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Mohsen Guizani

Bio: Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.


Papers
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Journal ArticleDOI
TL;DR: This article reports the low-cost implementation of GPS spoofing attack and WiFi attack on UAVs, and suggests solutions to them.
Abstract: Communication security is critically important for the success of Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in military and civilian applications, they often carry sensitive information that adversaries might try to get hold of. While UAVs consist of various modules to enable them to function properly, potential security vulnerabilities may also exist in those modules. For example, by launching a GPS spoofing attack or WiFi attack, adversaries can capture the targeted UAV and access the sought after information. In fact, it has become easy to launch such attacks. In this article, we report our low-cost implementation of these attacks and suggest solutions to them.

116 citations

Journal ArticleDOI
TL;DR: A convex–concave-procedure-based contract optimization algorithm for server recruitment and a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration, are proposed.
Abstract: Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex–concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information.

114 citations

Journal ArticleDOI
TL;DR: An attack-resistant trust model based on multidimensional trust metrics (ARTMM) is proposed in this paper that is quite suitable for mobile underwater environment and the performance of the ARTMM is clearly better than that of conventional trust models in terms of both evaluation accuracy and energy consumption.
Abstract: Underwater acoustic sensor networks (UASNs) have been widely used in many applications where a variable number of sensor nodes collaborate with each other to perform monitoring tasks. A trust model plays an important role in realizing collaborations of sensor nodes. Although many trust models have been proposed for terrestrial wireless sensor networks (TWSNs) in recent years, it is not feasible to directly use these trust models in UASNs due to unreliable underwater communication channel and mobile network environment. To achieve accurate and energy efficient trust evaluation in UASNs, an attack-resistant trust model based on multidimensional trust metrics (ARTMM) is proposed in this paper. The ARTMM mainly consists of three types of trust metrics, which are link trust, data trust, and node trust. During the process of trust calculation, unreliability of communication channel and mobility of underwater environment are carefully analyzed. Simulation results demonstrate that the proposed trust model is quite suitable for mobile underwater environment. In addition, the performance of the ARTMM is clearly better than that of conventional trust models in terms of both evaluation accuracy and energy consumption.

113 citations

Journal ArticleDOI
TL;DR: A lightweight mutual authentication scheme based on Physical Unclonable Functions for UAV-GS authentication is presented and is resilient against many security attacks such as masquerade, replay, node tampering, and cloning attacks, etc.
Abstract: Unmanned Aerial Vehicles (UAVs) are becoming very popular nowadays due to the emergence of application areas such as the Internet of Drones (IoD). They are finding wide applicability in areas ranging from package delivery systems to automated military applications. Nevertheless, communication security between a UAV and its ground station (GS) is critical for completing its task without leaking sensitive information either to the adversaries or to unauthenticated users. UAVs are especially vulnerable to physical capture and node tampering attacks. Further, since UAV devices are generally equipped with small batteries and limited memory storage, lightweight security techniques are best suited for them. Addressing these issues, a lightweight mutual authentication scheme based on Physical Unclonable Functions (PUFs) for UAV-GS authentication is presented in this paper. The UAV-GS authentication scheme is extended further to support UAV-UAV authentication. We present a formal security analysis as well as old-fashioned cryptanalysis and show that our protocol provides various security features such as mutual authentication, user anonymity, etc, and is resilient against many security attacks such as masquerade, replay, node tampering, and cloning attacks, etc. We also compare the performance of our protocol with state-of-the-art authentication protocols for UAVs, based on computation, communication, and memory storage cost.

113 citations

Journal ArticleDOI
TL;DR: This paper investigates ICN as communication enabler for IoT domain specific use cases, and the use of ICN features for the benefit of IoT networks, including IoT device & content naming, discovery, and caching.

113 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2002

9,314 citations