Software vulnerability prediction using Grey wolf optimized Random forest on the unbalanced data sets

Any vulnerability in the software creates a software security threat and helps hackers to gain unauthorized access to resources. Vulnerability prediction models help software engineers to effectively allocate their resources to find any vulnerable class in the software, before its delivery to customers. Vulnerable classes must be carefully reviewed by security experts and tested to identify potential threats that may arise in the future. In the present work, a novel technique based on Grey wolf algorithm and Random forest is proposed for software vulnerability prediction. Grey wolf technique is a metaheuristic technique and it is used to select the best subset of features. The proposed technique is compared with other machine learning techniques. Experiments were performed on three datasets available publicly. It was observed that our proposed technique (GW-RF) outperformed all other techniques for software vulnerability prediction.

Robot Hacking Games: China’s Competitions to Automate the Software Vulnerability Lifecycle

Software vulnerability discovery, patching, and exploitation—collectively known as the vulnerability lifecycle—is time consuming and labor intensive. Automating the process could significantly improve software security and offensive hacking. The Defense Advanced Research Projects Agency’s Cyber Grand Challenge supported teams of researchers from 2014 to 2016 that worked to create these tools. China took notice. In 2017, China hosted its first Robot Hacking Game, seeking to automate the software vulnerability lifecycle. Since then, China has hosted seven such competitions and the People’s Liberation Army has increased its role in hosting the games.

Network Software Vulnerability Identifier using J48 decision tree algorithm

Software vulnerabilities are the primary causes of different security issues in the modern era. When vulnerability is exploited by malicious assaults, it substantially jeopardizes the system's security and may potentially result in catastrophic losses. As a result, automatic classification methods are useful for successfully managing software vulnerabilities, improving system security performance, and lowering the chance of the system being attacked and destroyed. In the software industry and in the field of cyber security, the ever-increasing number of publicly reported security flaws has become a major source of concern. Because software security flaws play such a significant part in cyber security attacks, relevant security experts are conducting an increasing number of vulnerability classification studies, this project can predict the software vulnerability means the software's in the device are authorized or not and who scan the system multiple times, to identify the vulnerability j48 decision tree algorithm was used. Keywords: Malicious assaults, catastrophic losses, Security flaws, Cyber security, Vulnerability Classifications.