Biblio

Filters: Author is Imran, Muhammad Ali  [Clear All Filters]
2023-05-30
Shawky, Mahmoud A., Abbasi, Qammer H., Imran, Muhammad Ali, Ansari, Shuja, Taha, Ahmad.  2022.  Cross-Layer Authentication based on Physical-Layer Signatures for Secure Vehicular Communication. 2022 IEEE Intelligent Vehicles Symposium (IV). :1315—1320.
In recent years, research has focused on exploiting the inherent physical (PHY) characteristics of wireless channels to discriminate between different spatially separated network terminals, mitigating the significant costs of signature-based techniques. In this paper, the legitimacy of the corresponding terminal is firstly verified at the protocol stack’s upper layers, and then the re-authentication process is performed at the PHY-layer. In the latter, a unique PHY-layer signature is created for each transmission based on the spatially and temporally correlated channel attributes within the coherence time interval. As part of the verification process, the PHY-layer signature can be used as a message authentication code to prove the packet’s authenticity. Extensive simulation has shown the capability of the proposed scheme to support high detection probability at small signal-to-noise ratios. In addition, security evaluation is conducted against passive and active attacks. Computation and communication comparisons are performed to demonstrate that the proposed scheme provides superior performance compared to conventional cryptographic approaches.
2020-07-03
Usama, Muhammad, Asim, Muhammad, Qadir, Junaid, Al-Fuqaha, Ala, Imran, Muhammad Ali.  2019.  Adversarial Machine Learning Attack on Modulation Classification. 2019 UK/ China Emerging Technologies (UCET). :1—4.

Modulation classification is an important component of cognitive self-driving networks. Recently many ML-based modulation classification methods have been proposed. We have evaluated the robustness of 9 ML-based modulation classifiers against the powerful Carlini & Wagner (C-W) attack and showed that the current ML-based modulation classifiers do not provide any deterrence against adversarial ML examples. To the best of our knowledge, we are the first to report the results of the application of the C-W attack for creating adversarial examples against various ML models for modulation classification.

2017-03-07
Onireti, Oluwakayode, Qadir, Junaid, Imran, Muhammad Ali, Sathiaseelan, Arjuna.  2016.  Will 5G See Its Blind Side? Evolving 5G for Universal Internet Access Proceedings of the 2016 Workshop on Global Access to the Internet for All. :1–6.

Internet has shown itself to be a catalyst for economic growth and social equity but its potency is thwarted by the fact that the Internet is off limits for the vast majority of human beings. Mobile phones—the fastest growing technology in the world that now reaches around 80% of humanity—can enable universal Internet access if it can resolve coverage problems that have historically plagued previous cellular architectures (2G, 3G, and 4G). These conventional architectures have not been able to sustain universal service provisioning since these architectures depend on having enough users per cell for their economic viability and thus are not well suited to rural areas (which are by definition sparsely populated). The new generation of mobile cellular technology (5G), currently in a formative phase and expected to be finalized around 2020, is aimed at orders of magnitude performance enhancement. 5G offers a clean slate to network designers and can be molded into an architecture also amenable to universal Internet provisioning. Keeping in mind the great social benefits of democratizing Internet and connectivity, we believe that the time is ripe for emphasizing universal Internet provisioning as an important goal on the 5G research agenda. In this paper, we investigate the opportunities and challenges in utilizing 5G for global access to the Internet for all (GAIA). We have also identified the major technical issues involved in a 5G-based GAIA solution and have set up a future research agenda by defining open research problems.