Ved P Mishra

Bio: Ved P Mishra is an academic researcher from Amity University. The author has contributed to research in topics: Intrusion detection system & Big data. The author has an hindex of 7, co-authored 50 publications receiving 180 citations.

The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action

Abstract: Digital twins, Internet of Things (IoT), block chains, and Artificial Intelligence (AI) may redefine our imagination and future vision of globalization. Digital Twin will likely affect most of the enterprises worldwide as it duplicates the physical model for remote monitoring, viewing, and controlling based on the digital format. It is actually the living model of the physical system which continuously adapts to operational changes based on the real-time data from various IoT sensors and devices and forecasts the future of the corresponding physical counterparts with the help of machine learning/artificial intelligence. We have investigated the architecture, applications, and challenges in the implementation of digital twin with IoT capabilities. Some of the major research areas like big data and cloud, data fusion, and security in digital twins have been explored. AI facilitates the development of new models and technology systems in the domain of intelligent manufacturing.

Conversation to Automation in Banking Through Chatbot Using Artificial Machine Intelligence Language

Abstract: Artificial Machine Intelligence is a very complicated topic. It involves creating machines that are capable of simulating knowledge. This paper examines some of the latest AI patterns and activities and then provides alternative theory of change in some of the popular and widely accepted postulates of today. Based on basic A.I. (Artificial Intelligence) structuring and working for this, System-Chatbots are made (or chatter bots). The paper shows that A.I is ever improving. As of now there isn't enough information on A.I. however this paper provides a new concept which addresses machine intelligence and sheds light on the potential of intelligent systems. The rise of chatbots in the finance sector is the latest disruptive force that has changed the way customers interact. In the banking industry, the introduction of Artificial Intelligence has driven chatbots and changed the face of the interaction between bank and customers. The banking sector plays an important role in development into any country. It also explores the existing usability of chatbot to assess whether it can fulfill customers ever-changing needs.

Maximum Information Measure Policies in Reinforcement Learning with Deep Energy-Based Model

Abstract: we provided a framework for the acquisition of articulated electricity regulations for consistent states and actions, but it has only been attainable in summarised domains since then. Developers adapt our environment to learning maximum entropy policies, leading to a simple Q-learning service, which communicates the global optimum through a Boltzmann distribution. We could use previously approved amortized Stein perturbation theory logistic regression rather than estimated observations from that distribution form to obtain a stochastic diffusion network. In simulated studies with underwater and walking robots, we confirm that the entire algorithm's cost provides increased exploration or term frequency that allows the transfer of skills between tasks. We also draw a comparison to critical actor methods, which can represent on the accompanying energy-based model conducting approximate inference. Misleading multiplayer uses the recompense power to ensure that the user is further from either the evolutionary algorithms but has now evolved to become a massive task in developing intelligent exploration for deep reinforcement learning. In a misleading game, nearly all cutting-edge research techniques, including those qualify superstition yet, even with self-recompenses, which achieves enhanced outcomes in the sparse re-ward game, often easily collapse into global optimization traps. We are introducing another exploration tactic called Maximum Entropy Expand (MEE) to remedy this shortage (MEE). Based on entropy rewards but the off-actor-critical reinforced learning algorithm, we split the entity adventurer policy into two equal parts, namely, the target rule and the adventure policy. The explorer law is used to interact with the world, and the target rule is used to create trajectories, with the higher precision of the targets to be achieved as the goal of optimization. The optimization goal of the targeted approach is to maximize extrinsic rewards in order to achieve the global result. The ideal experience replay used to remove the catastrophic forgetting issue that leads to the operator's information becoming non-normalized during the off-exploitation period. To prevent the vulnerable, diverging, and generated by the dangerous triad, an on-policy form change is used specifically. Users analyse data likening our strategy with a region technique for deep learning, involving grid world experimentation techniques and deceptively recompense Dota 2 environments. The case illustrates that the MME strategy tends to be productive in escaping the current paper's coercive incentive trap and learning the correct strategic plan.

Process Mining in Intrusion Detection-The Need of Current Digital World

Abstract: In the current age of digital world, all users of Internet/Network as well as organizations are suffering from intrusions which results into data/information are theft/loss. In the present manuscript concept of intrusion detection system (IDS) were discussed along with its types and basic approaches. It is found that signature analysis, expert system, data mining etc. still using for IDS. Survey was given related to cybercrime incidents across various industry sectors. After analyzing the attacks on networks of organizations in different industry sectors it is found that still attacks like DDoS are not preventable. Comparison of data mining algorithms used for intrusion detection was also done. Various methods to implement the algorithm along with the advantages and disadvantages were also discussed in detail. Because of the disadvantages like over fitting, slow testing speed, unstable algorithms etc., intruders in the network are still active. To avert these shortcomings there is a need to develop real-time intrusion detection and prevention system through which data/information can be protected and saved in real-time basis before a severe loss is experienced. The real-time prevention is possible only if alerts are received instantly without delays. For this purpose, process mining could be used. This technique gives instant time alerts with real time analysis so as to prevent intrusions and data loss.

Green Cloud Computing - A Greener Approach To IT

Abstract: Cloud Computing is one of the most widely emergent areas in today’s IT sector. It helps a number of people to use various services through their own devices with the help of the internet. It provides an environment which has low cost, easy to use and also consumes less power with virtualization. VM’s are required to be managed by a number of task scheduling algorithms to make sure that the less amount of energy consumption takes place. This paper deals with the importance of green cloud computing and how it can provide alternatives to the IT sector in terms of the energy consumption, load on data centers, VM average load and the task distribution. All these experiments have been performed by using a green cloud simulator in which three different algorithms are being used such as DENS, Round Robin and Green Schedulers.

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.

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Abstract: Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Abstract: Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.