Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract With the advent of new technologies, using various formats of digital gadgets is becoming widespread. In today's world, where everyday tasks are inevitable without technology, this extensive use of computers paves the way for malicious activity. Malware is one of the well-known and widely used means utilized for doing destructive activities by malicious attackers. Producing malware from scratch is somewhat difficult, so attackers tend to obfuscate existing malware and prepare it to become an unrecognizable program. Since creating new malware from an old one using obfuscation is a creative task, there are some drawbacks to identifying obfuscated malwares. In this research, we propose a solution to overcome this problem by converting the code to an image in the first step and then using a semi-supervised approach combined with contrastive learning. In this case, an obfuscation in the malware bytecode corresponds to an augmentation in the image. Hence, by utilizing meaningful augmentations, which simulate some obfuscation changes and combine them to generate complex ambiguity procedures, our proposed solution is able to construct, learn, and detect a wide range of obfuscations. This work addresses two issues: 1) malware classification despite the data deficiency and 2) obfuscated malware detection by training on non-obfuscated malwares. According to the results, the proposed method overcomes the data shortage problem in malware classification, as its accuracy is 90.1% when just 10% of data is used for training the model. Moreover, training on basic malwares without obfuscation achieved 96.21 percent accuracy in detecting obfuscated malware.

Abstract Spectrum-Based Fault Localization (SBFL) techniques are widely used and studied. These techniques are straightforward, low-cost, and fast in comparison to other fault localization techniques, such as Mutation-Based and Learning-Based ones. However, the accuracy of these techniques is always controversial. The main criticism of these techniques refers to the mere use of coverage and spectra information. Hence, this study seeks to improve SBFL techniques by enhancing their accuracy while maintaining simplicity, aiming to make them applicable for regression faults. To achieve this, the first step involves integrating SBFL techniques with two features: (1) Change and (2) Test Case Weight, which utilizes the same coverage and spectrum data in a novel way. Additionally, the study examines the impact of incorporating Data and Control Dependency into the formula for fault localization accuracy. It also considers the suspiciousness scores generated by SBFL formulae as a feature.

Three SBFL techniques—Tarantula, Ochiai, and Jaccard—are applied in this study both as a feature and as a baseline for result comparison. The findings reveal that combining SBFL suspiciousness scores with Change and Test Case Weight significantly enhances performance by identifying at least 27% more faults at the Top-3 ranking. Furthermore, integrating these features with Data Dependency leads to even greater improvements, locating minimum 45% more faults at the Top-3 ranking compared to aforementioned SBFL formulae. Overall, this study emphasizes the limitations of existing SBFL formulae while highlighting the advantages of augmenting SBFL with additional features and repurposing spectral information to achieve greater accuracy in fault localization.

Abstract Abstract— This research proposes a novel approach to create a foundation for dynamic difficulty adjustment (DDA) within computer games that use procedural content generation (PCG), utilizing imitation learning to optimize gameplay. When PCG is used in creating the levels and enemies within a game, the difficulty adjustment must be ensured so that the game is not too hard or too easy for each player. However, PCG is random by nature, and thus, the developers may have a challenging task of adjusting the difficulty for each player in such games. The study aims to address these limitations by developing a foundation for DDA models based on imitation learning. The proposed model incorporates an imitation learning component, referred to as the 'Clone,' which replicates the player’s behavior, alongside an enemy creator agent that leverages procedural content generation (PCG) to design enemies. By analyzing the Clone's performance against these procedurally generated enemies, the system ensures the creation of fair and engaging levels. To this end, a 2D platformer Unity game using PCG was developed, and imitation learning was utilized through Unity's ML-agents module. These models were used to mimic the players' play-style to predict the player's performance in PCG-generated levels. Three separate models were created to mimic five players. It was observed that two of these models could mimic players' performance, showing that this method can be used to implement DDA.

Abstract Augmented reality technology integrates information with the environment in real-time. The capability to add information to objects and interact with users according to their requests can be an enjoyable and useful experience. Most Augmented Reality systems have a fixed static database containing registered objects. To enhance Augmented Reality applications, dynamic information sources can be used. An information source such as the web which has expanded and up-to-date data, could be an appropriate alternative. Using these information sources in real-time enables the use of Augmented Reality on a wider scale.
This paper introduces an Augmented Reality model based on online content is presented. In this research, case studies are human faces and context-based information is a virtual element, i.e., information about the target face. Using web information resources dynamizes the system in a variety of environments. Furthermore, the user interacts with the system through question-answering. Deep learning methods have been used for information retrieval and question-answering. Moreover, Answers to frequently asked questions that are confirmed by users are stored in the database for faster response time.
The experimental results reveal that users can interact with the Augmented Reality system through question-answering and augment the required information with a speed of 0.642 seconds and an F-score of 87.9% on the SQuAD dataset.