Towards a secure cooperation mechanism for Challenging Networks

A Challenging Network (CN) is a network paradigm adapting to the many issues of the environment in order to guarantee the communication among nodes. One of the most important issues of a CN is the problem of secure cooperation among nodes. In fact, an attacker, either internal or external, may constitute a threat for the network. In this work I investigate the problem of secure cooperation in three kinds of CNs: the Underwater Acoustic Net- works (UANs), the Delay Tolerant Networks (DTNs) and the Publish/Subscribe Networks (PSNs). A UAN is a network paradigm allowing communication among underwater nodes equipped with acoustic modems. Since the acoustic channel is an open medium, an attacker conveniently equipped could intercept the messages traversing the network. In this work I describe a cryptographic suite, aimed at protecting the communication among underwater acoustic nodes. A DTN is a network paradigm guaranteeing message delivery even in presence of network partitions. A DTN relies on the implicit assumption that nodes cooperate towards message forwarding. However, this assumption cannot be satisfied when there are malicious nodes acting as blackholes and voluntarily attracting and dropping messages. In this work I propose a reputation-based protocol for contrasting blackholes. A PSN is a network paradigm allowing communication from publishers to subscribers by means of an infrastructure, called Dispatcher. In this work I present a secure PSN conceived to support cooperation be- tween organizations. The service is based on the notion of security group, an overlay composed of brokers representing organizations that guarantees confidentiality and integrity in end-to-end delivery of messages and supports clients mobility

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

Signaling and Reciprocity:Robust Decentralized Information Flows in Social, Communication, and Computer Networks

Complex networks exist for a number of purposes. The neural, metabolic and food networks ensure our survival, while the social, economic, transportation and communication networks allow us to prosper. Independently of the purposes and particularities of the physical embodiment of the networks, one of their fundamental functions is the delivery of information from one part of the network to another. Gossip and diseases diffuse in the social networks, electrochemical signals propagate in the neural networks and data packets travel in the Internet. Engineering networks for robust information flows is a challenging task. First, the mechanism through which the network forms and changes its topology needs to be defined. Second, within a given topology, the information must be routed to the appropriate recipients. Third, both the network formation and the routing mechanisms need to be robust against a wide spectrum of failures and adversaries. Fourth, the network formation, routing and failure recovery must operate under the resource constraints, either intrinsic or extrinsic to the network. Finally, the autonomously operating parts of the network must be incentivized to contribute their resources to facilitate the information flows. This thesis tackles the above challenges within the context of several types of networks: 1) peer-to-peer overlays – computers interconnected over the Internet to form an overlay in which participants provide various services to one another, 2) mobile ad-hoc networks – mobile nodes distributed in physical space communicating wirelessly with the goal of delivering data from one part of the network to another, 3) file-sharing networks – networks whose participants interconnect over the Internet to exchange files, 4) social networks – humans disseminating and consuming information through the network of social relationships. The thesis makes several contributions. Firstly, we propose a general algorithm, which given a set of nodes embedded in an arbitrary metric space, interconnects them into a network that efficiently routes information. We apply the algorithm to the peer-to-peer overlays and experimentally demonstrate its high performance, scalability as well as resilience to continuous peer arrivals and departures. We then shift our focus to the problem of the reliability of routing in the peer-to-peer overlays. Each overlay peer has limited resources and when they are exhausted this ultimately leads to delayed or lost overlay messages. All the solutions addressing this problem rely on message redundancy, which significantly increases the resource costs of fault-tolerance. We propose a bandwidth-efficient single-path Forward Feedback Protocol (FFP) for overlay message routing in which successfully delivered messages are followed by a feedback signal to reinforce the routing paths. Internet testbed evaluation shows that FFP uses 2-5 times less network bandwidth than the existing protocols relying on message redundancy, while achieving comparable fault-tolerance levels under a variety of failure scenarios. While the Forward Feedback Protocol is robust to message loss and delays, it is vulnerable to malicious message injection. We address this and other security problems by proposing Castor, a variant of FFP for mobile ad-hoc networks (MANETs). In Castor, we use the same general mechanism as in FFP; each time a message is routed, the routing path is either enforced or weakened by the feedback signal depending on whether the routing succeeded or not. However, unlike FFP, Castor employs cryptographic mechanisms for ensuring the integrity and authenticity of the messages. We compare Castor to four other MANET routing protocols. Despite Castor's simplicity, it achieves up to 40% higher packet delivery rates than the other protocols and recovers at least twice as fast as the other protocols in a wide range of attacks and failure scenarios. Both of our protocols, FFP and Castor, rely on simple signaling to improve the routing robustness in peer-to-peer and mobile ad-hoc networks. Given the success of the signaling mechanism in shaping the information flows in these two types of networks, we examine if signaling plays a similar crucial role in the on-line social networks. We characterize the propagation of URLs in the social network of Twitter. The data analysis uncovers several statistical regularities in the user activity, the social graph, the structure of the URL cascades as well as the communication and signaling dynamics. Based on these results, we propose a propagation model that accurately predicts which users are likely to mention which URLs. We outline a number of applications where the social network information flow modelling would be crucial: content ranking and filtering, viral marketing and spam detection. Finally, we consider the problem of freeriding in peer-to-peer file-sharing applications, when users can download data from others, but never reciprocate by uploading. To address the problem, we propose a variant of the BitTorrent system in which two peers are only allowed to connect if their owners know one another in the real world. When the users know which other users their BitTorrent client connects to, they are more likely to cooperate. The social network becomes the content distribution network and the freeriding problem is solved by leveraging the social norms and reciprocity to stabilize cooperation rather than relying on technological means. Our extensive simulation shows that the social network topology is an efficient and scalable content distribution medium, while at the same time provides robustness to freeriding

CRM: a new dynamic cross-layer reputation computation model in wireless networks

This is the author accepted manuscript. The final version is available from University Press (OUP) via the DOI in this record.Multi-hop wireless networks (MWNs) have been widely accepted as an indispensable component of next-generation communication systems due to their broad applications and easy deployment without relying on any infrastructure. Although showing huge benefits, MWNs face many security problems, especially the internal multi-layer security threats being one of the most challenging issues. Since most security mechanisms require the cooperation of nodes, characterizing and learning actions of neighboring nodes and the evolution of these actions over time is vital to construct an efficient and robust solution for security-sensitive applications such as social networking, mobile banking, and teleconferencing. In this paper, we propose a new dynamic cross-layer reputation computation model named CRM to dynamically characterize and quantify actions of nodes. CRM couples uncertainty based conventional layered reputation computation model with cross-layer design and multi-level security technology to identify malicious nodes and preserve security against internal multi-layer threats. Simulation results and performance analyses demonstrate that CRM can provide rapid and accurate malicious node identification and management, and implement the security preservation against the internal multi-layer and bad mouthing attacks more effectively and efficiently than existing models.The authors would like to thank anonymous reviewers and editors for their constructive comments. This work is supported by: 1. Changjiang Scholars and Innovative Research Team in University (IRT1078), 2. the Key Program of NSFC-Guangdong Union Foundation (U1135002), 3. National Natural Science Foundation of China (61202390), 4. Fujian Natural Science Foundation：2013J01222, 5. the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications, Ministry of Education)

Erkennung und Vermeidung von Fehlverhalten in fahrzeugbasierten DTNs

Delay- and Disruption-Tolerant Networks (DTNs) are a suitable technology for many applications when the network suffers from intermittent connections and significant delays. In current vehicular networks, due to the high mobility of vehicles, the connectivity in vehicular networks can be highly unstable, links may change or break soon after they have been established and the network topology varies significantly depending on time and location. When the density of networked vehicles is low, connectivity is intermittent and with only a few transmission opportunities. This makes forwarding packets very difficult. For the next years, until a high penetration of networked vehicles is realized, delay-tolerant methods are a necessity in vehicular networks, leading to Vehicular DTNs (VDTNs). By implementing a store-carry-forward paradigm, VDTNs can make sure that even under difficult conditions, the network can be used by applications. However, we cannot assume that all vehicles are altruistic in VDTNs. Attackers can penetrate the communication systems of vehicles trying their best to destroy the network. Especially if multiple attackers collude to disrupt the network, the characteristics of VDTNs, without continuous connectivity, make most traditional strategies of detecting attackers infeasible. Additionally, selfish nodes may be reluctant to cooperate considering their profit, and due to hard- or software errors some vehicles cannot send or forward data. Hence, efficient mechanisms to detect malicious nodes in VDTNs are imperative. In this thesis, two classes of Misbehavior Detection Systems (MDSs) are proposed to defend VDTNs against malicious nodes. Both MDSs use encounter records (ERs) as proof to document nodes' behavior during previous contacts. By collecting and securely exchanging ERs, depending on different strategies in different classes of MDSs, a reputation system is built in order to punish bad behavior while encouraging cooperative behavior in the network. With independently operating nodes and asynchronous exchange of observations through ERs, both systems are very well suited for VDTNs, where there will be no continuous, ubiquitous network in the foreseeable future. By evaluating our methods through extensive simulations using different DTN routing protocols and different realistic scenarios, we find that both MDS classes are able to efficiently protect the system with low overhead and prevent malicious nodes from further disrupting the network.In Netzwerken mit zeitweisen Unterbrechungen oder langen Verzögerungen sind Delay- and Disruption-Tolerant Networks (DTNs) eine geeignete Technologie für viele Anwendungen. Die Konnektivität in Fahrzeugnetzen ist bedingt durch die hohe Mobilität und die geringe Verbreitung von netzwerkfähigen Fahrzeugen oft instabil. Bis zur flächendeckenden Verbreitung von netzwerkfähigen Fahrzeugen ist es daher zwingend notwendig auf Methoden des Delay Tolerant Networking zurückzugreifen um die bestmögliche Kommunikation zu gewährleisten. In diesem Zusammenhang wird von Vehicular Delay Tolerant Networks (VDTNs) gesprochen. Durch das Store-Carry-Forward-Prinzip kann ein VDTN Kommunikation für Anwendungen ermöglichen. Allerdings ist davon auszugehen, dass sich nicht alle Fahrzeuge altruistisch verhalten: Angreifer können Fahrzeuge übernehmen und das Netzwerk attackieren oder Knoten sind aus egoistischen Motiven oder auf Grund von Defekten unkooperativ. Verfahren, die Fehlverhalten in stabilen Netzen durch direkte Beobachtung erkennen können, sind in VDTNs nicht anwendbar. Daher sind Methoden, die Fehlverhalten in VDTNs nachweisen können, zwingend erforderlich. In dieser Arbeit werden zwei Klassen von Misbehavior Detection Systems (MDSs) vorgestellt. Beide Systeme basieren auf Encounter Records (ERs): Nach einem Kontakt tauschen zwei Knoten kryptografisch signierte Meta-Informationen zu den erfolgten Datentransfers aus. Diese ERs dienen bei darauffolgenden Kontakten mit anderen Netzwerkteilnehmern als vertrauenswürdiger Nachweis für das Verhalten eines Knotens in der Vergangenheit. Basierend auf der Auswertung gesammelter ERs wird ein Reputationssystem entwickelt, das kooperatives Verhalten belohnt und unkooperatives Verhalten bestraft. Dauerhaft unkooperative Knoten werden aus dem Netzwerk ausgeschlossen. Durch den asynchronen Austausch von Informationen kann jeder Knoten das Verhalten seiner Nachbarn selbstständig und unabhängig evaluieren. Dadurch sind die vorgestellten MDS-Varianten sehr gut für den Einsatz in einem VDTN geeignet. Durch umfangreiche Evaluationen wird gezeigt, dass sich die entwickelten MDS-Verfahren für verschiedene Routingprotokolle und in unterschiedlichen Szenarien anwenden lassen. In allen Fällen ist das MDS in der Lage das System mit geringem Overhead gegen Angreifer zu verteidigen und eine hohe Servicequalität im Netzwerk zu gewährleisten

Defense and traceback mechanisms in opportunistic wireless networks

 In this thesis, we have identified a novel attack in OppNets, a special type of packet dropping attack where the malicious node(s) drops one or more packets (not all the packets) and then injects new fake packets instead. We name this novel attack as the Catabolism attack and propose a novel attack detection and traceback approach against this attack referred to as the Anabolism defence. As part of the Anabolism defence approach we have proposed three techniques: time-based, Merkle tree based and Hash chain based techniques for attack detection and malicious node(s) traceback. We provide mathematical models that show our novel detection and traceback mechanisms to be very effective and detailed simulation results show our defence mechanisms to achieve a very high accuracy and detection rate

Scalable and Secure Multicast Routing for Mobile Ad-hoc Networks

Mobile Ad-Hoc Networks (MANETs) are decentralized and autonomous communication systems: They can be used to provide connectivity when a natural disaster has brought down the infrastructure, or they can support freedom of speech in countries with governmental Internet restrictions. MANET design requires careful attention to scalability and security due to low-capacity and error-prone wireless links as well as the openness of these systems. In this thesis, we address the issue of multicast as a means to efficiently support the MANET application of group communication on the network layer. To this aim, we first survey the research literature on the current state of the art in MANET routing, and we identify a gap between scalability and security in multicast routing protocols–two aspects that were only considered in isolation until now. We then develop an explicit multicast protocol based on the design of a secure unicast protocol, aiming to maintain its security properties while introducing minimal overhead. Our simulation results reveal that our protocol reduces bandwidth utilization in group communication scenarios by up to 45 % compared to the original unicast protocol, while providing significantly better resilience under blackhole attacks. A comparison with pure flooding allows us to identify a practical group size limit, and we present ideas for better large-group support

Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises can not afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives