Software vulnerabilities have been continually disclosed and documented. An important practice in documenting vulnerabilities is to describe the key vulnerability aspects, such as vulnerability type, root cause, affected product, impact, attacker type and attack vector, for the effective search and management of fast-growing vulnerabilities. We investigate 120,103 vulnerability reports in the Common Vulnerabilities and Exposures (CVE) over the past 20 years. We find that 56%, 85%, 38% and 28% of CVEs miss vulnerability type, root causes, attack vector and attacker type respectively. To help to complete the missing information of these vulnerability aspects, we propose a neural-network based approach for predicting the missing information of a key aspect of a vulnerability based on the known aspects of the vulnerability. We explore the design space of the neural network models and empirically identify the most effective model design. Using a large-scale vulnerability datas\-et from CVE, we show that we can effectively train a neural-network based classifier with less than 20% of historical CVEs. Our model achieves the prediction accuracy 94%, 79%, 89%and 70% for vulnerability type, root cause, attacker type and attack vector, respectively. Our ablation study reveals the prominent correlations among vulnerability aspects and further confirms the practicality of our approach.
Currently, while software engineers write code for various modules, quite often, various types of errors - coding, logic, semantic, and others (most of which are not caught by compilation and other tools) get introduced. Some of these bugs might be found in the later stage of testing, and many times it is reported by customers on production code. Companies have to spend many resources, both money and time in finding and fixing the bugs which would have been avoided if coding was done right. Also, concealed flaws in software can lead to security vulnerabilities that potentially allow attackers …
This paper presents an approach for identification of vulnerable IoT applications. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis. Tainted flows vulnerability is very much impacted by the structure of the program and the order of the statements in the code, designing an approach to detect such vulnerability needs to take into consideration such information in order to provide precise results. In this paper, we propose and develop an approach, FlowsMiner, that mines features from the code rel…
Background: Modern software uses many third-party libraries and frameworks as dependencies. Known vulnerabilities in these dependencies are a potential security risk. Software composition analysis (SCA) tools, therefore, are being increasingly adopted by practitioners to keep track of vulnerable dependencies. Aim: The goal of this study is to understand the difference in vulnerability reporting by various SCA tools. Understanding if and how existing SCA tools differ in their analysis may help security practitioners to choose the right tooling and identify future research needs. Method: We pres…
The presence of software vulnerabilities is an ever-growing issue in software development. In most cases, it is desirable to detect vulnerabilities as early as possible, preferably in a just-in-time manner, when the vulnerable piece is added to the code base. The industry has a hard time combating this problem as manual inspection is costly and traditional means, such as rule-based bug detection, are not robust enough to follow the pace of the emergence of new vulnerabilities. The actively researched field of machine learning could help in such situations as models can be trained to detect vul…
Software engineers can find vulnerabilities with less effort if they are directed towards code that might contain more vulnerabilities. HARMLESS is an incremental support vector machine tool that builds a vulnerability prediction model from the sourcecode inspected to date, then suggests what source code files should be inspected next. In this way, HARMLESS can reduce the time and effort required to achieve some desired level of recall for finding vulnerabilities. The tool also provides feedback on when to stop (at that desired level of recall) while at the same time, correcting human errors b…
Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building…
The University of Adelaide（阿德莱德大学）；Cyber Security Cooperative Research Centre（网络安全合作研究中心）
Many studies have developed Machine Learning (ML) approaches to detect Software Vulnerabilities (SVs) in functions and fine-grained code statements that cause such SVs. However, there is little work on leveraging such detection outputs for data-driven SV assessment to give information about exploitability, impact, and severity of SVs. The information is important to understand SVs and prioritize their fixing. Using large-scale data from 1,782 functions of 429 SVs in 200 real-world projects, we investigate ML models for automating function-level SV assessment tasks, i.e., predicting seven Commo…
Cross-Ecosystem Vulnerability Analysis for Python Applications
University of Athens（雅典大学）；National Infrastructures for Research and Technology（国家研究与技术基础设施）；Athens University of Economics and Business（雅典经济与商业大学）；ETH Zurich（苏黎世联邦理工学院）；Technical University of Crete（克里特理工大学）
