In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.

Importance of data security differs from sector to sector. However, data of sensitive nature are increasing day by day and thereby, it requires utmost attention towards the protection of data. Organizations from all over the world are getting involved to deploy the best cyber defense mechanism. Primary concerns for the organizations includes three common factors: people, processes, and technology. Smart national identity cards (smart cards) are immensely sensitive in nature. In Bangladesh, smart cards are being used for 22 services, which include banking, passports, driving licenses, etc. In addition, smart cards also hold bio-metric details for the cardholder such as retina scan, ten finger impressions, picture, and so on. Most of them are confidential data and prone to privacy breach. To address this issue, this paper propose a model for smart card management system using blockchain technology. In recent years, blockchain earned popularity among researchers to protect data from unlawful altercation. Adaptation of our proposed model will help the government of Bangladesh to secure citizens’ private information and bring transparency on their information management. Furthermore, it will provide access control capability to citizens in case of third-party information access requests.

This work is based on developing a system for the Bell's palsy, a type of facial nerve paralysis, patients to help them in passive exercise, which is one of the major ways to recovery. This exercise method will be provided in the form of computer game so the user will be able to perform exercise without the hardship that comes with active exercise. It will also show how much this system helps the patients and how its performance is analyzed by the users' i. e. the patients. We will also show that how passive exercise is preferable to current active one. As per initial experimentation, we found out that the proposed gaming approach for Bell's palsy recovery is useful to the patient.