Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253
Browse
Search Results
Article The impact of feature types, classifiers, and data balancing techniques on software vulnerability prediction models(2019) Kaya, Aydın; Keçeli, Ali Seydi; Çatal, Çağatay; Tekinerdoğan, BedirSoftware vulnerabilities form an increasing security risk for software systems, that might be exploited to attack and harm the system. Some of the security vulnerabilities can be detected by static analysis tools and penetration testing, but usually, these suffer from relatively high false positive rates. Software vulnerability prediction (SVP) models can be used to categorize software components into vulnerable and neutral components before the software testing phase and likewise increase the efficiency and effectiveness of the overall verification process. The performance of a vulnerability prediction model is usually affected by the adopted classification algorithm, the adopted features, and data balancing approaches. In this study, we empirically investigate the effect of these factors on the performance of SVP models. Our experiments consist of four data balancing methods, seven classification algorithms, and three feature types. The experimental results show that data balancing methods are effective for highly unbalanced datasets, text-based features are more useful, and ensemble-based classifiers provide mostly better results. For smaller datasets, Random Forest algorithm provides the best performance and for the larger datasets, RusboostTree achieves better performance.Conference Object Softare Vulnerability Prediction using Extreme Learning Machines Algorithm(2019) Keçeli, Ali Seydi; Kaya, Aydın; Çatal, Çağatay; Tekinerdoğan, BedirSoftware vulnerability prediction aims to detect vulnerabilities in the source code before the software is deployed into the operational environment. The accurate prediction of vulnerabilities helps to allocate more testing resources to the vulnerability-prone modules. From the machine learning perspective, this problem is a binary classification task which classifies software modules into vulnerability-prone and non-vulnerability-prone categories. Several machine learning models have been built for addressing the software vulnerability prediction problem, but the performance of the state-of-the-art models is not yet at an acceptable level. In this study, we aim to improve the performance of software vulnerability prediction models by using Extreme Learning Machines (ELM) algorithms which have not been investigated for this problem. Before we apply ELM algorithms for selected three public datasets, we use data balancing algorithms to balance the data points which belong to two classes. We discuss our initial experimental results and provide the lessons learned. In particular, we observed that ELM algorithms have a high potential to be used for addressing the software vulnerability prediction problem.