Halstead’s Complexity Measure of a Merge Sort and Modified Merge Sort Algorithms

Complexity measuring tools in computer science are deployed to measure and compare different characteristics of algorithms to find the best one to solve a particular problem or that suits a specific situation. Also,  this is used to measure the complexity of a software program without running the program itself. Given this, Halstead’s complexity metrics are deployed to compare the efficiency of two external sorting methods: the Merge Sort and the Modified Merge Sort Algorithms. The methodology used in achieving this lies in extracting operators and operands from the C_sharp (C#) implemented program of the two algorithms. Six Halstead metrics are evaluated using these operators and operands as parameters. The results show that the modified merge sort algorithm is much more efficient than the conventional Merge sort as it has a lower Program Volume, Program Difficulty, and Program Effort even though the advantage of a higher Intelligence content goes to the merge sort algorithm

Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection

Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection