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Intrusion Detection Techniques in Mobile Ad Hoc and Wireless Sensor Networks - IEEE October 2007

Intrusion Detection Techniques in Mobile Ad Hoc and Wireless Sensor Networks - IEEE October 2007. CMSC 681 - Advanced Computer Networks Oleg Aulov. MANET and WSN. No wires, Limited battery life, Limited memory and processing capability

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Intrusion Detection Techniques in Mobile Ad Hoc and Wireless Sensor Networks - IEEE October 2007

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  1. Intrusion Detection Techniques in Mobile Ad Hoc and Wireless Sensor Networks - IEEE October 2007 CMSC 681 - Advanced Computer Networks Oleg Aulov

  2. MANET and WSN • No wires, Limited battery life, Limited memory and processing capability • No base stations, Mobile nodes, Nodes relay data (act as routers) • Usually no centralized authority • Deployed in adverse or hostile environment • Prevention sec.-key distrib. Mgmt. schemes -doesn’t work once the node is compromised and the secrets leak. Insiders can cause greater damage.

  3. IDS-second line of defence • IDS - dynamically monitors the system to detect compromise of confidentiality, availability and integrity. • Two common types - • misuse based - stores database of known attacks • anomaly based - creates normal profile of system states or user behaviors (difficult to built, mobility challenges) • Specification based - manually developed specs, time-consuming

  4. ID in MANET - attacks • Routing logic compromise - blackhole, routing update storm, fabrication, • Traffic Distortion - dropping, coruption, flooding • Others - rushing, wormhole, spoofing

  5. MANET - Existing Research-Zhang et al • Agent attached to each node, performs ID & response individually • Unsupervised method to construct & select feature set (dist, velocity, # hops, etc) • Pattern classification problem - apply RIPPER(decision tree for rule induction) & SVM Light (support vector machine, when data cannot be classified by set of features) algorithms • Post Processing - to eliminate false alarms

  6. MANET - Existing Research Huang et al • Cross-Feature Analysis-learning based method to capture correlation patterns. • L featires - f1,f2,…,fL • fi - feature characterizing topology or route activities • Solve classification problem - • Create Set Ci:{f1,…,fi-1,fi+1,…,fL}, used to identify temporal correlation between one feature and all the other features. • Ci - very likely to predict in normal circumstances, very unlikely during attack

  7. MANET - Existing Research Huang and Lee • Collaboration with neighbors - broader ID range - more accurate, more information bout attacks • Cluster based detection scheme - FSM - Initial, Clique, Done, LostAd hoc On Demand Distance Vector (AODV) algorithm • EFSA - detect state and transition violations • Specification based approach, detects abnormal patterns and anomalous basic events.

  8. MANET - Existing Research Marti et al • Watchdog and Pathrater to identify and respond to routing misbehaviors. • Each node verifies that his data was forwarded correctly.DSR - dynamic source routing • Rate routes and use more reliable ones.

  9. MANET - Existing Research Tseng et al • Based on AODV - specification based ID • Detects run time violations • FSM - specify behaviors of AODV • Maintain RREP and RREQ messages

  10. MANET - Existing Research Sun et al • Use Markov Chains to characterize normal behaviors • Motivated by ZBIDS (zone based) - locally generated alerts inside the zone • Gateway Nodes - broadcast alerts within the zone • IDMEF (message exchange format) - presented to facilitate interoperability of IDS agents.

  11. ID in WSN

  12. Secure Localization • GPS not feasible • Utilization of beacon packets and beacon nodes • Du et al - utilize deployment knowledge to confirm beacon integrity • Liu et al - filter out malicious location references using • Mean square error • Compute inconsistency • Voting based location estimation

  13. Secure Aggregation • Wagner - robust statistics for resilient aggregation, truncation, trimming • Yang - Secure Hop by Hop Aggregation Protocol (SDAP) • Divide and conquer • Commit and attest • Grubbs’ test Buttyan - RANSAC paradigm for resilient aggregation. maximum likehood estimation

  14. Future Research Directions • Extended Kalman Filter Based Aggregation - light weight solution for estimation of neighbor monitoring features • Integration of Mobility and ID in MANET - consideration to use link change rate as an indication of mobility. • Collaboration of IDM and SMM (sys. Mon.) - to address a problem of detecting abnormal event vs. false alarm. - ask the surrounding nodes to confirm

  15. Questions ???

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