Cyberattacks always remain the major threats and challenging issues in the modern digital world. With the increase in the number of internet of things (IoT) devices, security challenges in these devices, such as lack of encryption, malware, ransomware, and IoT botnets, leave the devices vulnerable to attackers that can access and manipulate the important data, threaten the system, and demand ransom. The lessons from the earlier experiences of cyberattacks demand the development of the best-practices benchmark of cybersecurity, especially in modern Smart Environments. In this study, we propose an approach with a framework to discover malware attacks by using artificial intelligence (AI) methods to cover diverse and distributed scenarios. The new method facilitates proactively tracking network traffic data to detect malware and attacks in the IoT ecosystem. Moreover, the novel approach makes Smart Environments more secure and aware of possible future threats. The performance and concurrency testing of the deep neural network (DNN) model deployed in IoT devices are computed to validate the possibility of in-production implementation. By deploying the DNN model on two selected IoT gateways, we observed very promising results, with less than 30 kb/s increase in network bandwidth on average, and just a 2% increase in CPU consumption. Similarly, we noticed minimal physical memory and power consumption, with 0.42 GB and 0.2 GB memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device with the deployed model. The ML models were able to demonstrate nearly 93% of detection accuracy and 92% f1-score on both utilized datasets. The result of the models shows that our framework detects malware and attacks in Smart Environments accurately and efficiently.

This paper proposes an algorithmic solution to Group Key Management (GKM) in Wireless Sensor Networks (WSN), which could address a single point of failure in cybersecurity. The paper moves away from the traditional (de)centralized and distributed solution in GKM and focuses on GKM decision making based on a) the context in which WSN and their nodes communicate, and b) the semantic which describes the environment where WSN and their nodes reside. The proposed algorithm defines which node, within the WSN, could start a re-keying process by generating a group key, and why/how this decision on the re-keying has been made. The algorithm is computable and thus it would be feasible to implement it in software applications built upon a set of WSN nodes in constantly changeable and dynamic mobile computing environments.

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