Bappaditya Jana

Bio: Bappaditya Jana is an academic researcher from Techno India University. The author has contributed to research in topics: Cryptography & Encryption. The author has an hindex of 5, co-authored 15 publications receiving 79 citations. Previous affiliations of Bappaditya Jana include Techno India.

A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment

Abstract: Cloud computing is on the edge of another revolution where resources are globally networked and can be shared user to user in easy way. Cloud computing is an emerging research domain which encompasses computing, storage, software, network, and other heterogeneous requirements on demand. Today, dynamic resource allocation and proper distribution of loads in cloud server are a challenging task. So task scheduling is an essential step to enhance the performance of cloud computing. Although lots of scheduling algorithms are used in cloud environment but still now no reasonably efficient algorithms are used. We have proposed a Modified Particle Swarm Optimisation (MPSO) technique where we have focused on two essential parameters in cloud scheduling such as average scheduling length and ratio of successful execution. According to the result analysis in simulation, Modified Particle Swarm Optimization (MPSO) technique shows better performance than Min-Min, Max-Min, and Standard PSO. Finally, critical future research directions are outlined.

A performance analysis on elliptic curve cryptography in network security

Abstract: The world wide web is a most well known electronic protocol which allows us to perform massive wireless communication like military communication, commercial and other private communication. But there is a tremendous security crisis such that data security, integrity, theft, loss and presence of infected applications for a authentic communication through digital medium that is why research and development on cryptography gets huge importance by every government as well as commercial sector and private sector. There are many robust cryptographic technique already have tested. Elliptic curve cryptography is a one type of public key cryptosystem like RSA, Diffie — Hellman. The main advantage of ECC is that it allows equal security for a smaller key size than the key size used RSA, that is why ECC reduces the processing overhead. This paper represents a brief review with some statistical results on how ECC has becomes the latest trend in the present and future cryptographic scenario.

Quantum cryptography: Overview, security issues and future challenges

Abstract: In recent years Quantum Cryptography gets more attention as well as becomes most promising cryptographic field for faster, effective and more secure communications. Quantum Cryptography is an art of science which overlap quantum properties of light under quantum mechanics on cryptographic task instead of current state of algorithms based on mathematical computing technology. Major algorithms for public key encryption and some digital signature scheme such as RSA, El Gamal cryptosystem, hash function are vulnerable at quantum adversaries. Most of the factoring problem can be broken by Shore's algorithm and quantum computer threatens other hand discrete logarithm problem. Our paper describes why modern cryptography contributes quantum cryptography, security issues and future goals of modern cryptography.

A Hybrid Task Scheduling Approach Based on Genetic Algorithm and Particle Swarm Optimization Technique in Cloud Environment

Abstract: As per current trends, cloud services are becoming more popular day by day. Because these services satisfy several demands for heterogeneous resources without using dedicated IT infrastructure. In cloud platform, user gets shared pool of resources through Internet irrespective of any geographical location. But cloud services need to handle a gigantic amount of request, as the number of users increases exponentially. In order to manage a pool of requests till now no effective scheduling mechanism is available in practice. So to minimize the time delay and optimize the overall complexity, suitable scheduling methodology is very much required. In our study, we have presented a novel scheme for scheduling algorithm after the enhancement of genetic algorithm and particle swarm optimization technique. We have proposed a methodology that can provide a better response time from cloud provider and minimize the waiting time for particular clients in cloud environment.

A novel scheme to detect and remove black hole attack in cognitive radio vehicular ad hoc networks(CR-VANETs)

Abstract: Now a days the rapid advancement of wireless communication, Cognitive Radio for vehicular Ad Hoc networks has received immense attention from researchers. Due to the ever increasing demand for more radio spectrum, Cognitive Radio Networks is a one of the solution for this crisis. Security is the one of the basic issue to implement a better CRN. CRN is established in a open communication environment that's why CRN are more vulnerable to security threats than wired networks. Black hole attack is one of the most common attack in CR-VANETs. If the Ad-hoc networks are affected by this attack, it not able to perform effectively. In this situation as a consequence the process of any event detection severely affected. The affected nodes can lead to discrepancies in data analysis and hence come with an erroneous report. We have proposed a simple scheme for recognition of malicious node in Black Hole attack in CR-VANET. In our proposed model, we used a Trusty Dynamic Software Agent (TDSA) which are employed for each node in VANETs and shares databases in the memory spaces of the neighboring nodes. We have compared the communication range(R) and distance between two adjacency nodes. Thus we can detect the connectivity of the two nodes and decide the actual alternate path. After recognition of malicious node we change the effected route and an alternate pathway have proposed. and thus we can eliminate black hole attack from CR-VANETs.

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

Abstract: Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.

On Line Learning In Neural Networks

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An efficient load balancing system using adaptive dragonfly algorithm in cloud computing

Abstract: With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

Security and Privacy Challenges in Vehicular Ad Hoc Networks

Abstract: The steady increase in the number of vehicles on the roads comes with an increased number of accidents and fatalities. The manufacturers’ interest in providing services to the driver (customer), along with safety applications, have contributed to connecting vehicles in networks on the fly (i.e., Ad Hoc networks), to provide certain services and information to the driver. These Ad Hoc networks consist of mobile vehicles that are located in a certain geographical zone and within a certain radius of each other, and communicate with each other or with road side units (RSUs) over the wireless medium. In addition, these mobile vehicles share some common characteristic, e.g., driving direction. These networks are well known as Vehicular Ad Hoc Networks (VANETs). From the extensive adoption and development of Internet of Things (IoT), and the integration of and convergence between VANETs and IoT, has emerged a new type of network known as Internet of Vehicles (IoV). VANETs, IoV and Intelligent Transportation Systems (ITS) have witnessed an explosive growth over the past two decades. This growth and the wide gamut of applications and services, these systems and networks have also increased the threats and attacks against these networks as well as raised many security and privacy concerns. In this book chapter, we describe the need for security in VANETs in terms of security requirements, current challenges in securing VANETs and present current security issues related to these networks. In particular, we deliver a comprehensive and up-to-date summary of threats and attacks in VANETs. Additionally, we present proposed solutions and countermeasures to mitigate these threats and attacks in order to secure and defend VANETs against them.