Dynamic spectrum access enabled by cognitive radio networks are envisioned to drive the next generation wireless networks that can increase spectrum utility by opportunistically accessing unused spectrum. Due to the policy constraint that there could be no interference to the primary (licensed) users, secondary cognitive radios have to continuously sense for primary transmissions. Typically, sensing reports from multiple cognitive radios are fused as stand-alone observations are prone to errors due to wireless channel characteristics. Such dependence on cooperative spectrum sensing is vulnerable to attacks such as Secondary Spectrum Data Falsification (SSDF) attacks when multiple malicious or selfish radios falsify the spectrum reports. Hence, there is a need to quantify the trustworthiness of radios that share spectrum sensing reports and devise malicious node identification and robust fusion schemes that would lead to correct inference about spectrum usage. In this work, we propose an anomaly monitoring technique that can effectively capture anomalies in the spectrum sensing reports shared by individual cognitive radios during cooperative spectrum sensing in a multi-channel distributed network. Such anomalies are used as evidence to compute the trustworthiness of a radio by its neighbours. The proposed anomaly monitoring technique works for any density of malicious nodes and for any physical environment. We propose an optimistic trust heuristic for a system with a normal risk attitude and show that it can be approximated as a beta distribution. For a more conservative system, we propose a multinomial Dirichlet distribution based conservative trust framework, where Josang*s Belief model is used to resolve any uncertainty in information that might arise during anomaly monitoring. Using a machine learning approach, we identify malicious nodes with a high degree of certainty regardless of their aggressiveness and variations introduced by the pathloss environment. We also propose extensions to the anomaly monitoring technique that facilitate learning about strategies employed by malicious nodes and also utilize the misleading information they provide. We also devise strategies to defend against a collaborative SSDF attack that is launched by a coalition of selfish nodes. Since, defense against such collaborative attacks is difficult with popularly used voting based inference models or node centric isolation techniques, we propose a channel centric Bayesian inference approach that indicates how much the collective decision on a channels occupancy inference can be trusted. Based on the measured observations over time, we estimate the parameters of the hypothesis of anomalous and non-anomalous events using a multinomial Bayesian based inference. We quantitatively define the trustworthiness of a channel inference as the difference between the posterior beliefs associated with anomalous and non-anomalous events. The posterior beliefs are updated based on a weighted average of the prior information on the belief itself and the recently observed data. Subsequently, we propose robust fusion models which utilize the trusts of the nodes to improve the accuracy of the cooperative spectrum sensing decisions. In particular, we propose three fusion models: (i) optimistic trust based fusion, (ii) conservative trust based fusion, and (iii) inversion based fusion. The former two approaches exclude untrustworthy sensing reports for fusion, while the last approach utilizes misleading information. All schemes are analyzed under various attack strategies. We propose an asymmetric weighted moving average based trust management scheme that quickly identifies on-off SSDF attacks and prevents quick trust redemption when such nodes revert back to temporal honest behavior. We also provide insights on what attack strategies are more effective from the adversaries* perspective. Through extensive simulation experiments we show that the trust models are effective in identifying malicious nodes with a high degree of certainty under variety of network and radio conditions. We show high true negative detection rates even when multiple malicious nodes launch collaborative attacks which is an improvement over existing voting based exclusion and entropy divergence techniques. We also show that we are able to improve the accuracy of fusion decisions compared to other popular fusion techniques. Trust based fusion schemes show worst case decision error rates of 5% while inversion based fusion show 4% as opposed majority voting schemes that have 18% error rate. We also show that the proposed channel centric Bayesian inference based trust model is able to distinguish between attacked and non-attacked channels for both static and dynamic collaborative attacks. We are also able to show that attacked channels have significantly lower trust values than channels that are not– a metric that can be used by nodes to rank the quality of inference on channels.

Français:La notion de réseaux hétérogènes correspond à l’intégration de plusieurs technologies de transmission de données sans-fil dans le but d’accroitre la qualité de service des communications dans les réseaux mobiles.Historiquement, les mécanismes de sécurité des réseaux mobiles et sans-fil ont été largement focalisés sur la protection d’équipement utilisateur au niveau du dernier saut de communication et sur base d’une connectivité simple et unique. Cette connectivité, réduite à sa plus simple expression, a restraint le développement des protocoles de sécurité à des protocoles bi-parties, qui couvrent l’authentification des équipements utilisateurs et le chiffrement sur des communicationsLes mécanismes de sécurité et de cryptographie ne sont donc pas suffisants pour protéger correctement et efficacement des connections parallèles ou leur mobilité au sein de réseaux hétérogènes. Le but de cette thèse de doctorat, à travers quatre contributions personnelles, est d’apporter de nouveaux mécanismes et protocoles de sécurité afin de protéger au mieux les réseaux hétérogènes:• La première contribution se focalise sur le développement d’une nouvelle primitive cryptographique pour la protection des transmissions sans-fil. La propriété principale de celle-ci est de protéger les trames en cas de capture. Cette primitive introduit, notamment, la notion de force brute probabiliste (ce qui veut dire qu’un attaquant ne peut pas choisir parmi différentes clés équiprobables laquelle est effectivement utilisée).• La seconde contribution propose un nouveau protocole pour gérer d’une manière sure et efficace la mobilité des équipements utilisateurs entre différentes technologies au sein de réseaux hétérogènes.• La troisième contribution couvre la gestion des clés maîtres des utilisateurs, embarqués au sein des cartes SIM, utilisées au sein des réseaux d’opérateurs mobiles. Nos protocoles et mécanismes automa- tisent des changements réguliers et sûrs de la clé maître, et ajoutent de la diversité dans la gestion des clés de sessions pour minimiser l’impact en cas de révélation de ces dernières (par le biais d’un vol de base de donnée, par exemple)• La quatrième contribution introduit un nouveau paradigme de connectivité pour les réseaux mo- biles basé sur des communications 1−à−n. Le paradigme redéfinit les frontières de sécurité et place l’équipement utilisateur au centre d’un groupe authentifié mobile. Par conséquent, le changement de paradigme mène à la création de nouveaux protocoles pour l’authentification, la gestion de la mo- bilité et la négociation protégées de clés afin de fournir une protection de bout en bout entre deux équipements utilisateurs ou plus. / English:Heterogeneous Networks (HetNets) is the integration of multiple wireless technologies to increase the quality of service of the communications in mobile networks. This evolution is the next generation of Public Land Mobile Networks (PLMNs).Mobile and wireless network security mechanisms have largely focused on the protection of the User Equipment (UE) within the last mile (the last hop of the communication in the chain of connected devices) and on single connections. The single connectivity has reduced the development of the security to two party protocols, and they cover the authentication of the UE to the mobile network and the encryption on a single channel based on homogeneous communications through a unique technology.The current security and cryptographic mechanisms are not sufficient to protect correctly, and efficiently, parallel connections or their mobility in HetNets. The purpose of the PhD Thesis is to bring new security protocols and mechanisms to protect HetNets.The contributions, that are brought by the thesis, follow the evolution of HetNets through 4 contributions by starting from the wireless transmissions to the largest frame of HetNets architecture:• The first contribution focuses on the development of an new cryptographic primitives for wireless transmissions. The main property is to protect the frame from eavesdropping. The primitive introduces the notion of probabilistic brute force (meaning that an attacker cannot decide among different keys which the used one).• The second contribution proposes a new protocol to manage efficiently and securely the mobility of the UEs between different technologies inside HetNets.• The third contribution covers the management of the master secrets, embedded within the Universal Subscriber Identity Module (USIM), in large PLMNs. Our mechanisms and protocols automate regular and secure changes of the master secret, and they add diversity in the management of session keys to minimize the impact of key leakages (in case of credential database theft, for instance).• The fourth contribution introduces a new connectivity paradigm for mobile networks based on one-to- many communications. The paradigm redesigns the security borders and puts the UE in the center of a mobile authenticated group. Therefore, the paradigm shift leads to new security protocols for authentication, mobility management, and secure negotiation to provide end-to-end encryption between two or more UEs. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished