By Onolaja Olufunmilola

Overview Introduction Motivation Trust, reputation and misbehaivour Literature review DDDAS Model description Applications Evaluation Publications Future work

Introduction Ad hoc and wireless sensor networks have gained popularity in recent years. Security is very important in these networks because sensor nodes have recently been developed for mission critical environments, such as,  Military and security monitoring,  Wildfire detection,  Human tracking and monitoring,  Land mine detection,  Battlefield surveillance and  Traffic regulation. The nature of these networks, such as node mobility, limited transmission power and dynamic formulation has led to the exposure to problems caused by misbehaving nodes in the network.

Motivation Previous researches have suggested the use of Public Key Infrastructure (PKI) and other security mechanisms, which require a lot of computation. They however, have been ineffective because of the inherent properties of nodes –  Limited computation,  Battery power and  Storage capabilities. Some proposed Reputation and Trust-Based Systems (RTBSs). While trying to solve security issues, the RTBSs introduce other problems.

Motivation The dynamic and volatile nature of mobile wireless networks makes it difficult to differentiate between normal and malicious network operations. This therefore, calls for an equally dynamic approach to identifying and isolating misbehaving nodes. – DDDAS (Dynamic Data Driven Application Systems)

Trust, reputation and misbehaivour When a node is said to be trusted, it implicitly means that the probability that it will perform an action or behave in a way that is beneficial or at least not detrimental in the network is high enough to consider engaging in some form of cooperation with the node [Gam88]. Reputation on the other hand, is the opinion of an entity about another; it is the level of trustworthiness of a node.

Trust, reputation and misbehaivour The difference between trust and reputation is that trust is active because it is a node’s belief in another node. On the other hand, reputation is passive because it is the perception that is formed by different nodes about a particular node. [AD06]

Trust, reputation and misbehaivour A node is said to be misbehaving when it deviates from the expected behaviour of nodes. Misbehaviour among nodes can either be in terms of routing or forwarding.  packet dropping  modification  fabricate

Literature Review Node Cooperation Enforcement CORE CONFIDANT MANETs Trust Enhanced Model High Integrity Networks FrameworkWSNs Event Based Framework Node Cooperation Enforcement CORE CONFIDANT MANETs Trust Enhanced Model High Integrity Networks FrameworkWSNs Event Based Framework

Summary table of reputation and trust models

Literature Review The models are plagued with outstanding issues because while they try to solve the problems, they introduce other problems into the network. Some outstanding problems include: Collusion attacks  Watchdog mechanism Lack of dynamism False praise and accusations Identity persistence The models are plagued with outstanding issues because while they try to solve the problems, they introduce other problems into the network. Some outstanding problems include: Collusion attacks  Watchdog mechanism Lack of dynamism False praise and accusations Identity persistence

Collusion Attack Using packet modification attack as an example: Suppose node A forwards a packet P through B to D, node C can decide to misbehave and B colludes with C. With the watchdog mechanism, it is possible that B does not report to A when C modifies the packet to P#.

Collusion Attack The problem of collusion is very important because its effects can considerably affect network performance and may hinder communication vital to fulfilling of the mission of ad hoc and sensor networks. [LJT04]

Why DDDAS? The highly dynamic and volatile nature of adhoc and sensor networks calls for an equally dynamic approach to identifying problems. The DDDAS paradigm is a novel approach of symbiotic relation between applications or simulations. In this paradigm, applications can accept and respond dynamically to new data injected into an executing application, and in reverse, such application systems have the ability to dynamically control the measurement processes.

Why DDDAS? The simulation can make predictions about an entity regarding how it will change and what its future state will be. The simulation is then continuously adjusted (feedback) with data gathered from the entity (measurement). Current researches in DDDAS focus on simulations of physical/artificial/social entities.  weather and climate prediction,  disaster recovery,  traffic management etc The paradigm offers the promise of improving modeling methods, and augmenting the analysis and prediction capabilities of application simulations

Model Description a. The concepts of the paradigm are applied to build a reputation system to address the issue of collusion among nodes, b. The dynamic data obtained is used to gain a better understanding and more accurate prediction of the level of trust, c. Incorporate the DDDAS paradigm to dynamically measure, simulate and control run-time behaviour, d.The simulation dynamically measures trust levels to determine the reputation of each node and will continually incorporate new measurements at runtime for the system to accurately determine and update the TVs.

Model Description In order to provide more secure networks (in terms of trusted communication, there are some requirements: Firstly, there is a requirement for monitoring the behaviour of nodes at runtime and providing feedback to the reputation system. Prediction of node behaviour, in order to have a more proactive approach to the detection of malicious members is another requirement. The DDDAS paradigm makes provisions to meet these requirements. Application of the concepts of the paradigm in our model provides dynamism in the detection of malicious nodes and prediction of future behaviour of each node.

Model Description

Trust Formulation tv new = (tv h +(w * tv o )) t w + 2 tv f = Σ(tv h ) + tv o n tv o = a * Σ (tv h ) n

Possible Applications  Criminal and terrorist monitoring  Military applications  Femtocells deployment

Evaluation The research objectives listed below will be evaluated. 1. Dynamic changes to ratings of nodes at runtime; 2. Predict the future behaviour of nodes; 3. Propose a framework, adaptable in different applications; 4. Detect misbehaving nodes using the simulation system. The research questions to be answered are: How useful is the DDDAS paradigm in providing security in a network? To what extent will the framework support dynamism? How dynamic is node trust rating? How accurate is the trust rating prediction? How applicable is the model in different network scenarios and applications? Has the semi-distributed architecture improved security?

Evaluation The tools identified for use in the evaluation stage are:  The ns-2 is a discrete event simulator targeted at networking research. The simulator will be used extensively for evaluation. The simulator will consist of a certain number of nodes in a specified space and simulation time.  ATAM is a technique for evaluating architectures, identifying risks and improving on architectures. Rigorous tests will be carried out using analysis and simulations. The model will be evaluated in terms of its effectiveness in achieving a better overall security. The success of the work will be based on fulfilling the objectives outlined and contributing to the body knowledge by proposing a dynamic framework for more secure (trusted) mobile networks.

Publication I will be attending the MObile and NEtworking Technologies for social applications (MONET09) workshop. The accepted paper - An Architecture for Dynamic Trust Monitoring in Mobile Networks will be presented, a copy is attached to this report. The final proceedings will be published by Springer Verlag as LNCS.

References [Gam88] D. Gambetta. Can we trust? Basil Blackwell, trust: making and breaking cooperative relations edition, [AD06] W. J. Adams and N. J. Davis. Tms: A trust management system for access control in dynamic collaborative environments. In Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference, volume 2006, pages 143 – 150, [Dou08] C. Douglas. Dynamic data driven applications systems - dddas In ICCS (3), pages 3 – 4, [LJT04] Z. Liu, A.W. Joy, and R.A. Thompson. A dynamic trust model for mobile ad hoc networks. In 10 th IEEE International Workshop on Future Trends of Distributed Computing Systems, pages 80 – 85, [MM02] P. Michiardi and R. Molva. Core: A collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks. In Advanced Communications and Multimedia Security, volume 100 of International Federation for Information Processing, pages 107–121, [BLB02] S. Buchegger and J.Y. Le Boudec. Performance analysis of the confidant protocol (cooperation of nodes: Fairness in dynamic ad-hoc networks). In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc, pages 226–36, [HWK04] Q. He, D.P. Wu, and P. Khosla. Sori: A secure and objective reputation-based incentive scheme for ad-hoc networks. In Proc. WCNC Wireless Communications and Networking Conference 2004 IEEE, volume 2 of IEEE Wireless Communications and Networking Conference, pages 825–30, [BVLT07] V. Balakrishnan, V. Varadharajan, P. Lucs, and U.K. Tupakula. Trust enhanced secure mobile ad-hoc network routing. In Advanced Information Networking and Applications Workshops AINAW’07, volume 1, pages 27 – 33, [GBS08] S. Ganeriwal, L. K. Balzano, and M. B. Srivastava. Reputation-based framework for high integrity sensor networks. ACM Transactions on Sensor Networks, 4(3):15:1 – 37, [CWHG08] H. Chen, H. Wu, J. Hu, and C. Gao. Event-based trust framework model in wireless sensor networks. In IEEE International Conference on Networking, Architecture, and Storage, pages 359 – 364, 2008.