Synchronous Relaying Of Sensor Data

In this paper we have put forth a novel methodology to relay data obtained by inbuilt sensors of smart phones in real time to remote database followed by fetching of this data . Smart phones are becoming very common and they are laced with a number of sensors that can not only be used in native applications but can also be sent to external nodes to be used by third parties for application and service development

Intrusion Detection System for Platooning Connected Autonomous Vehicles

The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks

A Review on Data Mining Techniques towards Various Intrusion Detection Systems in MANET

The computer network and its application over the various platforms has the tremendous growth. This exploits vulnerabilities over the network and it is very tough to solve the security issues. The vast number of intrusion over the network leads to failure of network. There are many IDS available for detecting the intrusion. This paper argues the various Data mining techniques available and how it helps to achieve the goal with higher accuracy on IDS. Also this paper discusses and compare with traditional approaches of DM practices on IDS. Also the paper tells the user about IDS and IPS for the network with data mining approaches and suggesting the user for selecting the preferable approach for finding the intrusion over the network effectively

The Modified Secure AODV Routing Protocol for Black Hole Attack in Manet

Mobile Adhoc Network is gathering of portable nodes which are actively structuring a momentary network without utilizing any pre accessible network infrastructure or central management. Each node in MANET not only provides as a specific terminal but also performs as a router to form a route. While a source node plans to send data to an intended node, packets are moved from the middle nodes. An Adhoc routing protocol is a classical method that supervises how nodes opt any route and in which manner they have to route packets among computing devices in a MANET. Because of different factors with lack of infrastructure, deficiency of already established trust relationship among the various nodes and dynamic topology, the MANET routing protocols are weak to different routing attacks. In contrast to conventional wired networks, such type attacks are executed simply in MANET because of the unsupervised entrance to the wireless medium. The malicious exploitation of various routing information results in the diffusion of wrong routing information which could eventually guide to network failure. One of these attacks in the existing wireless routing protocol like Ad-hoc on demand Distance Vector (AODV) Routing protocol is the Black Hole Attack against network truthfulness. In this attack, the data packets doesn’t arrive at the destination node, thus data loss happens. There is number of detection and protection methods to reduce the intruder that achieve the black hole attack. Therefore, this paper proposes Modified Secure AODV routing protocols (MSAODV) found on threshold evaluation and cryptographic verification. In this paper, the black hole attack and the proposed MSAODV protocols are simulated in the Network Simulator NS-2 under different MANET circumstances and their performances are evaluated on various parameters like Packet drop ratio, routing overload, throughput etc. Keywords: AODV, Black hole, gray hole, worm hole attack, MANET, AOMD

A novel secure routing scheme using probabilistic modelling for better resistivity against lethal attacks

Study towards Wireless Adhoc Network dates two decades back with various researchers evolving up with new solutions towards addressing its problems. Irrespective of various other problems, the issues related to the secure routing is yet unsolved owing to massively increasing fatal strategies of the adversary. Review of existing literature shows that the existing secure routing scheme can only govern over the stated attacks reducing the applicability in case of dynamic attacks. Therefore, this manuscript introduces a novel probabilistic model which offers the capability to wireless nodes to identify the malicious behavior and react accordingly. Different from existing intrusion prevention system, the proposed system allows the malicious node to participate in the data forwarding process and exhaust its resources with no chance of launching an attack. The simulated outcome of the study shows that the proposed secure routing scheme offers better data forwarding characteristic in contrast to the existing system in the aspect of intrusion detection and secure data transmission

Intelligent black hole detection in mobile AdHoc networks

Security is a critical and challenging issue in MANET due to its open-nature characteristics such as: mobility, wireless communications, self-organizing and dynamic topology. MANETs are commonly the target of black hole attacks. These are launched by malicious nodes that join the network to sabotage and drain it of its resources. Black hole nodes intercept exchanged data packets and simply drop them. The black hole node uses vulnerabilities in the routing protocol of MANETS to declare itself as the closest relay node to any destination. This work proposed two detection protocols based on the collected dataset, namely: the BDD-AODV and Hybrid protocols. Both protocols were built on top of the original AODV. The BDD-AODV protocol depends on the features collected for the prevention and detection of black hole attack techniques. On the other hand, the Hybrid protocol is a combination of both the MI-AODV and the proposed BDD-AODV protocols. Extensive simulation experiments were conducted to evaluate the performance of the proposed algorithms. Simulation results show that the proposed protocols improved the detection and prevention of black hole nodes, and hence, the network achieved a higher packet delivery ratio, lower dropped packets ratio, and lower overhead. However, this improvement led to a slight increase in the end-to-end delay

Analysis of Behavioral Characteristics of Multiple Blackhole Attacks with TCP and UDP Connections in Mobile ADHOC Networks based on Machine Learning Algorithms

In Mobile Adhoc Networks (MANET’s), a suit of nodes which are under mobility work together to transmit data packets in a multiple-hop manner without relying on any fixed or centralized infrastructure. A significant obstacle in managing these networks is identifying malicious nodes, or "black holes". To detect black holes, we proposed a method involves broadcasting a Cseq to the neighboring nodes and awaiting the node's response is utilized. This Network is simulated with 25 number of nodes connected with TCP connection and observed the different behavioural characteristics of nodes. Then the connections are changed to UDP and observed the characteristics. Then characteristics are analyzed with different machine learning algorithms. The network is simulated in NS2 environment