Abstract-The mechanisms adopted by cellular technologies for user identification allow an adversary to collect information about individuals and track their movements within the network; and thus exposing privacy of the users to unknown risks. Efforts have been made toward enhancing privacy preserving capabilities in cellular technologies, culminating in Long Term Evolution LTE technology. LTE security architecture is substantially enhanced comparing with its predecessors 2G and 3G; however, LTE does not eliminate the possibility of user privacy attacks. LTE is still vulnerable to user identity privacy attacks. This paper includes an evaluation of LTE security architecture and proposes a security solution for the enhancement of user identity privacy in LTE. The solution is based on introducing of pseudonyms that replace the user permanent identifier (IMSI) used for identification. The scheme provides secure and effective identity management in respect to the protection of user privacy in LTE. The scheme is formally verified using proVerif and proved to provide an adequate assurance of user identity privacy protection.

This research proposes a model for presenting email to Artificial Neural Network (ANN) to classify spam and legitimate emails. The proposed model based on selecting wise 13 fixed features relevant to spam emails combined with text features. The experiment tests many scenarios to find out the best-suited combination of features representation. These scenarios show the effect of using term frequency (tf), term frequency-inverse document frequency (tf*idf), Level two (L2) normalization, and principal component analysis (PCA) for dimension reduction. Text features vectors are represented in the principal component space as a reduced form of the original features vectors. PCA reduction effect on ANN performance is also studied. Among these tests, best-suited model that improves ANN classification and speeds up training is concluded and suggested. An idea of integrating ANN anti-spam filter into score-based anti-spam systems is also explained in this paper. XEAMS email gateway, the commercial anti-spam, already uses Naïve Bayes (NB) filter as one of its many techniques to identify spam email. The proposed approach influences filtering results by 7.5% closer to XEAMS anti-spam system results than NB filter does on real-life emails of Arabic and English messages.

An approach enabling end-users to verify that a downloaded untrusted code will not leak confidential data to unauthorized parties is presented. The approach certifies RISC-style assembly programs for secure information flow by statically analyzing the code based on the idea of Proof Carrying Code (PCC). The proofs that untrusted code does not leak sensitive information are generated and checked on the host machine and if they are valid, then the untrusted code can be installed and executed safely. The proposed security analyzer operates directly on the machinecode requiring only the inputs and outputs of the code be annotated with security levels. The generated proofs serve as evidence that give end-users a guarantee about the security of the untrusted code.