Detection and avoidance of routing attack in mobile ad-hoc network using intelligent node

The routing attacks are created in order to damage the network in Mobile Ad-hoc. Previously, Dempster-shafer theory introduced a solution for these routing attacks where it entirely works on the principle of Dempster rule with various important factors to mitigate these critical routing attacks. Previously the system contains an Intrusion detection mechanism which is used to create a message whenever the attacker attacks the network. This Intrusion detection system sends an alert message to each mobile node in the network, when the attacker attacks the network. Then, Routing table change Detector identifies exactly how many changes has occurred in each node after receiving the alert messages from the intrusion detection system and also it make some changes in the routing table of each node in the network. From these changes, the Intrusion detection system identifies the attackers and these attackers are isolated from the network. The main drawback of this existing system is whenever the attacker is occurred, the Intrusion detection system has to send an alert message every time and the routing table change detector has to make some changes in the routing table. In order to avoid these drawbacks, the knowledge based intelligent system is proposed. In this proposed system, initially a source node has to get an authorized path from the intelligent node (a node with high energy) to send a data to the destination node. This proposed system discussed with the four routing attacks such as route salvage, sleep deprivation, colluding miss relay and collision attack

A Review On Detection And Mitigation Of Black And Gray Hole Attacks In Manet

Abstract In wireless ad hoc networks, the absence of any control on packets forwarding, make these networks vulnerable by various deny of service attacks (DoS). A node, in wireless ad hoc network, counts always on intermediate nodes to send these packets to a given destination node. An intermediate node, which takes part in packets forwarding, may behave maliciously and drop packets which goes through it, instead of forwarding them to the following node. Such behavior is called black hole attack. In this paper, after having specified the black hole attack, a secure mechanism, which consists in checking the good forwarding of packets by an intermediate node, is required. In this paper a survey is taken from previous works

QUANTUM PHASE SHIFT FOR ENERGY CONSERVED SECURED DATA COMMUNICATION IN MANET

A Mobile Ad-Hoc Network(MANET) is a structure-less network where the mobile nodes randomly moved in any direction within the transmission range of the network. Due to this mobility, wide range of intrusion occurs in MANET. Therefore, Intrusion Detection Systems (IDS) are significant in MANETs to identify the malicious behavior. In order to improve the secured data communication an efficient Quantum Phase Shift Energy Conserved Data Security (QPSEC-DS) technique is introduced. The Quantum Phase Shift (QPS) technique is used for ensuring the security during the data transmission from sender to receiver in MANET. Initially, the quantum based approach is used to encrypt the information using QPS at the sender through secret key distribution. The receiver side also performs the same QPS, and then the encrypted bit is received successfully. This in turns attains the secured packet transmission without any malicious node in the MANET. Based on the phase shifting, the energy conservation between the sender and receiver is measured for transmitting the data packet using QPSEC-DS technique. Also, the enhanced Dynamic Source Routing (DSR) protocol is applied in QPSEC-DS technique is implemented to improve the energy management and secured data communication between the source and destination in an efficient manner. The QPSEC-DS technique conducts the simulations work on parameters including packet delivery ratio, energy consumption, communication overhead and end to end delay

Known Unknowns: Indeterminacy in Authentication in IoT

The Internet of Things (IoT), comprising a plethora of heterogeneous devices, is an enabling technology that can improve the quality of our daily lives, for instance by measuring parameters from the environment (e.g., humidity, temperature, weather, energy consumption, traffic, and others) or our bodies (e.g., health data). However, as with any technology, IoT has introduced a number of security and privacy challenges. Indeed, IoT devices create, process, transfer and store data, which are often sensitive, and which must be protected from unauthorized access. Similarly, the infrastructure that links with IoT, as well as the IoT devices themselves, is an asset that needs to be protected. The focus of this work is examining authentication in IoT. In particular, in this work we conducted a state-of-the-art review of the access control models that have been proposed, including both traditional access control models and emerging models that have recently been proposed and are tailored for IoT. We identified that the existing models cannot cope with indeterminacy, an inherent characteristic of IoT, which hinders authentication decisions. In this context, we studied the two known components of indeterminacy, i.e., uncertainty and ambiguity, and proposed a new model that handles indeterminacy in authentication in IoT environments

Location based services in wireless ad hoc networks

In this dissertation, we investigate location based services in wireless ad hoc networks from four different aspects - i) location privacy in wireless sensor networks (privacy), ii) end-to-end secure communication in randomly deployed wireless sensor networks (security), iii) quality versus latency trade-off in content retrieval under ad hoc node mobility (performance) and iv) location clustering based Sybil attack detection in vehicular ad hoc networks (trust). The first contribution of this dissertation is in addressing location privacy in wireless sensor networks. We propose a non-cooperative sensor localization algorithm showing how an external entity can stealthily invade into the location privacy of sensors in a network. We then design a location privacy preserving tracking algorithm for defending against such adversarial localization attacks. Next we investigate secure end-to-end communication in randomly deployed wireless sensor networks. Here, due to lack of control on sensors\u27 locations post deployment, pre-fixing pairwise keys between sensors is not feasible especially under larger scale random deployments. Towards this premise, we propose differentiated key pre-distribution for secure end-to-end secure communication, and show how it improves existing routing algorithms. Our next contribution is in addressing quality versus latency trade-off in content retrieval under ad hoc node mobility. We propose a two-tiered architecture for efficient content retrieval in such environment. Finally we investigate Sybil attack detection in vehicular ad hoc networks. A Sybil attacker can create and use multiple counterfeit identities risking trust of a vehicular ad hoc network, and then easily escape the location of the attack avoiding detection. We propose a location based clustering of nodes leveraging vehicle platoon dispersion for detection of Sybil attacks in vehicular ad hoc networks --Abstract, page iii

Indeterminacy-aware prediction model for authentication in IoT.

The Internet of Things (IoT) has opened a new chapter in data access. It has brought obvious opportunities as well as major security and privacy challenges. Access control is one of the challenges in IoT. This holds true as the existing, conventional access control paradigms do not fit into IoT, thus access control requires more investigation and remains an open issue. IoT has a number of inherent characteristics, including scalability, heterogeneity and dynamism, which hinder access control. While most of the impact of these characteristics have been well studied in the literature, we highlighted “indeterminacy” in authentication as a neglected research issue. This work stresses that an indeterminacy-resilient model for IoT authentication is missing from the literature. According to our findings, indeterminacy consists of at least two facets: “uncertainty” and “ambiguity”. As a result, various relevant theories were studied in this work. Our proposed framework is based on well-known machine learning models and Attribute-Based Access Control (ABAC). To implement and evaluate our framework, we first generate datasets, in which the location of the users is a main dataset attribute, with the aim to analyse the role of user mobility in the performance of the prediction models. Next, multiple classification algorithms were used with our datasets in order to build our best-fit prediction models. Our results suggest that our prediction models are able to determine the class of the authentication requests while considering both the uncertainty and ambiguity in the IoT system

A Dynamic Risk-Based Access Control Approach: Model and Implementation

Access control (AC) refers to mechanisms and policies that restrict access to resources, thus regulating access to physical or virtual resources of an information system. AC approaches are used to represent these mechanisms and policies by which users are granted access and specific access privileges to the resources or information of the system for which AC is provided. Traditional AC approaches encompass a variety of widely used approaches, including attribute-based access control (ABAC), mandatory access control (MAC), discretionary access control (DAC) and role-based access control (RBAC). Emerging AC approaches include risk adaptive access control (RAdAC), an approach that suggests that AC can adapt depending on specific situations. However, traditional and emerging AC approaches rely on static pre-defined risk mitigation tasks and do not support the adaptation of an AC risk mitigation process (RMP). There are no provided mechanisms and automated support that allow AC approaches to construct RMPs and to adapt to provide more flexible, custom-tailored responses to specific situations in order to minimize risks. Further, although existing AC approaches can operate in several knowledge domains at once, they do not explicitly take into account the relationships among risks related to different dimensions, e.g., security, productivity. In addition, although in the real world, risks accumulate over time, existing AC approaches do not appropriately provide means for risk resolution in situations in which risks accumulate as different, dangerous tasks impact risk measures. This thesis presents the definition, the implementation, and the application through two case studies of a novel AC risk-mitigation approach that combines dynamic RMP construction and risk assessment extended to include forecasting based on multiple risk-related utilities and events; provides support for a dynamic risk assessment that depends on one or multiple risk dimensions (e.g., security and productivity); offers cumulative risk assessment in which each action of interest can impact the risk-related utilities in a dynamic way; and presents an implementation of an adaptive simulation method based on risk-related utilities and events