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System call analysis is a behavioral malware detection technique that is popular due to its promising detection results and ease of implementation. This study describes a system that uses system call analysis to detect malware that evade traditional defenses. The system monitors executing processes to identify compromised hosts in production environments. Experimental results compare the effectiveness...
Software watermarking is a tool used to combat software piracy by embedding identifying information into a program. Most existing proposals for software watermarking have the shortcoming that they heavily rely on the stealth of watermark to prevent adversaries removing marks. Besides, the watermark is separate from the original program and can be destroyed via fairly straightforward semantics-preserving...
Malware proliferation has become a serious threat to the Internet in recent years. Most of the current malware are subspecies of existing malware that have been automatically generated by illegal tools. To conduct an efficient analysis of malware, estimating their functions in advance is effective when we give priority to analyze. However, estimating malware functions has been difficult due to the...
In the online job recruitment domain, accurate classification of jobs and resumes to occupation categories is important for matching job seekers with relevant jobs. An example of such a job title classification system is an automatic text document classification system that utilizes machine learning. Machine learning-based document classification techniques for images, text and related entities have...
Software defect prediction is important for improving software quality. Defect predictors allow software test engineers to focus on defective modules. Cross-Project Defect Prediction (CPDP) uses data from other companies to build defect predictors. However, outliers may lower prediction accuracy. In this study, we propose a transfer learning based model called VAB-SVM for CPDP robust in handling outliers...
Bug reporting and fixing the reported bugs play a critical part in the development and maintenance of software systems. The software developers and end users can collaborate in this process to improve the reliability of software systems. Various end users report the defects they have found in the software and how these bugs affect them. However, the same defect may be reported independently by several...
In order to solve the problems of traditional SVM classifier for software defect prediction, this paper proposes a novel dynamic SVM method based on improved cost-sensitive SVM (CSSVM) which is optimized by the Genetic Algorithm (GA). Through selecting the geometric classification accuracy as the fitness function, the GA method could improve the performance of CSSVM by enhancing the accuracy of defective...
This paper proposes a hybrid approach for Vietnamese word segmentation. The approach combines a dictionary-based method and a machine learning method to detect word boundaries in Vietnamese text by comparing English-Vietnamese pairs. We also point out several characteristics of Vietnamese which affect the Vietnamese word segmentation task and word alignment of English-Vietnamese text. Moreover, we...
Bugs are inevitable in software projects. Resolving bugs is the primary activity in software maintenance. Developers, who fix bugs through code changes, are naturally important participants in bug resolution. However, there are other participants in these projects who do not perform any code commits. They can be reporters reporting bugs; people having a deep technical know-how of the software and...
Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization(PSO) and Support Vector Machine(SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing...
Recently, the rapid growth of globalization requires writing a large number of multilingual texts. However, Japanese PC users need to switch the input mode between Japanese and the Latin alphabet on conventional Japanese input method. That is cumbersome. Meanwhile, the solution system using a dictionary is hard to maintain because new words are created every year with high frequency. This paper proposes...
Embedded microcontrollers are employed in an increasing number of applications as a target for the implementation of classification systems. This is true for example for the fields of sports, automotive and medical engineering. However, important challenges arise when implementing classification systems on embedded microcontrollers, which is mainly due to limited hardware resources. In this paper,...
File-type Identification (FTI) is an important problem in digital forensics, intrusion detection, and other related fields. Using state-of-the-art classification techniques to solve FTI problems has begun to receive research attention, however, general conclusions have not been reached due to the lack of thorough evaluations for method comparison. This paper presents a systematic investigation of...
Grooming attack recognition is a complex issue that is difficult to address using simple word matching in order to identify potential hazard for minor users. In this paper, the utilization of document classification to create patterns from real dialogs is proposed. Furthermore, a decision making method that results in generating proper warning signals based on the classification results is introduced...
A critical item of a bug report is the so-called "severity", i.e. the impact the bug has on the successful execution of the software system. Consequently, tool support for the person reporting the bug in the form of a recommender or verification system is desirable. In previous work we made a first step towards such a tool: we demonstrated that text mining can predict the severity of a given...
In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This paper extends the idea of predicting software faults by using an ensemble of classifiers which has been shown to improve classification performance in other research fields. Benchmarking results on...
Assigning a bug to the right developer is a key in reducing the cost, time, and efforts for developers in a bug fixing process. This assignment process is often referred to as bug triaging. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set-based modeling of bug-fixing expertise of developers. Bugzie considers a system to have multiple technical aspects,...
The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results...
This paper describes the making of the e-Culturas corpus from written texts by 10-11-year-old-children. These texts have been written from the memory of some event in which the kid has felt fear, hapiness or sadness. The purpose of the corpus in this report is the training of an automatic classifier for these sentiments. The results of the evaluation of the classifier are hopeful, in spite of being...
Recently, machine learning classifiers have emerged as a way to predict the existence of a bug in a change made to a source code file. The classifier is first trained on software history data, and then used to predict bugs. Two drawbacks of existing classifier-based bug prediction are potentially insufficient accuracy for practical use, and use of a large number of features. These large numbers of...
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