The Internet of Things (IoT) paradigm has applications in many domains and is growing these days progressively. The applications are e-business, e-healthcare, and e-transportation. Recently, the container microservices-based Mobile Cloud Computing (MCC) has gained popularity, a lightweight framework compared to the monolithic virtual machine-based system. MCC combines fog nodes or cloud nodes with a base station to run the applications. However, storing the sensitive data of IoT applications on the untrusted nodes and failure of services are critical challenges in the existing architecture. This study proposes a novel microservices-based by combining fog and cloud services with efficient schemes. The first scheme is the Latency Aware Task Assignment Algorithm, which determines the optimal assignment of tasks to minimize the makespan of all applications. The second scheme is Fully Homomorphism Encryption, which ensures data security before an offload to any external assistance for execution. The final one is the Failure Aware, which handles any transient failure during application execution in the architecture. The experimental results show that the recommended architecture improved resource utilization, and the proposed schemes satisfied the security demand while reducing the makespan of applications.

Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real-world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features.