Sahil Garg

Fuzzified Cuckoo based Clustering Technique for Network Anomaly Detection

Abstract With the increasing penetration of security threats, the severity of their impact on the underlying network has increased manifold. Hence, a robust anomaly detection technique, Fuzzified Cuckoo based Clustering Technique (F-CBCT), is proposed in this paper which operates in two phases: training and detection. The training phase is supported using Decision Tree followed by an algorithm based on hybridization of Cuckoo Search Optimization and K-means clustering. In the designed algorithm, a multi-objective function based on Mean Square Error and Silhouette Index is employed to evaluate the two simultaneous distance functions namely-Classification measure and Anomaly detection measure. Once the system is trained, detection phase is initiated in which a fuzzy decisive approach is used to detect anomalies on the basis of input data and distance functions computed in the previous phase. Experimental results in terms of detection rate (96.86%), false positive rate (1.297%), accuracy (97.77%) and F-Measure (98.30%) prove the effectiveness of the proposed model.

Renewable Energy-based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers

Cloud computing has emerged as one of the most popular technologies of the modern era for providing on-demand services to the end users. Most of the computing tasks in cloud data centers are performed by geodistributed data centers which may consume a hefty amount of energy for their operations. However, the usage of renewable energy resources with appropriate server selection and consolidation can mitigate the energy related issues in cloud environment. Hence, in this paper, we propose a renewable energy-aware multi-indexed job classification and scheduling scheme using container as-a-service for data centers sustainability. In the proposed scheme, incoming workloads from different devices are transferred to the data center which has sufficient amount of renewable energy available with it. For this purpose, a renewable energy-based host selection and container consolidation scheme is also designed. The proposed scheme has been evaluated using Google workload traces. The results obtained prove 15%, 28%, and 10.55% higher energy savings in comparison to the existing schemes of its category.

Bloom filter based optimization scheme for massive data handling in IoT environment

Abstract With the widespread popularity of big data usage across various applications, need for efficient storage, processing, and retrieval of massive datasets generated from different applications has become inevitable. Further, handling of these datasets has become one of the biggest challenges for the research community due to the involved heterogeneity in their formats. This can be attributed to their diverse sources of generation ranging from sensors to on-line transactions data and social media access. In this direction, probabilistic data structures (PDS) are suitable for large-scale data processing, approximate predictions, fast retrieval and unstructured data storage. In conventional databases, entire data needs to be stored in memory for efficient processing, but applications involving real time in-stream data demand time-bound query output in a single pass. Hence, this paper proposes Accommodative Bloom filter (ABF), a variant of scalable bloom filter, where insertion of bulk data is done using the addition of new filters vertically. Array of m bits is divided into b buckets of l bits each and new filters of size ‘ m ∕ k ′ are added to each bucket to accommodate the incoming data. Data generated from various sensors has been considered for experimental purposes where query processing is done at two levels to improve the accuracy and reduce the search time. It has been found that insertion and search time complexity of ABF does not increase with increase in number of elements. Further, results indicate that ABF outperforms the existing variants of Bloom filters in terms of false positive rates and query complexity, especially when dealing with in-stream data.

A Game of Incentives: An Efficient Demand Response Mechanism using Fleet of Electric Vehicles

With the explosive penetration of Electric Vehicles (EVs) in the last decade, the load on the existing grids have exaggerated manifold. This has led to severe demand-supply imbalances causing grid instability and reliability issues. Towards this end, Demand Reponse (DR) management has been identified as an important means to tackle this problem. Thus, in this paper, a robust Stackelberg Game has been proposed wherein the Utility Provider (UP) and fleet of EVs are assumed to be playing the roles of a competitive leader and followers, respectively. In the considered game, EVs charging problem has been formulated as a non-cooperative game; in which EVs decide their charging slot in accordance with the real-time electricity prices announced by the UP. The existence and uniqueness of the formulated Stackelberg Game has been theoretically proved in the paper. Further, the efficacy of the formulated game has been validated on real-time data traces obtained from Haryana State Electricity Board, India.

DROpS: A demand response optimization scheme in SDN-enabled smart energy ecosystem

Abstract With an exponential increase in the utilization of intellective appliances, meeting the energy demand of consumers by traditional power grids is a significant challenge. In integration, the amalgamation of electric vehicles, industrial Internet-of-Things (IoT), and smart communities with power grids has escalated the global energy demand. Consequently, the desideratum for a reliable energy supply and resilient energy ecosystem has incentivized the evolution of Smart Grids (SG). Such intelligent grids are equipped with autonomous controllers and advanced technologies like advanced metering infrastructure, smart sensors, and accounting management software. However, the existing demand response management and energy supply techniques are lagging behind in meeting the desired objectives in the SG ecosystem. In order to handle these challenges, a Demand Replication Optimization Scheme (DROpS) for the smart energy ecosystem is designed in this paper. In addition, a Multi-Leader Multi-Follower Stackelberg game is formulated in this paper to operate with the proposed scheme. However, the success of DROpS depends heavily on real-time communication between the consumers and the suppliers. Hence, dynamic and scalable network architecture is required to handle the seamless data generated by a sizably voluminous number of connected sensors, devices, and smart appliances deployed in the SG. For the successful operation of the architecture, a Software-Defined Networking (SDN)-enabled control scheme for flow management is additionally developed. In the proposed scheme, the SG ecosystem is divided into multiple zones such that the dedicated virtual SDN controllers are deployed for network resource utilization in an optimized manner. The proposed scheme is evaluated using a real smart home test bed and data traces from the Haryana power grid. The effectiveness of the proposed scheme is demonstrated in terms of significant gains observed for load variation and latency.

Fuzzy Based Efficient Mechanism for URL Assignment in Dynamic Web Crawler

World wide web (WWW) is a huge collection of unorganized documents. To build the database from this unorganized network, web crawlers are often used. The crawler which interacts with millions of web pages needs to be efficient in order to make a search engine powerful. This utmost requirement necessitates the parallelization of web crawlers. In this work, a fuzzy-based technique for uniform resource locater (URL) assignment in dynamic web crawler is proposed that utilizes the task splitting property of the processor. In order to optimize the performance of the crawler, the proposed scheme addresses two important aspects, (i) creation of crawling framework with load balancing among parallel crawlers, and (ii) making of crawling process faster by using parallel crawlers with efficient network access. Several experiments are conducted to monitor the performance of the proposed scheme. The results prove the effectiveness of the proposed scheme.