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Source Code Authorship Attribution (SCAA) is to find the real author of source code in a corpus. Though, it is a privacy threat to open-source programmers, but, it may be significantly helpful to develop forensic based applications. Such as, ghostwriting detection, copyright dispute settlements, and other code analysis applications. The efficient features extraction is the key challenge for classifying real authors of specific source codes. In this paper, the Program Dependence Graph with Deep Learning (PDGDL) methodology is proposed to identify authors from different programming source codes. First, the PDG is implemented to extract control and data dependencies from source codes. Second, the preprocessing technique is applied to convert PDG features into small instances with frequency details. Third, the Term Frequency Inverse Document Frequency (TFIDF) technique is used to zoom the importance of each PDG feature in source code. Fourth, Synthetic Minority Over-sampling Technique (SMOTE) is applied to tackle the class imbalance problem. Finally, the deep learning algorithm is applied to extract coding styles' features for each programmer and to attribute the real authors. The deep learning algorithm is further fine-tuned with drop out layer, learning error rate, loss and activation function, and dense layers for better accuracy of results. The proposed work is analyzed on 1000 programmers' data, collected from Google Code Jam (GCJ). The dataset contains three different programming languages, i.e., C++, Java, C#. The results are appreciable in outperforming the existing techniques from the perspective of classification accuracy, precision, recall, and f-measure metrics.INDEX TERMS Code authorship attribution, program dependence graph, deep learning, software forensics and security, software plagiarism.

To reduce the risks of malicious software, malware detection methods using machine learning have received tremendous attention in recent years. Most of the conventional methods are based on supervised learning, which relies on static features with definite labels. However, recent studies have shown the models based on supervised learning are vulnerable to deliberate attacks. This work tends to expose and demonstrate the weakness in these models. A DQEAF framework using reinforcement learning to evade anti-malware engines is presented. DQEAF trains an AI agent through a neural network by constantly interacting with malware samples. Actions are a set of reasonable modifications, which do not damage samples' structure and functions. The agent selects the optimal sequence of actions to modify the malware samples, thus they can bypass the detection engines. The training process depends on the characteristics of the raw binary stream features of samples. The experiments show that the proposed method has a success rate of 75%. The efficacy of the proposed DQEAF has also been evaluated by other families of malicious software, which shows good robustness.INDEX TERMS Anti-malware engines evasion, deep machine learning, malware detection, reinforcement learning.

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Summary The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack.

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