Jayanta Poray

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

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 thats 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.

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

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 performance analysis on elliptic curve cryptography in network security

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.

An analysis of security threats and countermeasures in VANET

Rapid advancement of Information technology and Communication, VANET(VehicularAd-hoc Networks) has received immence attention from academia, industry and government in all over world. Due to the change of life style, number of Vehicles on roads increases in last few years. Driving of Vahicles has not stopped from being more challenging and dangerous in day to day. Again Road infrastructure are already saturated. Safety distance between two adjacence vehicle and reasonable speed of vehicles are hardly respected. Drivers is going often with lack of enough attention. Tens of thousands of people die and hundreds of thousands get injured in traffic accidents all over the world in each year. So the life or death risk may be must special feature of VANETs. In the traditional networks or other emerging mobile networks, Security and privacy failures usually bring financial losses, where as in VANET, both security and privacy failures could be much more serious life or death risk. So VANET have emerged as an exciting research and application area. In this paper we have tried to discussed about the overview of VANET infrastructure, some applicatons and some sereous technical and security issues to implement an ideal VANET. We have also mentioned some major attacks on VANET. We hope the insight discussed here will help protocol designers and applications engineers to improve the services provided in VANET and assist drivers to make secure techniques.

Human computer interaction for sentiment analysis and opinion mining: A review

Inspite of much emphasis in the field of man machine interface, very little has been established till date. Researchers have studied many ways of communication between man and machine using signals from human beings and machine to help them communicate with each other in an easier way. Brain signal is such an example which can be used on a system for a variety of purposes. This can be useful in the medical, cognitive science as well as technological field in a wider aspect. Therefore, keeping this in mind a system can be implemented which can solve some common problems related to the brain, by dealing with conversion of brain signals into useful formats to solve such problems. There are several approaches to find the sentiment (emotion) from the EEG signals such that machine can interpret the exact or near meaning of the sentiment. Here, we have studied such approaches and showed how these methods could be adopted for further enhancements in this domain.

Application of graph theory in bigdata environment

In data driven age, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only in high volume, but also high in variety, velocity & veracity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Furthermore, decision makers need to be able to gain valuable insights from such varied and rapidly changing data, ranging from daily transactions to customer interactions and social network data. Graph theory is another flexible domain where we can able to analyze & predict and take decisions effortlessly, comparatively quicker & atmost accurately. In this paper we have reviewed and proposed certain graphical algorithms based on bigdata and analyse their effort towards decision making.

Towards a decision support system by the study of cell malfunctions for breast cancer

Breast cancer is one of the leading cause of death for women today and it is the most common cancer in developed countries. The cause and degree of the breast cancer are very much associated with the malfunctions of its tissues and cells. It is very hard and rigorous task for the doctors to observe the clinical records for many affected patients and regulate the therapy manually. Therefore, it is very much necessary to properly process the bulk amount of clinical records (containing cell details) automatically and come with the best possible treatment for the affected patients. In this work we have proposed a decision support system with the help of two data mining techniques; namely, decision tree learning and association rules mining. Clinical data have been studied, pre-processed and analyzed with the help of a data mining tool (e.g., WEKA). Finally, as an outcome we come with the decision support tool for practical purpose.