Software Defined Network (SDN) is a flexible paradigm that provides support for a variety of data-intensive applications with real-world smart Internet of Things (IoT) devices. This emerging architecture updates with the managing ability and network control. Still, the benefits are challenging to achieve due to the presence of intruder flow into the network. The research topic of intrusion detection and prevention system (IDPS) has grasped the attention to reduce the effect of intruders. Distributed Denial of Service (DDoS) is a targeted attack that develops malicious traffic is flooded into a particular network device. These intruders also involve even with legitimate network devices, the authenticated device will be compromised to inject malicious traffic. In this paper, we investigate the involvement of intruders in three-Tier IDPS with regard to user validation, packet validation and flow validation. Not all the authentication users can be legitimate, since they are compromised, so that the major contribution is to identify all the compromised devices by knee analysis of the packets. Routers are the edge devices employed in first tier which is responsible to validate the IoT user with RFID tag and encrypted signature. Then the authenticated user’s packets are submitted into second tier with switches that validates the packets using type-II fuzzy filtering. Then the key features are extracted from packets and they are classified into normal, suspicious and malicious. The mismatched packets are analyzed in controllers which maintain two queues as suspicious and normal. Then suspicious queue packets are classified and predicted using deep learning method. The proposed work is experimented in OMNeT++ environment and the performances are evaluated in terms of intruder Detection Rate, Failure Rate, Delay, Throughput and Traffic Load.

The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.

For conserving energy, duty cycle is defined by setting up the active and sleep periods of network nodes. In beacon enabled networks, to provide support for duty cycle, the IEEE 802.15.4 standard uses optional super-frame structure. This duty cycle is usually fixed and does not consider the topology changes that often occur in dynamic sensor networks. In this paper, existing energy conserving duty cycling approaches for 802.15.4 networks especially the adaptive duty cycling techniques for wireless sensor networks are summed up. Also, this paper highlights the shortcomings of the proposals in the literature, such as induced additional latency, so that they may not support the practical scenarios of Internet of Things (IoT). Further, this study highlights a gross shortcoming that relative performance comparison of RL-based proposals cannot be performed without using a benchmarking framework and real test-bed environment. In this paper, we have presented the future research directions that would lay the foundation for successful development of energy efficient RL-based duty-cycling techniques.

Edit distance is the most widely used method to quantify similarity between two strings. We investigate the problem of similarity search under edit distance. Given a collection of sequences, the goal of similarity search under edit distance is to find sequences in the collection that are similar to a given query sequence where the similarity score is computed using edit distance. The canonical method of computing edit distance between two strings uses a dynamic programming-based approach that runs in quadratic time and space, which may not provide results in a reasonable amount of time for large sequences. It advocates for parallel algorithms to reduce the time taken by edit distance computation. To this end, we present scalable parallel algorithms to support efficient similarity search under edit distance. The efficiency and scalability of the proposed algorithms is demonstrated through an extensive set of experiments on real datasets. Moreover, to address the problem of uneven workload across different processing units, which is mainly caused due to the significant variance in the size of the sequences, different data distribution schemes are discussed and empirically analyzed. Experimental results have shown that the speedup achieved by the hybrid approach over inter-task and intra-task parallelism is 18 and 13, respectively.