Mayank Dave

Bio: Mayank Dave is an academic researcher from National Institute of Technology, Kurukshetra. The author has contributed to research in topics: Wireless sensor network & Digital watermarking. The author has an hindex of 25, co-authored 177 publications receiving 2271 citations. Previous affiliations of Mayank Dave include Shiv Nadar University.

A multi-criterion fuzzy logic intra-cluster and inter-cluster-based stable cluster head election approach in large scale WSN

Abstract: Extending the lifetime of wireless sensor networks WSNs is a major concern in recent years. The main problem seems in the recent proposed approaches is that these use direct communication among nodes with the rotation of cluster heads CHs periodically to distribute energy consumption among various cluster heads CHs. Energy saving mechanisms based only on metrics related to nodes' residual power cannot be directly applied to find stable CHs. The reason is that a sensor node or a CH having more residual power is willing to accept all requests, because it has enough residual battery power, therefore, much traffic will be injected to that node. In that case, a sharp decay of node's backup battery power. In this research paper, a multi-criterion fuzzy logic intra-cluster and inter-cluster based multi-hop routing protocol is proposed. The simulation results confirm that our proposed approach is more efficient than state-of-the-art approaches.

Towards Utilizing Blockchain for Countering Distributed Denial-of-Service (DDoS)

Abstract: Distributed denial of service (DDoS) attacks have been a matter of serious concern for network administrators in the last two decades. These attacks target the resources such as memory, CPU cycles, and network bandwidth in order to make them unavailable for the benign users, thereby violating availability, one of the components of cyber security. With the existence of DDoS-as-a-service on internet, DDoS attacks have now become more lucrative for the adversaries to target a potential victim. In this work, the authors focus on countering DDoS attacks using one of the latest technologies called blockchain. In inception phase, utilizing blockchain for countering DDoS attacks has proved to be quite promising. The authors also compare existing blockchain-based defense mechanisms to counter DDoS attacks and analyze them. Towards the end of the work, they also discuss possible future research directions in this domain.

Unified approach for QoS management with adaptation and renegotiation

Abstract: A new adaptation protocol for quality of service (QoS) adaptation with renegotiation is developed that allows a connection-oriented network like ATM to recover from the QoS violations in order to satisfy end-to-end QoS requirements. This protocol is applicable to networks using PNNI where the nodes are grouped together in peer groups hierarchically. It Is assumed that every node has a QoS/Route Monitor unit that receives QoS/Link State Update (LSU) messages from the network on a periodic or triggered basis. In addition to its usual functions, this monitor would also function as QoS agent, QoS manager or Connection QoS Manager depending on its location. The QoS/Route Monitor is responsible for sending or receiving the signalling messages required in the protocol. To facilitate functioning of the protocol, several signalling messages have been defined that characterize different control mechanisms required for the protocol and the network. Finally, a unified model for quality of service management in broadb...

Study and Implementation of Location-based Access control Mechanism in Cloud services

Abstract: Access to cloud-based services requires identity and access control management. This control management mechanism is required to provide security, to prevent data loss and protection from malware attacks. For security reasons, the tendency of using multi-factor authentication is widely used over single-factor authentication as single-factor authentication are more prone to security infringements. Here the use of location-based authentication is suggested as another level of authentication over primary level. By making the user’s location grounded at a particular point, attackers supposedly have more difficulty in compromising such a system. Authentication is the most important security feature during exchange of data between client and server. The location based authentication helps to add another layer of security on top of traditional access patterns. Most commonly, authentication schemes depend upon three factors: (1) known by the user e.g. personal identity number(PIN), password; (2) owned by the user e.g. token,etc.(3) individual identity of the user e.g. bio-metrics, such as voice recognition, fingerprints, retinal scans,etc. In this paper, we propose a location-based authentication scheme for cloud connectivity. The proposed mechanism consists of three parts: location registration, authentication and location verification. We aim to minimize the risk of identity theft by employing an extra layer to user access verification.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Abstract: Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.