An Overview of Security Challenges in Vehicular Ad-Hoc Networks

© 2017 IEEE. Vehicular Ad hoc Networks (VANET) is emerging as a promising technology of the Intelligent Transportation systems (ITS) due to its potential benefits for travel planning, notifying road hazards, cautioning of emergency scenarios, alleviating congestion, provisioning parking facilities and environmental predicaments. But, the security threats hinder its wide deployment and acceptability by users. This paper gives an overview of the security threats at the various layers of the VANET communication stack and discuss some of the existing solutions, thus concluding why designing a security framework for VANET needs to consider these threats for overcoming security challenges in VANET

Quality of Service Provisioning with modified IEEE 802.11 MAC Protocol

There has been a phenomenal increase in the demand of quality-of-service (QoS) in wireless networks over the years due to rapid growth in the number of wireless and mobile devices. Such devices are in use to access Internet and QoS aware applications such as video conferencing, voice-over IP, interactive video-on-demand and many other multimedia applications. wireless local area networks (WLANs) confirming to the IEEE 802.11 standard have become extremely popular at an unprecedented rate. As a result, WLAN networks are gaining the momentum and making their way into residential, commercial, industrial and public areas. These trends are more and more accelerated in places like airports, hotels and coffee shop, this typically has many floating end users. The time stringent applications are delay sensitive that require throughput and delay bound creates an urgent need for QoS support in WLANs

A Synchronized Shared Key Generation Method for Maintaining End-to-End Security of Big Data Streams

A large number of mission critical applications ranging from disaster management to smart city are built on the Internet of Things (IoT) platform by deploying a number of smart sensors in a heterogeneous environment. The key requirements of such applications are the need of near real-time stream data processing in large scale sensing networks. This trend gives birth of an area called big data stream. One of the key problems in big data stream is to ensure the end-to-end security. To address this challenge, we proposed Dynamic Prime Number Based Security Verification (DPBSV) and Dynamic Key Length Based Security Framework (DLSeF) methods for big data streams based on the shared key derived from synchronized prime numbers in our earlier works. One of the major shortcomings of these methods is that they assume synchronization of the shared key. However, the assumption does not hold when the communication between Data Stream Manager (DSM) and sensing devices is broken. To address this problem, this paper proposes an adaptive technique to synchronize the shared key without communication between sensing devices and DSM, where sensing devices obtain the shared key re-initialization properties from its neighbours. Theoretical analyses and experimental results show that the proposed technique can be integrated with our DPBSV and DLSeF methods without degrading the performance and efficiency. We observed that the proposed synchronization method also strengthens the security of the models

Personal data broker instead of blockchain for students’ data privacy assurance

Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.Peer ReviewedPostprint (author's final draft

A Novel Multi-Path Anonymous Randomized Key Distribution Scheme for Geo Distributed Networks

A major concern in distributed networks is the ability to provide acceptable levels of security. This is achieved by using encryption and authentication mechanisms that depend on encryption keys. However, given the ever-expanding nature of the network, it is difficult to keep setting up authorities that can aid the key-exchange process. This paper presents a novel solution to the challenge of exchanging keys of a large, distributed network without the need to set up additional authorities. The key-exchange scheme presented takes advantage of features such as packet anonymity, random selection and a multi-path approach for the exchange process. The paper also discusses the effectiveness of the proposed scheme against various threat scenarios

Secure-GLOR: An adaptive secure routing protocol for dynamic wireless mesh networks

© 2017 IEEE. With the dawn of a new era, digital security has become one of the most essential part of any network. Be it a physical network, virtual network or social network, the demand for secure data transmission is ever increasing. Wireless mesh networks also stand the same test of security as the legacy networks. This paper presents a secure version of the Geo-Location Oriented Routing (GLOR) protocol for wireless mesh networks, incorporating a multilevel security framework. It implements authentication using the new features of the network model and enables encryption throughout the network to provide high levels of security

A Novel Hybrid Authentication Model for Geo Location Oriented Routing in Dynamic Wireless Mesh Networks

Authentication is an essential part of any network and plays a pivotal role in ensuring the security of a network by preventing unauthorised devices/users access to the network. As dynamic wireless mesh networks are evolving and being accepted in various fields, there is a strong need to improve the security of the network. It’s features like self-organizing and self-healing make it great but get undermined when rigid authentication schemes are used. We propose a hybrid authentication scheme for such dynamic mesh networks under three specified scenarios; full authentication, quick authentication and new node authentication. The proposed schemes are applied on our previous works on dynamic mesh routing protocol, Geo location Oriented Routing Protocol (GLOR Simulation results show our proposed scheme is efficient in terms of resource utilization as well as defending against security threats

MSGR: A Mode-Switched Grid-Based Sustainable Routing Protocol for Wireless Sensor Networks

© 2013 IEEE. A Wireless Sensor Network (WSN) consists of enormous amount of sensor nodes. These sensor nodes sense the changes in physical parameters from the sensing range and forward the information to the sink nodes or the base station. Since sensor nodes are driven with limited power batteries, prolonging the network lifetime is difficult and very expensive, especially for hostile locations. Therefore, routing protocols for WSN must strategically distribute the dissipation of energy, so as to increase the overall lifetime of the system. Current research trends from areas, such as from Internet of Things and fog computing use sensors as the source of data. Therefore, energy-efficient data routing in WSN is still a challenging task for real-Time applications. Hierarchical grid-based routing is an energy-efficient method for routing of data packets. This method divides the sensing area into grids and is advantageous in wireless sensor networks to enhance network lifetime. The network is partitioned into virtual equal-sized grids. The proposed mode-switched grid-based routing protocol for WSN selects one node per grid as the grid head. The routing path to the sink is established using grid heads. Grid heads are switched between active and sleep modes alternately. Therefore, not all grid heads take part in the routing process at the same time. This saves energy in grid heads and improves the network lifetime. The proposed method builds a routing path using each active grid head which leads to the sink. For handling the mobile sink movement, the routing path changes only for some grid head nodes which are nearer to the grid, in which the mobile sink is currently positioned. Data packets generated at any source node are routed directly through the data disseminating grid head nodes on the routing path to the sink

A fast and self-adaptive on-line learning detection system

© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability