A Pay-and-Stay model for tackling intruders in hybrid wireless mesh networks

Abstract

Intrusion handling in wireless mesh networks (WMNs) is a relatively less addressed topic. The difficulty may lie in the fact that there are other wireless networks for which some intrusion detection or prevention schemes are proposed that could also be applied in some way in a WMN setting. As those schemes are contributing, researchers may not find it necessary to specifically focus on this field. Another critical reason may be the difficulty in developing an effective scheme for WMNs. In fact, the structural differences among various wireless ad hoc networking technologies make it imperative to devise the mechanisms in subtle but critically different ways. For WMNs, there is a proper network backbone that is called mesh backbone (which is not present in many other wireless network counterparts), which supports the fringe part or the mesh clients. Hence, it is often possible to install the intrusion handling mechanisms or agents in the stable part and allowing some flexibility in the client or fringe parts. Nonetheless, instead of thinking in this pattern, we take a different approach of tackling intrusion by allowing an intruder to stay in the network as long as it proves to be worthy of staying in the network by supporting the network’s regular activities. The idea is that by not always directly purging rogue entities, one could exploit the intruder’s resources, thereby benefiting the network. We call our approach an intrusion-tackling mechanism and term it a Pay-and-Stay model. Alongside presenting the details and analysis of our model, in this paper, we also present the basics of various forms of intrusion handling in such types of networks. Using our evaluation results, we found that the model could be very effective in handling intruders and defending the network against a broad range of security attacks.

A Survey of Security Challenges, Attacks Taxonomy and Advanced Countermeasures in the Internet of Things

“…There is still some weakness in the existing approaches such as security resilience, connection probability and scalability. [131], MAPE [126], Linear SVM [128], IoT Malware Detection [127], ELSA [130], Hybrid Spyware Detection [129], BLOWFISH, PRESENT, HEIGHT, AES, 3DES, RSA, ECC, DSA, DH [233], [40], K-Anonymity Technique [135], BUDDIES [136], LSR [146], Lightweight RSS-Based Scheme [148], Privacy-Preserving Scheme [147], Game Theory [152], [153], [234], CHA-IDS [140], TCGA [145], INTI [143], Sensor Scheduling Technique [120], Mobile Agent-Based IDS [150], Determeministic Detection [156], Jammer Localization, Detection and Mitigation [157]- [159], [182], [189], [190] and Tamper Detection [160].…”

“…The authors designed and implemented their IDS in the Contiki operating system and evaluated using Cooja simulator [187]. Game theory-based detection model presented in [152], [153] to model and detect SF attacks for wireless mesh networks (WMNs) efficiently. The authors in [154] proposed a solution, which allows breaking the data packets into a number of smaller pieces.…”