Papers by Muhammad Shahid Usama
Energies, 2019
A free-float bike-sharing system faces various operational challenges to maintain good service qu... more A free-float bike-sharing system faces various operational challenges to maintain good service quality while optimizing the operational cost. The primary problems include the fulfillment of the users demand at all stations, and the replacement of faulty bikes presented in the system. This study focuses on a free-float bike-sharing system rebalancing problem (FFBP) with faulty bikes using battery electric vehicles (BEVs). The target inventory of bikes at each station is obtained while minimizing the total traveling time through the presented formulation. Using CPLEX solver, the model is demonstrated through numerical experiments considering the various vehicle and battery capacities, and a cost–benefit analysis is performed for BEV and conventional internal combustion engine vehicles (ICEVs) while taking the BEV manufacturing and indirect emission into account. The results show that the annual cost incurred on an ICEV is 56.9% more as compared to the cost of using an equivalent BEV. ...
Recently, many deep neural network (DNN) based modulation classification schemes have been propos... more Recently, many deep neural network (DNN) based modulation classification schemes have been proposed in the literature. We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings. We have used Carlini & Wagner (C-W) attack for performing the adversarial attack. To the best of our knowledge, the robustness of these modulation classifiers have not been evaluated through C-W attack before. Our results clearly indicate that state-of-art deep machine learning based modulation classifiers are not robust against adversarial attacks.
Proceedings of the Afternoon Workshop on Self-Driving Networks, 2018
Along with recent networking advances (such as software-defined networks, network functions virtu... more Along with recent networking advances (such as software-defined networks, network functions virtualization, and programmable data planes), the networking field, in a bid to construct highly optimized self-driving and self-organizing networks, is increasingly embracing artificial intelligence and machine learning. It is worth remembering that the modern Internet that interconnects millions of networks is a 'complex adaptive social system', in which interventions not only cause effects but the effects have further knock-on consequences (not all of which are desirable or anticipated). We believe that self-driving networks will likely raise new unanticipated challenges (particularly in the human-facing domains of ethics, privacy, and security). In this paper, we propose the use of insights and tools from the field of "systems thinking"-a rich discipline developing for more than half a century, which encompasses more realistic models of complex social systems-and highlight their relevance for studying the long-term effects of network architectural interventions, particularly for self-driving networks. We show that these tools complement existing simulation and modeling tools and provide new insights and capabilities. To the best of our knowledge, this is the first study that has considered the relevance of formal systems thinking tools for the analysis of self-driving networks.
International Journal of Environmental Research and Public Health, 2020
To enable older drivers to maintain mobility without endangering public safety, it is necessary t... more To enable older drivers to maintain mobility without endangering public safety, it is necessary to develop more effective means of assessing their fitness-to-drive as alternatives to an on-road driving test. In this study, a functional ability test, simulated driving test, and on-road driving test were carried out for 136 older drivers. Influencing factors related to fitness-to-drive were selected based on the correlation between the outcome measure of each test and the pass/fail outcome of the on-road driving test. Four potential alternatives combining different tests were considered and three modeling techniques were compared when constructing the fitness-to-drive assessment model for the elderly. As a result, 92 participants completed all of the tests, of which 61 passed the on-road driving test and the remaining 31 failed. A total of seven influencing factors from all types of tests were selected. The best model was trained by the technique of gradient boosted machine using all ...
NoSQL-based databases are attractive to store and manage big data, mainly due to high scalability... more NoSQL-based databases are attractive to store and manage big data, mainly due to high scalability and data modeling flexibility. However, security in NoSQL-based databases is not as compared to the SQL-based relational databases, which raises concerns for users. Specifically, the security of data-at-rest is of serious concern for the users, who deploy their databases on the cloud, because any unauthorized access to the servers will expose the data easily. There have been some efforts to enable encryption for data at rest for NoSQL-based databases. However, most of the existing solutions do not support secure query processing and are difficult to integrate with the applications. In our work, we address the NoSQL data-at-rest security issue by proposing a system which decomposes a given database into two sub-databases. Then encrypt the data, support secure query processing, and enable seamless integration with NoSQL-based databases. The proposed solution for the data at rest is based ...
2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020
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IEEE Access, 2021
Technology advancement for renewable energy resources and its integration to the distribution net... more Technology advancement for renewable energy resources and its integration to the distribution network (DN) has garnered substantial interest in the last few decades. Integrating such resources has proven to reduce power losses and improve the reliability of DN. However, the growing number of these resources in DN has imposed additional operational and control issues in voltage regulation, system stability, and protection coordination. Incorporation of various types of distributed generators (DG) into DN causes significant changes in the system. These including new fault current sources, new fault levels, a blinding effect in the protection scheme, reduction in the reach of relays, and decrement in the detection of lowlevel fault currents for existing relays. Such changes will jeopardize the effectiveness of the entire protection scheme in the DN. This research aims to propose a robust protection scheme in which the relay coordination settings are optimized based on the network layout. The potential impacts of DGs on the DN are mitigated by utilizing a user-defined overcurrent-based relay characteristic to obtain the minimum operating time while satisfying protection coordination constraints. A hybrid optimization algorithm based on Metaheuristic and Linear Programming that has the capability to attain the optimal solution and reduces computational time is proposed in this work. The performance of the proposed technique is tested on radial DN integrated with microgrid (MG). The results obtained show the proposed technique has successfully reduced the relay operating time while meeting the protection coordination requirements for dynamic operating modes of a network.
2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), 2018
Future communications and data networks are expected to be largely cognitive self-organizing netw... more Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive selforganization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work.
BIOMEDICA, 2020
Background and Objective: COVID-19 can cause severe acute respiratory distress syndrome. With det... more Background and Objective: COVID-19 can cause severe acute respiratory distress syndrome. With deteriorating disease, most of the patients may require intensive care admission. This study was carried out to determine and evaluate the response of Tocilizumab with special reference to C-reactive protein (CRP) in critically ill patients presented to Farooq Hospital, West Wood Lahore. Methods: This retrospective study included the data of 55 critically ill COVID-19 patients (respiratory rate ≥30, SpO2<93%, oxygen requirement ≥5L/min, PaO2/FiO2 ≤300 mmHg) admitted in Corona unit of Farooq Hospital West Wood Lahore, who were being treated with Tocilizumab alongwith standard treatment protocol between April 27 and June 21, 2020. The data has been retrieved from hospital records after taking appropriate permission and consent. Demographic, clinical features and serum CRP were recorded for each of them, before and after administration of Tocilizumab. Data analysis was done by Statistical Package for the Social Sciences (SPSS) version 22.0 and expressed as frequency and percentages. Results: Out of 55 patients who were administered Tocilizumab, 72.7% survived whereas 27.3% died. There was higher median reduction of CRP levels in patients who survived (77.5 to 34.9 mg/L) as compared to those who died (65.5 to 45.3 mg/L). There was a statistically significant difference between CRP levels at the time of admission, 72 hours after Tocilizumab was administered (P<0.0001). Conclusion: Tocilizumab administration might be helpful in reducing the complications of cytokine storm in patients with severe COVID-19 pneumonia.
IEEE Transactions on Artificial Intelligence, 2021
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its abili... more Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its ability to achieve high performance in a range of environments with little manual oversight. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications (e.g., smart grids, traffic controls, and autonomous vehicles) unless its vulnerabilities are addressed and mitigated. To address this problem, we provide a comprehensive survey that discusses emerging attacks on DRL-based systems and the potential countermeasures to defend against these attacks. We first review the fundamental background on DRL and present emerging adversarial attacks on machine learning techniques. We then investigate the vulnerabilities that an adversary can exploit to attack DRL along with state-of-theart countermeasures to prevent such attacks. Finally, we highlight open issues and research challenges for developing solutions to deal with attacks on DRL-based intelligent systems. Impact Statement-Deep Reinforcement Learning (DRL) has numerous real-life applications ranging from autonomous driving to healthcare. It has demonstrated superhuman performance in playing complex games like Go. However, in recent years, many researchers have identified various vulnerabilities of DRL. Keeping this critical aspect in mind, in this paper we present a comprehensive survey of different attacks on DRL and various countermeasures that can be used for robustifying DRL. To the best of our knowledge, this survey is the first attempt at classifying the attacks based on the different components of the DRL pipeline. This paper will provide a roadmap for the researchers and practitioners to develop robust DRL systems.
IEEE Internet Computing, 2021
Spurred by the recent advances in deep learning to harness rich information hidden in large volum... more Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/RL) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally.
IEEE Access, 2021
Technology advancement in the last few decades allows large penetration of renewable energy resou... more Technology advancement in the last few decades allows large penetration of renewable energy resources in the distribution network (DN). The integration of such resources has shown a substantial impact on DN through power loss reduction and improved network reliability. Besides this, the existing protection system has encountered coordination challenges due to the bidirectional power flow, different types and capacity of generation sources, and changes in fault levels due to network operating modes (grid-connected or islanded). Such conditions may cause the relays to malfunction and imperil the effectiveness of the existing protection scheme. Therefore, an efficient and robust protection coordination scheme is imperative to avoid network reliability and stability issues to the grid. This review paper presents a comparative analysis of various protection techniques implemented to alleviate the impact of integrated resources into DN. Moreover, a comparison of classical and modified protection approaches in terms of advantages, shortcomings, and implementation costs is presented. The prime objective of this study is to highlight the prominence of utilizing user-defined programmable relays for modern DNs. Moreover, recommendations are presented by considering the application of user-defined relay characteristics that can be proved as a robust protection scheme to cope with the protection challenges in existing and future power systems developments. INDEX TERMS Distribution networks (DN), microgrids (MG), protection coordination scheme, renewable energy resources (RES), user-defined characteristics (UDC).
Journal of Transport and Land Use, 2020
The Free-Floating Bike-Sharing System (FFBS) enables commuters to pick up and drop off a shared b... more The Free-Floating Bike-Sharing System (FFBS) enables commuters to pick up and drop off a shared bike without going to a docking station before and after the trip. However, the specific sharing features of the FFBS can also be a problem for users because randomly scattered and faulty bikes within the system can result in bike unavailability and maintenance difficulties. This not only negatively impacts the company’s service quality but also causes user safety issues. In this study, a mechanism for the rebalancing of useable bikes and faulty bikes is presented in two steps: 1) gathering each faulty bike at a station determined by the model traversing the shortest path by light service vehicles; and 2) enabling the rebalancing operation to obtain optimal bike inventory levels at all stations and collect faulty bikes at a depot. The destination station from which each faulty bike is taken is considered a decision variable rather than shifting them to a closer station. The mechanism is b...
IEEE Communications Surveys & Tutorials, 2020
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelli... more Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation-which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications-will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings. Index Terms-Connected and autonomous vehicles, machine/deep learning, adversarial machine learning, adversarial perturbation, perturbation detection, and robust machine learning. TABLE I: Comparison of this paper with existing survey and review papers on the security of machine learning (ML) and connected and autonomous vehicles (CAVs).
International Journal of Distributed Sensor Networks, 2019
A substitution box is a core component of the popular symmetric-key algorithms. However, the majo... more A substitution box is a core component of the popular symmetric-key algorithms. However, the major problem of the conventional substitution boxes is the statistic behavior, which is employed as a fixed-size lookup table. To solve the fixed-size lookup table problem, various substitution box construction methods were proposed with key control, but it is hard to enhance all cryptographic properties, for example, linear and differential probabilities. Thus, chaos is applied for key control in designing robust substitution boxes due to unpredictable and random-like behavior. Moreover, the confusion and diffusion properties of cryptography can be achieved by chaos. This article introduces an efficient construction of a key-dependent substitution box based on the mixing property of the chaotic sine map. The substitution box so constructed has very low differential and linear approximation probabilities. The experimental results confirmed that the proposed method to construct substitution ...
IEEE Network, 2019
The holy grail of networking is to create cognitive networks that organize, manage, and drive the... more The holy grail of networking is to create cognitive networks that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the progress in the field of machine learning (ML), which has now already disrupted a number of industries and revolutionized practically all fields of research. But are the ML models foolproof and robust to security attacks to be in charge of managing the network? Unfortunately, many modern ML models are easily misled by simple and easily-crafted adversarial perturbations, which does not bode well for the future of ML-based cognitive networks unless ML vulnerabilities for the cognitive networking environment are identified, addressed, and fixed. The purpose of this article is to highlight the problem of insecure ML and to sensitize the readers to the danger of adversarial ML by showing how an easilycrafted adversarial ML example can compromise the operations of the cognitive self-driving network. In this paper, we demonstrate adversarial attacks on two simple yet representative cognitive networking applications (namely, intrusion detection and network traffic classification). We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.
IEEE Technology and Society Magazine, 2019
Big data revolution promises to be instrumental in facilitating sustainable development in many s... more Big data revolution promises to be instrumental in facilitating sustainable development in many sectors of life such as education, health, agriculture, and in combating humanitarian crises and violent conflicts. However, lurking beneath the immense promises of big data are some significant risks such as (1) the potential use of big data for unethical ends; (2) its ability to mislead through reliance on unrepresentative and biased data; and (3) the various privacy and security challenges associated with data (including the danger of an adversary tampering with the data to harm people). These risks can have severe consequences and a better understanding of these risks is the first step towards mitigation of these risks. In this paper, we highlight the potential dangers associated with using big data, particularly for human development.
Sensors, 2019
The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) netwo... more The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technolog...
IEEE Access, 2019
The publication of this article was funded by the Qatar National Library (QNL). The statements ma... more The publication of this article was funded by the Qatar National Library (QNL). The statements made herein are solely the responsibility of the authors.
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)
A comprehensive study of the MOS Current Mode Logic (MCML) is presented. Operation of a conventio... more A comprehensive study of the MOS Current Mode Logic (MCML) is presented. Operation of a conventional MCML latch is analyzed and some modified structures are described. A novel structure is proposed for increased stability with reduced delay parameters. General problems with single-ended to differential conversion are addressed. Comparative performance measures of Master-Slave (MS) latches are presented in a 0.18-µm CMOS technology.
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Papers by Muhammad Shahid Usama