Vulnerability Prediction Based on Weighted Software Network for Secure Software Building

Abstract

To build a secure communications software, Vulnerability Prediction Models (VPMs) are used to predict vulnerable software modules in the software system before software security testing. At present many software security metrics have been proposed to design a VPM. In this paper, we predict vulnerable classes in a software system by establishing the system's weighted software network. The metrics are obtained from the nodes' attributes in the weighted software network. We design and implement a crawler tool to collect all public security vulnerabilities in Mozilla Firefox. Based on these data, the prediction model is trained and tested. The results show that the VPM based on weighted software network has a good performance in accuracy, precision, and recall. Compared to other studies, it shows that the performance of prediction has been improved greatly in Pr and Re.

Network Software Vulnerability Identifier using J48 decision tree algorithm

“…They used these algorithms: MFSA, BN, NB, NN, SVM, RF .Accuracy is about 80-91%. [8] [9] Madae Zolanvri, includes Machine Learning to analyses the network vulnerability of IIOT (Industrial Internet Of Things) IIOT it refers to the Interconnected sensors, Instrument's and other devices networked together with computers industrial applications. Here they deploy backdoor, command injection and SQL .Main purpose of using IIOT in this paper is taking a huge advantage of IOT in the Industrial Control System's(ICs) they looking for Protocols ex: Madbus,DNP3,BACnet, and more.…”