The advancements in networking technologies have introduced the Internet of Everything (IoE) and smart living concepts. The main idea behind making everything smarter is to improve individuals’ quality of life. An excellent example of such a technology is smart healthcare which provides efficient, sustainable, and real-time human services. However, data security and privacy are among the most important challenges of smart healthcare applications. Blockchain (BC) has been considered as a promising solution for the secure management of healthcare data due to its immutability and transparency features. However, there is a trade-off between transparency and user data privacy which is a prominent challenge in adopting BC for healthcare applications. Some researchers have considered user data privacy and proposed a few solutions; however, data owner's access control desire has not been considered in the state-of-the-art models. In this paper, to overcome the trade-off challenge between transparency and access control, we propose an architecture (so-called BCHealth) that enables data owners to define their desired access policies over their privacy-sensitive healthcare data. BCHealth is composed of two separate chains for storing access policies and data transactions. We address the real-world development challenges of BC, i.e., scalability, delay, and overhead, by adopting a clustering approach. Our extensive experimental analysis proves the efficiency of BCHealth (in terms of computation and processing time) and its resilience against several security attacks.@en

The rapid increase in the number of malicious programs has made malware forensics a daunting task and caused users' systems to become in danger. Timely identification of malware characteristics including its origin and the malware sample family would significantly limit the potential damage of malware. This is a more profound risk in Cyber-Physical Systems (CPSs), where a malware attack may cause significant physical damage to the infrastructure. Due to limited on-device available memory and processing power in CPS devices, most of the efforts for protecting CPS networks are focused on the edge layer, where the majority of security mechanisms are deployed. Since the majority of advanced and sophisticated malware programs are combining features from different families, these malicious programs are not similar enough to any existing malware family and easily evade binary classifier detection. Therefore, in this article, we propose a novel multilabel fuzzy clustering system for malware attack attribution. Our system is deployed on the edge layer to provide insight into applicable malware threats to the CPS network. We leverage static analysis by utilizing Opcode frequencies as the feature space to classify malware families. We observed that a multilabel classifier does not classify a part of samples. We named this problem the instance coverage problem. To overcome this problem, we developed an ensemble-based multilabel fuzzy classification method to suggest the relevance of a malware instance to the stricken families. This classifier identified samples of VirusShare, RansomwareTracker, and BIG2015 with an accuracy of 94.66%, 94.26%, and 97.56%, respectively.@en