The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Bangning Zhang - One of the best experts on this subject based on the ideXlab platform.
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physical layer security of multiUser satellite communication systems with channel estimation error and multiple eavesdroppers
IEEE Access, 2019Co-Authors: Kefeng Guo, Yuzhen Huang, Bangning ZhangAbstract:This paper investigates the physical layer security (PLS) for the transmission of confidential information over satellite communication systems under unified framework. The generalized shadowed-Rician (SR) fading is utilized to model the satellite channel, and multiple Legitimate Users are served to cooperatively receive the broadcast signal with a cluster of unauthorized eavesdroppers. In particular, the channel estimation error (CEE) is considered for both the Legitimate User and eavesdropping links. Based on the practical channel modeling, we derive the closed-form expressions for the probability of strictly positive secrecy capacity (PSPSC), secrecy outage probability (SOP), and average secrecy capacity (ASC) of the considered satellite communication system in the presence of imperfect channel estimation (ICE), which can provide efficient methods to evaluate the impacts of various propagation parameters on the secrecy performance. In order to obtain further insights into the key parameters on the secrecy performance at high signal-to-noise ratios (SNRs), the asymptotic expressions of the SOP and ASC are also derived. Monte Carlo simulation results are provided to verify the correctness of our performance analysis.
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Physical Layer Security with Threshold-Based MultiUser Scheduling in Multi-Antenna Wireless Networks
IEEE Transactions on Communications, 2016Co-Authors: Maoqiang Yang, Daoxing Guo, Yuzhen Huang, Trung Q Duong, Bangning ZhangAbstract:In this paper, we consider a multiUser downlink wiretap network consisting of one base station (BS) equipped with AA antennas, NB single-antenna Legitimate Users, and NE single-antenna eavesdroppers over Nakagami-m fading channels. In particular, we introduce a joint secure transmission scheme that adopts transmit antenna selection (TAS) at the BS and explores threshold-based selection diversity (tSD) scheduling over Legitimate Users to achieve a good secrecy performance while maintaining low implementation complexity. More specifically, in an effort to quantify the secrecy performance of the considered system, two practical scenarios are investigated, i.e., Scenario I: the eavesdropper’s channel state information (CSI) is unavailable at the BS, and Scenario II: the eavesdropper’s CSI is available at the BS. For Scenario I, novel exact closed-form expressions of the secrecy outage probability are derived, which are valid for general networks with an arbitrary number of Legitimate Users, antenna configurations, number of eavesdroppers, and the switched threshold. For Scenario II, we take into account the ergodic secrecy rate as the principle performance metric, and derive novel closed-form expressions of the exact ergodic secrecy rate. Additionally, we also provide simple and asymptotic expressions for secrecy outage probability and ergodic secrecy rate under two distinct cases, i.e., Case I: the Legitimate User is located close to the BS, and Case II: both the Legitimate User and eavesdropper are located close to the BS. Our important findings reveal that the secrecy diversity order is AAmA and the slope of secrecy rate is one under Case I, while the secrecy diversity order and the slope of secrecy rate collapse to zero under Case II, where the secrecy performance floor occurs. Finally, when the switched threshold is carefully selected, the considered scheduling scheme outperforms other well known existing schemes in terms of t- e secrecy performance and complexity tradeoff.
Muhammad Ahmad - One of the best experts on this subject based on the ideXlab platform.
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extended sammon projection and wavelet kernel extreme learning machine for gait based Legitimate User identification
ACM Symposium on Applied Computing, 2019Co-Authors: Muhammad Ahmad, Salvatore Distefano, Manuel Mazzara, Adil Khan, Amjad Ali, Ali TufailAbstract:Smartphones have pervasively integrated into our home and work environments managing confidential information but their owners still rely on as explicit as inefficient and insecure identification processes. Therefore, if a device is stolen, a thief can have access to the owner's personal information and services though the stored password/s. To avoid such situations, this work demonstrates the possibilities of Legitimate User identification in a semi-controlled environment through the built-in smartphone motion dynamics captured by two different sensors. This is a two step process: sub-activity recognition followed by User/impostor identification. Prior to the identification, Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four Users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrated that time and frequency domain features, optimized by ESP to train the wavelet kernel based extreme learning machine classifier, implement an effective system to identify the Legitimate User or an impostor with 97% accuracy.
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Smartwatch-Based Legitimate User Identification for Cloud-Based Secure Services
Mobile Information Systems, 2018Co-Authors: Muhammad Ahmad, Mohammed A. Alqarni, Adil Mehmood Khan, Manuel Mazzara, Sajjad Hussain Chauhdary, Asad Khan, Tariq Umer, Salvatore DistefanoAbstract:Smartphones are ubiquitously integrated into our home and work environment and Users frequently use them as the portal to cloud-based secure services. Since smartphones can easily be stolen or coopted, the advent of smartwatches provides an intriguing platform Legitimate User identification for applications like online banking and many other cloud-based services. However, to access security-critical online services, it is highly desirable to accurately identifying the Legitimate User accessing such services and data whether coming from the cloud or any other source. Such identification must be done in an automatic and non-bypassable way. For such applications, this work proposes a two-fold feasibility study; (1) activity recognition and (2) gait-based Legitimate User identification based on individual activity. To achieve the above-said goals, the first aim of this work was to propose a semicontrolled environment system which overcomes the limitations of Users’ age, gender, and smartwatch wearing style. The second aim of this work was to investigate the ambulatory activity performed by any User. Thus, this paper proposes a novel system for implicit and continuous Legitimate User identification based on their behavioral characteristics by leveraging the sensors already ubiquitously built into smartwatches. The design system gives Legitimate User identification using machine learning techniques and multiple sensory data with 98.68% accuracy.
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Extended Sammon Projection and Wavelet Kernel Extreme Learning Machine for Gait-Based Legitimate User Identification on Smartphones
arXiv: Cryptography and Security, 2017Co-Authors: Muhammad Ahmad, Adil Mehmood KhanAbstract:Smartphones have ubiquitously integrated into our home and work environments, however, Users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored password/s. As a result of this potential scenario, this work demonstrates the possibilities of Legitimate User identification in a semi-controlled environment through the built-in smartphones motion dynamics captured by two different sensors. This is a two-fold process: sub-activity recognition followed by User/impostor identification. Prior to the identification; Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four Users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrate that together time and frequency domain features optimized by ESP to train the wavelet kernel based extreme learning machine classifier is an effective system to identify the Legitimate User or an impostor with \(97\%\) accuracy.
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Multi Sensor-based Implicit User Identification.
arXiv: Cryptography and Security, 2017Co-Authors: Muhammad Ahmad, Adil Mehmood Khan, Salvatore Distefano, Manuel Mazzara, Ali Kashif Bashir, Shahzad SarfrazAbstract:Smartphones have ubiquitously integrated into our home and work environments, however, Users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner's personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic Legitimate User identification system based on gait biometrics extracted from User walking patterns captured by a smartphone. A set of preprocessing schemes is applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized using a non-linear unsupervised feature selection method. The selected features create an underlying gait biometric representation able to discriminate among individuals and identify them uniquely. Different classifiers (i.e. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Bagging, and Extreme Learning Machine (ELM)) are adopted to achieve accurate Legitimate User identification. Extensive experiments on a group of $16$ individuals in an indoor environment show the effectiveness of the proposed solution: with $5$ to $70$ samples per window, KNN and bagging classifiers achieve $87-99\%$ accuracy, $82-98\%$ for ELM, and $81-94\%$ for SVM. The proposed pipeline achieves a $100\%$ true positive and $0\%$ false-negative rate for almost all classifiers.
Fei Pan - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Base Station Cooperation for Physical Layer Security in Two-Cell Wireless Networks
IEEE Access, 2016Co-Authors: Hong Wen, Jie Tang, Fei PanAbstract:We study physical layer security in two-cell wireless networks in which a base station (Alice) intends to send a confidential message to a Legitimate User (Bob) with the help of a cooperative base station (Charlie), in the presence of an eavesdropper (Eve). Adaptive base station cooperation is explored to secure communication between Alice and Bob, and ensure the desired quality of service (QoS) at Charlie’s User. In particular, we consider two different scenarios where the channel state information of Eve is perfectly and statistically known, respectively. In either scenario, we provide a cooperative transmission scheme for secrecy rate maximization, subject to both security and QoS constraints. Unlike the conventional cooperative security with a fixed transmission scheme, we propose a mechanism for transmit strategy adaptation with security protection. Specifically, the cooperative transmission is replaced by a cooperative jamming scheme if either security or QoS constraint is not satisfied. Our design enables adaptive secure transmission, and thus is flexible and environment-adaptive. Moreover, numerical results confirm that our scheme is efficient in power resource utilization.
Sidharth Jaggi - One of the best experts on this subject based on the ideXlab platform.
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communication efficient secret sharing in the presence of malicious adversary
International Symposium on Information Theory, 2020Co-Authors: Rawad Bitar, Sidharth JaggiAbstract:Consider the communication efficient secret sharing problem. A dealer wants to share a secret with n parties such that any k ≤ n parties can reconstruct the secret and any z < k parties eavesdropping on their shares obtain no information about the secret. In addition, a Legitimate User contacting any d, k ≤d ≤n, parties to decode the secret can do so by reading and downloading the minimum amount of information needed. We are interested in communication efficient secret sharing schemes that tolerate the presence of malicious parties actively corrupting their shares and the data delivered to the Users. The knowledge of the malicious parties about the secret is restricted to the shares they obtain. We characterize the capacity, i.e., maximum size of the secret that can be shared. We derive the minimum amount of information needed to to be read and communicated to a Legitimate User to decode the secret from d parties, k ≤d≤ n. We construct codes that achieve capacity. In addition, the constructed codes achieve minimum read and communication costs for all possible values of d. Our codes are based on Staircase codes, previously introduced for communication efficient secret sharing, and on the use of a pairwise hashing scheme used in distributed data storage and network coding settings to detect the presence of a limited knowledge adversary.
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communication efficient secret sharing in the presence of malicious adversary
arXiv: Information Theory, 2020Co-Authors: Rawad Bitar, Sidharth JaggiAbstract:Consider the communication efficient secret sharing problem. A dealer wants to share a secret with $n$ parties such that any $k\leq n$ parties can reconstruct the secret and any $z
Ali Tufail - One of the best experts on this subject based on the ideXlab platform.
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extended sammon projection and wavelet kernel extreme learning machine for gait based Legitimate User identification
ACM Symposium on Applied Computing, 2019Co-Authors: Muhammad Ahmad, Salvatore Distefano, Manuel Mazzara, Adil Khan, Amjad Ali, Ali TufailAbstract:Smartphones have pervasively integrated into our home and work environments managing confidential information but their owners still rely on as explicit as inefficient and insecure identification processes. Therefore, if a device is stolen, a thief can have access to the owner's personal information and services though the stored password/s. To avoid such situations, this work demonstrates the possibilities of Legitimate User identification in a semi-controlled environment through the built-in smartphone motion dynamics captured by two different sensors. This is a two step process: sub-activity recognition followed by User/impostor identification. Prior to the identification, Extended Sammon Projection (ESP) method is used to reduce the redundancy among the features. To validate the proposed system, we first collected data from four Users walking with their device freely placed in one of their pants pockets. Through extensive experimentation, we demonstrated that time and frequency domain features, optimized by ESP to train the wavelet kernel based extreme learning machine classifier, implement an effective system to identify the Legitimate User or an impostor with 97% accuracy.