The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they constantly offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cyber security threats in IoT are better than the state of the art methods.

In the cloud-based Internet of Things (IoT) environments, quantifying uncertainty is an important element input to keep the acceptable level of reliability in various configurations. In this paper, we aim to address the pricing model of delivering data over the cloud while taking into consideration the dynamic uncertainty factors such as network topology, transmission/reception energy, nodal charge and power, and computation capacity. These uncertainty factors are mapped to different nodes with varying capabilities to be processed using Artificial Intelligence (AI)-based algorithms. Accordingly, we aim to find a way to calculate and predict the price per big data service over the cloud using AI and deep learning. Therefore, in this paper, we propose a framework to address big data delivery issues in cloud-based IoT environments by considering uncertainty factors. We compare the performance of the framework using two AI-based techniques called Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA) in both centralized and distributed versions. The use of AI techniques can be applied in multilevel to provide a kind of deep learning to further improve the performance of the system under study. The results reveal that the distributed algorithm outperforms the centralized one. In addition, the results show that the GA has lower running time compared to the SAA in all the test cases such as 68% of improvement in the centralized version, and 66% of improvement in the distributed version in case when the size of uncertainty array is 256. Moreover, when the size of uncertainty array increases, the results show 60% speed up in the distributed GA compared to its centralized version. The improvements achieved would help the service providers to actually improve their profit using the proposed framework

Internet of Things (IoT) and Mobile Edge Computing (MEC) technology acts as a significant part of daily lives to facilitate control and monitoring of objects to revolutionize the ways that human interacts with physical world. IoT system includes large volume of data with network connectivity, power, and storage resources to transform data into meaningful information. Blockchain has decentralized nature to provide useful mechanism for addressing IoT challenges. Blockchain is distributed ledger with fundamental attributes, namely recorded, transparent, and decentralized. Blockchain formed participants in distributed ledger to record the transactions and communicate with other through trustless method. Security is considered as the most valuable features of Blockchain. IoT and Blockchain are emerging ideas for creating the applications to share the intrinsic features. Several existing works has been developed for the integration of blockchain with IoT. But, Blockchain protocols in the state-of-the-art works with IoT failed to consider the computational loads, delays, and bandwidth overhead which lead to new set of problems. The review estimates main challenges in integration of Blockchain and IoT technologies to attain high-level solutions by addressing the shortcomings and limitations of IoT and Blockchain technologies.

Energy-efficient retrofits embrace enhancement of the building envelope through climate control strategies, employment of building-integrated renewable energy technologies, and insulation for a sustainable city. Building envelope improvements with insulation is a common approach, yet decision-making plays an important role in determining the most appropriate envelope retrofit strategy. In this paper, the main objective is to evaluate different retrofit strategies (RS) through a calibrated simulation approach. Based on an energy performance audit and monitoring, an existing building is evaluated on performance levels and improvement potentials with basic energy conservation measures. The considered building is experimentally monitored for a full year, and monitoring data are used in calibrating the simulation model. The validation of the base model is done by comparing the simulation analysis with the experimental investigation, and good agreement is found. Three different retrofit strategies based on Intervention of minor (RS1), Moderate (RS2), and Major (RS3) are analyzed and juxtaposed with the base model to identify the optimal strategy of minimizing energy consumption. The result shows that total energy intensity in terms of the percentage reduction index is about 16.7% for RS1, 19.87 for RS2, and 24.12% for RS3. Hence, RS3 is considered the optimal retrofit strategy and is further simulated for a reduction in carbon dioxide (CO₂) emissions and payback investigation. It was found that the annual reduction in CO₂ emissions of the building was 18.56%, and the payback period for the investment was 10.6 years.