both verification of barrier existence and a basis for quantitative measures between the predicted barrier behaviour and performance of the actual barrier. This paper explores the first stage of this process, the binding between explicit mitigation arguments in hazard analysis and the barrier concept. Examples from the domains of computer-assisted detection in mammography and free route airspace feasibility are examined and the implications for system certification are considered
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