The superior reconfigurability of software defined radio mobile devices has made it one of the most promising technology on the wireless network and in the mobile communication industry. The evolution from a static and rigid system to a highly dynamic environment, which offers many advantages over current systems, has been made possible thanks to the concepts of programmability and reconfigurability introduced by the software defined radio technology and the higher level of flexibility and openness of this technology's devices. Clearly, the software defined radio mobile device's flexibility is a great advantage since the customer is able to use the same device in different parts of the world, with different wireless technologies. Despite the advantages, there are still issues to be discussed regarding security. According to the Software Defined Radio Forum some of the concerns are the radio configuration download, storage and installation, user's privacy, and cloning. To address the SDR Forum concerns a raud-prevention framework is proposed. The framework is composed by new pieces of hardware, new modules and new protocols that together greatly enhance the overall security of software defined radio mobile devices and this new highly dynamic environment. The framework offers security monitoring against malicious attacks and viruses that may affect the configuration data; protects sensitive information through the use of protected storage; creates and protects an identity for the system; employs a secure and efficient protocol for radio configuration download and update; and finally, establishes an anti-cloning scheme, which not only guarantees that no units can be cloned over the air but also elevates the level of difficulty to clone units if the attacker has physical access to those units. Even if cloned units exist, the anti-cloning scheme is able to identify them and deny any service.

We present a theory for constructing linear, black box approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. The construction of the linear model to a face recognition algorithm involves embedding of a training set of face images constrained by the distances between them, as computed by the face recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical multi-dimensional scaling (MDS). We empirically demonstrate the adequacy of the linear model using six face recognition algorithms, spanning both template based and feature based approaches on standard face recognition benchmarks such as the Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. The experimental results show that the average Error in Modeling for six algorithms is 6.3% at 0.001 False Acceptance Rate (FAR), for FERET fafb probe set which contains maximum number of subjects among all the probe sets. We demonstrate the usefulness of the linear model for algorithm dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian Intra/Extra-class person classifier (BAY), Elastic Bunch Graph Matching algorithm (EBGM), and the commercial face recognition algorithms. We also propose a novel paradigm to reconstruct face templates from match scores using the linear model and use the reconstructed templates to explore the security breach in a face recognition system. We evaluate the proposed template reconstruction scheme using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA), Bayesian Intra/Extra-class person classifier (BAY), and a feature based commercial algorithm. With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve 73%, 72% and 100% chance of breaking in as a randomly chosen target subject for the commercial, BAY and PCA based face recognition system, respectively. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts.