Game Theory Approaches in Taxonomy of Intrusion Detection for MANETs

MANETs are self configuring networks that are formed by a set of wireless mobile nodes and have no fixed network infrastructure nor administrative support. Since transmission range of wireless network interfaces is limited, forwarding hosts may be needed. Each node in a wireless ad hoc network functions is as both a host and a router. Due to their communication type and resources constraint, MANETs are vulnerable to diverse types of attacks and intrusions so, security is a critical issue. Network security is usually provided in the three phases: intrusion prevention, intrusion detection and intrusion tolerance phase. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theory approaches to address the network security issues. This paper reviews some existing game theory solutions which are designed to enhance network security in the intrusion detection phase. Keywords: Mobile Ad hoc Network (MANET), Intrusion detection system (IDS), Cluster head, host based, Game theory

Security in networks of unmanned aerial vehicles for surveillance with an agent-based approach inspired by the principles of blockchain

Unmanned aerial vehicles (UAVs) can support surveillance even in areas without network infrastructure. However, UAV networks raise security challenges because of its dynamic topology. This paper proposes a technique for maintaining security in UAV networks in the context of surveillance, by corroborating information about events from different sources. In this way, UAV networks can conform peer-to-peer information inspired by the principles of blockchain, and detect compromised UAVs based on trust policies. The proposed technique uses a secure asymmetric encryption with a pre-shared list of official UAVs. Using this technique, the wrong information can be detected when an official UAV is physically hijacked. The novel agent based simulator ABS-SecurityUAV is used to validate the proposed approach. In our experiments, around 90% of UAVs were able to corroborate information about a person walking in a controlled area, while none of the UAVs corroborated fake information coming from a hijacked UAV

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

Mechanism design and game theoretical models for intrusion detection

In this thesis, we study the problems related to intrusion detection systems in Mobile Ad hoc Networks (MANETs). Specifically, we are addressing the leader election in the presence of selfish nodes, the tradeoff between security and IDS's resource consumption, and the multi-fragment intrusion detection via sampling. To balance the resource consumption among all the nodes and prolong the lifetime of a MANET, the nodes with the most remaining resources should be elected as the leaders. Selfishness is one of the main problems facing such a model where nodes can behave selfishly during the election or after. To address this issue, we present a solution based on the theory of mechanism design. More specifically, the solution provides nodes with incentives in the form of reputations to encourage nodes in participating honestly in the election process. The amount of incentives is based on the Vickrey-Clarke-Groves (VCG) mechanism to ensure that truth-telling is the dominant strategy of any node. To catch and punish a misbehaving elected leader, checkers are selected randomly to monitor the behavior of a leader. To reduce the false-positive rate, a cooperative game-theoretic model is proposed to analyze the contribution of each checker on the catch decision. A multi-stage catch mechanism is also introduced to reduce the performance overhead of checkers. Additionally, we propose a series of local election algorithms that lead to globally optimal election results. Note that the leader election model, which is known as moderate model is only suitable when the probability of attacks is low. Once the probability of attacks is high, victims should launch their own IDSs. Such a robust model is, however, costly with respect to energy, which leads nodes to die fast. Clearly, to reduce the resource consumption of IDSs and yet keep its effectiveness, a critical issue is: When should we shift from moderate to robust mode? Here, we formalize this issue as a nonzero-sum non-cooperative game-theoretical model that takes into consideration the tradeoff between security and IDS resource consumption. Last but not least, we consider the problem of detecting multi-fragments intrusions that are launched from a MANET targeting another network. To generalize our solution, we consider the intrusion to be launched from any type of networks. The detection is accomplished by sampling a subset of the transmitted packets over selected network links or router interfaces. Given a sampling budget, our framework aims at developing a network packet sampling strategy to effectively reduce the success chances of an intruder. Non-cooperative game theory is used to express the problem formally. Finally, empirical results are provided to support our solutions

Multi-Level Multi-Objective Programming and Optimization for Integrated Air Defense System Disruption

The U.S. military\u27s ability to project military force is being challenged. This research develops and demonstrates the application of three respective sensor location, relocation, and network intrusion models to provide the mathematical basis for the strategic engagement of emerging technologically advanced, highly-mobile, Integrated Air Defense Systems. First, we propose a bilevel mathematical programming model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder\u27s penetration path through a defended region. Next, we formulate a multi-objective, bilevel optimization model to relocate surviving sensors to maximize an intruder\u27s minimal expected exposure to traverse a defended border region, minimize the maximum sensor relocation time, and minimize the total number of sensors requiring relocation. Lastly, we present a trilevel, attacker-defender-attacker formulation for the heterogeneous sensor network intrusion problem to optimally incapacitate a subset of the defender\u27s sensors and degrade a subset of the defender\u27s network to ultimately determine the attacker\u27s optimal penetration path through a defended network