Toward Improved Deep Learning-based Vulnerability Detection

Deep learning (DL) has been a common thread across several recent techniques for vulnerability detection. The rise of large, publicly available datasets of vulnerabilities has fueled the learning process underpinning these techniques. While these datasets help the DL-based vulnerability detectors, they also constrain these detectors’ predictive abilities. Vulnerabilities in these datasets have to be represented in a certain way, e.g., code lines, functions, or program slices within which the vulnerabilities exist. We refer to this representation as a base unit. The detectors learn how base units can be vulnerable and then predict whether other base units are vulnerable. We have hypothesized that this focus on individual base units harms the ability of the detectors to properly detect those vulnerabilities that span multiple base units (or MBU vulnerabilities). For vulnerabilities such as these, a correct detection occurs when all comprising base units are detected as vulnerable. Verifying how existing techniques perform in detecting all parts of a vulnerability is important to establish their effectiveness for other downstream tasks. To evaluate our hypothesis, we conducted a study focusing on three prominent DL-based detectors: ReVeal, DeepWukong, and LineVul. Our study shows that all three detectors contain MBU vulnerabilities in their respective datasets. Further, we observed significant accuracy drops when detecting these types of vulnerabilities. We present our study and a framework that can be used to help DL-based detectors toward the proper inclusion of MBU vulnerabilities.</p

Unmanned and Autonomous Systems of Systems Test and Evaluation: Challenges and Opportunities

The introduction of Unmanned and Autonomous Systems (UAS) brings substantial, interesting, and in many cases, new challenges to the Department of Defense’s Test and Evaluation community. The test and evaluation of UASs becomes significantly more complicated than traditional systems, especially as we approach more fully autonomous systems and need to test integrated systems of systems in joint military operational testing environments. Compounding the multi-faceted considerations involved in test and evaluation, systems have continuously increasing complexity and capabilities and can be at different maturity levels. Emergent properties, particularly those that are unplanned and undesired, also need to be considered. Challenges identified by the Unmanned and Autonomous Systems Test community and related to the test and evaluation of the UASs are discussed. This paper presents various approaches for addressing these challenges including an innovative Prescriptive and Adaptive Testing Framework and decision support system, PATFrame

Ružička days : International conference 16th Ružička Days “Today Science – Tomorrow Industry” : Proceedings

Proceedings contains articles presented at Conference divided into sections: open lecture (1), chemical analysis and synthesis (3), chemical and biochemical engineering (8), food technology and biotechnology (8), medical chemistry and pharmacy (3), environmental protection (11) and meeting of young chemists (2)