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 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 Abstract - - Abstract - - Abstract - This paper proposes a master-slave approach to simultaneously control two drones with the aim of carrying an object toward a goal. The proposed method utilizes the Double Deep Q-Learning (DDQN) technique to train a master agent to be able to carry an object toward a goal with the help of a slave agent. This procedure is implemented such that the master agent gathers the observations and specifies the actions to be made by itself and the slave agent. Indeed, the slave agent just applies a predefined action and does not process any input for producing the output. This manner of learning, leads to a unified convergence to an optimal solution compared to the situation in which each agent is trained separately. To verify the functionality of the proposed method, the algorithm is examined in the webots simulation environment. The simulations show that the introduced method has a good performance when controlling the drones to reach to the goal. The introduced method, other than algorithmic benefits which leads to a faster convergence of the model, suggests some reduction in the processing demand. The reason is that the learning procedure is guided by one of the agents and consequently only one of the agents is responsible for doing the calculations that lead to choosing the action. In this scenario, the slave agent does not require any processing resources for choosing the action and just simply applies a predefined action dictated by the master agent.

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.