A Mobile Ad hoc Network (MANET) is a collection of autonomous self-organized nodes. They use wireless mediu m for co mmunicat ion, thus two nodes can communicate directly if and only if they are within each other"s transmission radius in a mu lti-hop fashion. Many conventional routing algorithms have been proposed for MANETs. An emerg ing area that has recently captured much attention in network routing researches is Swarm Intelligence (SI). Besides conventional approaches, many new researches have proposed the adoption of Swarm Intelligence for MANET routing. Swarm Intelligence (SI) refers to complex behaviors that arise fro m very simp le individual behaviors and interactions, which is often observed in nature, especially among social insects such as ants, bees, fishes etc. Although each individual has little intelligence and simp ly follows basic rules using local in formation obtained fro m the environ ment. Ants routing resembles basic mechanisms fro m distributed Swarm Intelligence (SI) in b iological systems and turns out to become an interesting solution where routing is a problem. Ants based routing is gaining more popularity because of its adaptive and dynamic nature. A nu mber of Swarm Intelligence (SI) based algorithms were p roposed by researchers. In this paper, we study bio-inspired routing protocols for MANETs.

Mobile Ad hoc Network (MANET) is an autonomous collection of mobile nodes that form a temporary network without of any existing network infrastructure or central access point. The popularity of these networks created security challenges as an important issue. The traditional routing protocols perform well with dynamically changing topology but are not designed to defense against security challenges. In this paper we discuss about current challenges in an ad hoc environment which includes the different types of potential attacks that are possible in the Mobile Ad hoc Networks that can harm its working and operation. We have done literature study and gathered information relating to various types of attacks. In our study, we have found that there is no general algorithm that suits well against the most commonly known attacks. But the complete security solution requires the prevention, detection and reaction mechanisms applied in MANET. To develop suitable security solutions for such environments, we must first understand how MANETs can be attacked. This paper provides a comprehensive study of attacks against mobile ad hoc networks. We present a detailed classification of the attacks against MANETs

Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.

Education acts as a soul in the overall societal development, in one way or the other. Aspirants, who gain their degrees genuinely, will help society with their knowledge and skills. But, on the other side of the coin, the problem of fake certificates is alarming and worrying. It has been prevalent in different forms from paper-based dummy certificates to replicas backed with database tampering and has increased to astronomic levels in this digital era. In this regard, an overlay mechanism using blockchain technology is proposed to store the genuine certificates in digital form and verify them firmly whenever needed without delay. The proposed system makes sure that the certificates, once verified, can be present online in an immutable form for further reference and provides a tamper-proof concealment to the existing certification system. To confirm the credibility of the proposed method, a prototype of blockchain-based credential securing and verification system is developed in ethereum test network. The implementation and test results show that it is a secure and feasible solution to online credential management system.

Abstract:The Communication Network model studied in this paper consists of two nodes connected in tandem with feedback for both the nodes. Each node has buffer and a transmitter for holding packets and for transmitting packets respectively. The packets after transmitted by the nodes may move to the next node or returned back to the same node for retransmission in feedback. It is considered that the arrival of packets at the nodes follows Non Homogeneous Poisson (NHP) process and transmission is characterized by Poisson process. Transmission rate of both nodes are adjusted before transmission by using Dynamic Bandwidth Allocation (DBA) policy. The model is evaluated using the difference-differential equations and a probability generating function of the number of packets in the buffer connected to the transmitter. Through mathematical modeling, performance measures including average number of packets in each buffer, the probability of emptiness of the network, the average waiting time in the buffer and in the network, throughput of the transmitters, utilization and the variance of the number of packets in the buffer are derived under transient conditions.