Reputation aware obfuscation for mobile opportunistic networks

© 2013 IEEE. Current anonymity techniques for mobile opportunistic networks typically use obfuscation algorithms to hide node's identity behind other nodes. These algorithms are not well suited to sparse and disconnection prone networks with large number of malicious nodes and new opportunistic, adaptive. So, new, opportunistic, adaptive fully localized mechanisms are needed for improving user anonymity. This paper proposes reputation aware localized adaptive obfuscation for mobile opportunistic networks that comprises of two complementary techniques: opportunistic collaborative testing of nodes' obfuscation behaviour (OCOT) and multidimensional adaptive anonymisation (AA). OCOT-AA is driven by both explicit and implicit reputation building, complex graph connectivity analytics and obfuscation history analyses. We show that OCOT-AA is very efficient in terms of achieving high levels of node identity obfuscation and managing low delays for answering queries between sources and destinations while enabling fast detection and avoidance of malicious nodes typically within the fraction of time within the experiment duration. We perform extensive experiments to compare OCOT-AA with several other competitive and benchmark protocols and show that it outperforms them across a range of metrics over a one month real-life GPS trace. To demonstrate our proposal more clearly, we propose new metrics that include best effort biggest length and diversity of the obfuscation paths, the actual percentage of truly anonymised sources' IDs at the destinations and communication quality of service between source and destination

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles

The increased interest in autonomous vehicles has led to the development of novel networking protocols in VANETS. In such a widespread safety critical application, security is paramount to the implementation of the networks. We view new autonomous vehicle edge networks as oppor-tunistic networks that bridge the gap between fully distributed vehicular networks based on short range vehicle to vehicle communication and cellular based infrastructure for centralized solutions. Experiments are conducted using opportunistic networking protocols to provide data to autono-mous trams and buses in a smart city. Attacking vehicles enter the city aiming to disrupt the net-work to cause harm to the general public. In the experiments the number of vehicles and the at-tack length is altered to investigate the impact on the network and vehicles. Considering different measures of success as well as computation expense, measurements are taken from all nodes in the network across different lengths of attack. The data gathered from each node allows explora-tion into how different attacks impact metrics including the delivery probability of a message, the time taken to deliver and the computation expense to each node. The novel multidimensional analysis including geospatial elements provides evidence that the state-of-the-art MaxProp algo-rithm outperforms the benchmark as well as other, more complex routing protocols in most of the categories. Upon the introduction of attacking nodes however, PRoPHET provides the most relia-ble delivery probability while under attack. Two different attack methods (black and grey holes) are used to disrupt the flow of messages throughout the network and the more basic protocols show they are less consistent. In some metrics, the PRoPHET algorithm performs better while un-der attack due to the benefit of reduced network traffic

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

Contextual dishonest behaviour detection for cognitive adaptive charging in dynamic smart micro-grids

The emerging Smart Grid (SG) paradigm promises to address decreasing grid stability from thinning safe operating margins, meet continually rising demand from pervasive high capacity devices such as electric vehicles (EVs), and fully embrace the shift towards green energy solutions. At the SG edge, widespread decentralisation of heterogeneous devices coupled with fluctuating energy availability and need as well as a greatly increased fluidity between their roles as energy producers, consumers, and stores raises significant challenges to ensuring robustness and security of both information and energy exchange. Detecting and mitigating both malicious and non-malicious threats in these environments is essential to the realisation of the full potential of the SG. To address this need for robust, localised, real-time security at the grid edge we propose CONCEDE, a collaborative cross-layer ego-network integrity awareness and attack impact reduction extension to our previous work on delay-tolerant cognitive adaptive energy exchange. We detail a substantial, targeted, energy disruption attack perpetrated by colluding mobile energy prosumers. Our CONCEDE proposal is then evaluated in multiple, diverse smart micro-grid (SMG) scenarios using hybrid traces of EVs and infrastructure from Europe, North America, and South America in the presence of a coordinated attack from malicious distributors seeking to disrupt energy supply to a target community. We show that CONCEDE successfully detects and identifies the nodes exhibiting malicious, dishonest behaviour and that CONCEDE also reduces the impact of a coordinated energy disruption attack on innocent parties in all explored scenarios across multiple criteria

Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work