ABSTRACT Recently, the usage of Internet of Things (IoT) devices has been increasing drastically and thus producing huge amounts of data. To handle this big data, researchers have proposed hybrid model SDP (Serverless data pipeline) approaches, supporting intermediate data processing across the fog topology. However, these predetermined path SDPs never consider the task expectation requirements and current node capabilities, and data flows through fixed paths. But in shared network environments like fog and cloud, we cannot expect that the resources will always be reserved for the pipeline. This predetermined SDP's behavior ultimately produces a low Quality of Experience (QoE) for the user regarding pipeline performance. This paper proposes a QoE‐based dynamic path SDP, which can dynamically reroute its data path on the nodes and offer better QoE. For the node selection, we used the hierarchical fuzzy‐based placement strategy and demonstrated the approach with an SDP image processing application. The designed QoE‐based SDP outperforms the Predetermined SDP in handling real‐time data without any pipeline interrupts. The experiment results showed that the proposed method is almost free from the bottleneck effect with a loss of 2% packet drops. In Predetermined SDP, we found 74% of packet loss when the load on the SDP is significant.

With recent advances in cyber-attacks, traditional rule-based intrusion detection systems are not adequate to meet the present-day challenge. Recently machine learning-based intrusion detection system (IDS) has been proposed to detect such advanced/unknown cyber-attacks. The performance of such machine learning-based IDS largely depends upon the feature set used. Generally, using more features increases the accuracy of attack detection and increases detection time. This paper proposes a new network intrusion detection system based on an artificial neural network (ANN), which uses a multi-objective genetic algorithm to satisfy the requirements: accuracy of attack detection and faster response. The performance of the proposed method is tested by using the KDD'99, NSL-KDD, and CIC-IDS-2017 datasets. The results show that the performance of the proposed method is better than the existing methods. Besides, the new process provides a trade-off on the number of features used vs. accuracy and time for detection.

Abstract Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in‐time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE‐aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic. The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.44%, 37.83% improvement in RG, and 28.46%, 37.5%, 23.07% improvement in PTRR, when compared with QoE‐aware, randomized, FIFO algorithms, respectively.

In data grids, the fast and proper replica selection decision leads to better resource utilization due to reduction in latencies to access the best replicas and speed up the execution of the data grid jobs. In this paper, we propose a new strategy that improves replica selection in data grids with the help of the reduct concept of the Rough Set Theory (RST). Using Quickreduct algorithm the unsupervised clustering is changed into supervised reducts. Then, Rule algorithm is used for obtaining optimum rules to derive usage patterns from the data grid information system. The experiments are carried out using Rough Set Exploration System (RSES) tool.

Many existing grid authorization systems adopt an inefficient structure of storing security policies for the available resources. That leads to huge repetitions in checking security rules. One of the efficient mechanisms that handle these repetitions is the Hierarchical Clustering Mechanism (HCM) [1]. HCM reduces the redundancy in checking security rules compared to the Brute Force Approach as well as the Primitive Clustering Mechanism (PCM). Further enhancement of HCM is done to make it suitable for dynamic environments [2]. However, HCM is not totally free from repetitions. Moreover, HCM is an expensive process in terms of decision tree size and memory consuming. In this paper, a new Rough Set based PCM is proposed which increases the efficiency of the authorization process and further reduces the redundancy.