Sensors for Detection of Misbehaving Nodes in MANETs

The fact that security is a critical problem when implementing mobile ad hoc networks (MANETs) is widely acknowledged. One of the different kinds of misbehavior a node may exhibit is selfishness. A selfish node wants to preserve its resources while using the services of others and consuming their resources. One way of preventing selfishness in a MANET is a detection and exclusion mechanism. In this paper, we focus on the detection and present different kinds of sensors that will find selfish nodes. First we present simulations that show the negative effects which selfish nodes cause in MANET. In the related work section we will analyze the detection mechanisms proposed by others. Our new detection mechanisms that we describe in this paper are called activity-based overhearing, iterative probing, and unambiguous probing. Simulation-based analysis of these mechanisms show that they are highly effective and can reliably detect a multitude of selfish behaviors

Fragility impact of RL based advanced air mobility under gradient attacks and packet drop constraints

The increasing utilization of unmanned aerial vehicles (UAVs) in advanced air mobility (AAM) necessitates highly automated conflict resolution and collision avoidance strategies. Consequently, reinforcement learning (RL) algorithms have gained popularity in addressing conflict resolution strategies among UAVs. However, increasing digitization introduces challenges related to packet drop constraints and various adversarial cyber threats, rendering AAM fragile. Adversaries can introduce perturbations into the system states, reducing the efficacy of learning algorithms. Therefore, it is crucial to systematically investigate the impact of increased digitization, including adversarial cyber-threats and packet drop constraints to study the fragile characteristics of AAM infrastructure. This study examines the performance of artificial intelligence(AI) based path planning and conflict resolution strategies under different adversarial and stochastic packet drop constraints in UAV systems. The fragility analysis focuses on the number of conflicts, collisions and fuel consumption of the UAVs with respect to its mission, considering various adversarial attacks and packet drop constraint scenarios. The safe deep q-networks (DQN) architecture is utilized to navigate the UAVs, mitigating the adversarial threats and is benchmarked with vanilla DQN using the necessary metrics. The findings are a foundation for investigating the necessary modification of learning paradigms to develop antifragile strategies against emerging adversarial threats

An acknowledgment-based scheme to defend against cooperative black hole attacks in optimized link state routing protocol

In this paper, we address the problem of cooperative black hole attack, one of the major security issues in mobile ad hoc networks. The aim of this attack is to force nodes in the network to choose hostile nodes as relays to disseminate the partial topological information, thereby exploiting the functionality of the routing protocol to retain control packets. In optimized link state routing (OLSR) protocol, if a cooperative black hole attack is launched during the propagation of topology control (TC) packets, the topology information will not be disseminated to the whole network which may lead to routing disruption. In this paper, we investigate the effects of the cooperative black hole attack against OLSR, in which two colluding MPR nodes cooperate in order to disrupt the topology discovery. Then we propose an Acknowledgment based technique that overcomes the shortcomings of the OLSR protocol, and makes it less vulnerable to such attacks by identifying and then isolating malicious nodes in the network. The simulation results of the proposed scheme show high detection rate under various scenarios. ©2008 IEEE

Stimulating cooperative behavior of autonomous devices - an analysis of requirements and existing approaches

In the context of mobile and wireless devices, an information system is no longer a centralized component storing all the relevant data nor is it a decentralized component governed by a common authority. Rather, the information spread across huge numbers of autonomous mobile and wireless devices owned by independent organizations and individuals can be regarded as a highly dynamic, virtual information system. For this vision to become reality, the autonomous devices involved need to be motivated to cooperate. This cooperation needs to occur not only on the application layer, but, depending on the network architecture, also on the lower layers from the link layer on upwards. In this report, we investigate on which protocol layers cooperation is needed and what constitutes uncooperative behavior. We then identify necessary properties of incentive schemes that encourage cooperation and discourage uncooperative behavior. In this context, we examine remuneration types that are a major constituent of incentive schemes. Finally, using the example of ad hoc networks, the most challenging technical basis of a wireless information system, we compare existing incentive schemes to these characteristics

A taxonomy of incentive patterns - the design space of incentives for cooperation

In ad hoc networks, devices must cooperate in order to compensate for the absence of infrastructure. Yet, autonomous devices are free to decide whether to cooperate or not. Hence, incentives are indispensable to induce cooperation between autonomous devices. Recently, several approaches have been suggested that stimulate cooperation among devices. In this report, we point out that these approaches fall short of exploiting the design space of incentives for cooperation. Therefore, we introduce incentive patterns as a means of systematically conceiving incentive schemes with respect to the specifics of the application environment. Based on economics, we derive several incentive patterns and discuss them with respect to a set of general characteristics. Consequently, we propose a taxonomy that classifies the derived incentive patterns. Lastly, we exemplify systematic design of incentive schemes in the context of our DIANE project