Adithya Krishna S.

With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for malware classification. Multiple DNN architectures can be designed and trained to detect and classify malware binaries. Results demonstrate the potential of DNNs in accurately classifying malware with high accuracy rates observed across different malware types. Additionally, the… Show more

With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for malware classification. Multiple DNN architectures can be designed and trained to detect and classify malware binaries. Results demonstrate the potential of DNNs in accurately classifying malware with high accuracy rates observed across different malware types. Additionally, the feasibility of deploying these DNN models on edge devices to enable real-time classification, particularly in resource-constrained scenarios proves to be integral to large IoT systems. By optimizing model architectures and leveraging edge computing capabilities, the proposed methodologies achieve efficient performance even with limited resources. This study contributes to advancing malware detection techniques and emphasizes the significance of integrating cybersecurity measures for the early detection of malware and further preventing the adverse effects caused by such attacks. Optimal considerations regarding the distribution of security tasks to edge devices are addressed to ensure that the integrity and availability of large-scale IoT systems are not compromised due to malware attacks, advocating for a more resilient and secure digital ecosystem. Show less

Reinforcement Learning (RL) has developed as a powerful machine learning paradigm that has achieved great success in a variety of applications such as gaming, robotics, and natural language processing. As academics become more interested in using RL in real-world contexts, they face new problems and complications. This work investigates the key issues of RL in real-world contexts, such as dealing with high-dimensional and continuous state-action spaces, as well as coping with partial… Show more

Reinforcement Learning (RL) has developed as a powerful machine learning paradigm that has achieved great success in a variety of applications such as gaming, robotics, and natural language processing. As academics become more interested in using RL in real-world contexts, they face new problems and complications. This work investigates the key issues of RL in real-world contexts, such as dealing with high-dimensional and continuous state-action spaces, as well as coping with partial observability via state estimation. It investigates the trade-offs between real-world experience and simulations, considering the expense and feasibility of getting real-world physical data for training. The importance of reward shaping in leading RL agents in real-world contexts is being researched. This paper discusses the limitations of RL in real-world applications, as well as potential future possibilities for developing the subject. Understanding these obstacles and opportunities will allow academics and practitioners to fully realize the potential of RL in tackling real-world problems and opening new paths for revolutionary applications. Show less

The visually impaired face a plethora of problems. The primary problem they face is navigating from one place to another. The detection of obstacles in the user's proximity is another challenge that needs to be addressed. This paper provides a comparative study of various real-time image recognition and object detection methods that might help develop effective navigation systems for the visually impaired.