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Abdelouahid Derhab
Center of Excellence in Information Assurance, King Saud University, Riyadh, Saudi Arabia

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Original research
Published: 28 June 2021 in Journal of Ambient Intelligence and Humanized Computing
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Remote deep learning paradigm raises important privacy concerns related to clients sensitive data and deep learning models. However, dealing with such concerns may come at the expense of more client-side overhead, which does not fit applications relying on constrained environments. In this paper, we propose a privacy-preserving solution for deep-learning-based inference, which ensures effectiveness and privacy, while meeting efficiency requirements of constrained client-side environments. The solution adopts the non-colluding two-server architecture, which prevents accuracy loss as it avoids using approximation of activation functions, and copes with constrained client-side due to low overhead cost. The solution also ensures privacy by leveraging two reversible perturbation techniques in combination with paillier homomorphic encryption scheme. Client-side overhead evaluation compared to the conventional homomorphic encryption approach, achieves up to more than two thousands times improvement in terms of execution time, and up to more than thirty times improvement in terms of the transmitted data size.

ACS Style

Amine Boulemtafes; Abdelouahid Derhab; Nassim Ait Ali Braham; Yacine Challal. Privacy-preserving remote deep-learning-based inference under constrained client-side environment. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -14.

AMA Style

Amine Boulemtafes, Abdelouahid Derhab, Nassim Ait Ali Braham, Yacine Challal. Privacy-preserving remote deep-learning-based inference under constrained client-side environment. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-14.

Chicago/Turabian Style

Amine Boulemtafes; Abdelouahid Derhab; Nassim Ait Ali Braham; Yacine Challal. 2021. "Privacy-preserving remote deep-learning-based inference under constrained client-side environment." Journal of Ambient Intelligence and Humanized Computing , no. : 1-14.

Journal article
Published: 23 June 2021 in IEEE Transactions on Intelligent Transportation Systems
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In this paper, we propose H-IDFS, a Histogram-based Intrusion Detection and Filtering framework, which assembles the CAN packets into windows, and computes their corresponding histograms. The latter are fed to a multi-class IDS classifier to identify the class of the traffic windows. If the window is found malicious, the filtering system is invoked to filter out the normal CAN packets from each malicious window. To this end, we propose a novel one-class SVM, named OCSVM-attack that is trained on normal traffic and considers the invariant and quasi-invariant features of the attack. Experimental results on two CAN datasets: OTIDS and Car-Hacking, show the superiority of the proposed H-IDFS, as it achieves an accuracy of 100% for window classification, and correctly filters out between 94.93% and 100% of normal packets from malicious windows.

ACS Style

Abdelouahid Derhab; Mohamed Belaoued; Irfan Mohiuddin; Fajri Kurniawan; Muhammad Khurram Khan. Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -14.

AMA Style

Abdelouahid Derhab, Mohamed Belaoued, Irfan Mohiuddin, Fajri Kurniawan, Muhammad Khurram Khan. Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-14.

Chicago/Turabian Style

Abdelouahid Derhab; Mohamed Belaoued; Irfan Mohiuddin; Fajri Kurniawan; Muhammad Khurram Khan. 2021. "Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-14.

Journal article
Published: 23 June 2021 in Applied Energy
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Real-time, accurate, and stable forecasting plays a vital role in making strategic decisions in the smart grid (SG). This ensures economic savings, effective planning, and reliable and secure power system operation. However, accurate and stable forecasting is challenging due to the uncertain and intermittent electric load behavior. In this context, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is needed. Thus, a support vector regression (SVR) model emerged to cater the non-linear time-series predictions. However, it suffers from computational complexity and hard-to-tune appropriate parameters problem. Due to these problems, forecasting results of SVR are not as accurate as required. To solve such problems, a novel hybrid approach is developed by integrating feature engineering (FE) and modified fire-fly optimization (mFFO) algorithm with SVR, namely FE-SVR-mFFO forecasting framework. FE eliminates redundant and irrelevant features to ensure high computational efficiency. The mFFO algorithm obtains and tunes the SVR model’s appropriate parameters to effectively avoid trapping into local optimum and returns accurate forecasting results. Besides, most literature studies are focused on forecast accuracy improvement. However, the forecasting model’s effectiveness and productiveness are determined equally by its stability and convergence rate. Considering only one objective (accuracy or stability or convergence rate) is inadequate; thus, the proposed FE-SVR-mFFO forecasting framework achieves these three relatively independent objectives simultaneously. To evaluate the effectiveness and applicability of the proposed framework, real half-hourly load data of five states of Australia (New South Wales (NSW), Queensland (QLD), South Australia (SA), Tasmania (TAS), and Victoria (VIC)) are employed as a case study. Experimental results show that the proposed framework outperforms benchmark frameworks like EMD-SVR-PSO, FS-TSFE-CBSSO, VMD-FFT-IOSVR, and DCP-SVM-WO in terms of accuracy, stability, and convergence rate.

ACS Style

Ghulam Hafeez; Imran Khan; Sadaqat Jan; Ibrar Ali Shah; Farrukh Aslam Khan; Abdelouahid Derhab. A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. Applied Energy 2021, 299, 117178 .

AMA Style

Ghulam Hafeez, Imran Khan, Sadaqat Jan, Ibrar Ali Shah, Farrukh Aslam Khan, Abdelouahid Derhab. A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid. Applied Energy. 2021; 299 ():117178.

Chicago/Turabian Style

Ghulam Hafeez; Imran Khan; Sadaqat Jan; Ibrar Ali Shah; Farrukh Aslam Khan; Abdelouahid Derhab. 2021. "A novel hybrid load forecasting framework with intelligent feature engineering and optimization algorithm in smart grid." Applied Energy 299, no. : 117178.

Research article
Published: 25 May 2021 in Complexity
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Checking the equivalence of two Boolean functions, or combinational circuits modeled as Boolean functions, is often desired when reliable and correct hardware components are required. The most common approaches to equivalence checking are based on simulation and model checking, which are constrained due to the popular memory and state explosion problems. Furthermore, such tools are often not user-friendly, thereby making it tedious to check the equivalence of large formulas or circuits. An alternative is to use mathematical tools, called interactive theorem provers, to prove the equivalence of two circuits; however, this requires human effort and expertise to write multiple output functions and carry out interactive proof of their equivalence. In this paper, we (1) define two simple, one formal and the other informal, gate-level hardware description languages, (2) design and develop a formal automatic combinational circuit equivalence checker (CoCEC) tool, and (3) test and evaluate our tool. The tool CoCEC is based on human-assisted theorem prover Coq, yet it checks the equivalence of circuit descriptions purely automatically through a human-friendly user interface. It either returns a machine-readable proof (term) of circuits’ equivalence or a counterexample of their inequality. The interface enables users to enter or load two circuit descriptions written in an easy and natural style. It automatically proves, in few seconds, the equivalence of circuits with as many as 45 variables (3.5 × 10 13 states). CoCEC has a mathematical foundation, and it is reliable, quick, and easy to use. The tool is intended to be used by digital logic circuit designers, logicians, students, and faculty during the digital logic design course.

ACS Style

Wilayat Khan; Farrukh Aslam Khan; Abdelouahid Derhab; Adi Alhudhaif. CoCEC: An Automatic Combinational Circuit Equivalence Checker Based on the Interactive Theorem Prover. Complexity 2021, 2021, 1 -12.

AMA Style

Wilayat Khan, Farrukh Aslam Khan, Abdelouahid Derhab, Adi Alhudhaif. CoCEC: An Automatic Combinational Circuit Equivalence Checker Based on the Interactive Theorem Prover. Complexity. 2021; 2021 ():1-12.

Chicago/Turabian Style

Wilayat Khan; Farrukh Aslam Khan; Abdelouahid Derhab; Adi Alhudhaif. 2021. "CoCEC: An Automatic Combinational Circuit Equivalence Checker Based on the Interactive Theorem Prover." Complexity 2021, no. : 1-12.

Journal article
Published: 22 April 2021 in IEEE Access
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Twitter is one of the most popular micro-blogging social media platforms that has millions of users. Due to its popularity, Twitter has been targeted by different attacks such as spreading rumors, phishing links, and malware. Tweet-based botnets represent a serious threat to users as they can launch large-scale attacks and manipulation campaigns. To deal with these threats, big data analytics techniques, particularly shallow and deep learning techniques have been leveraged in order to accurately distinguish between human accounts and tweet-based bot accounts. In this paper, we discuss existing techniques, and provide a taxonomy that classifies the state-of-the-art of tweet-based bot detection techniques. We also describe the shallow and deep learning techniques for tweet-based bot detection, along with their performance results. Finally, we present and discuss the challenges and open issues in the area of tweet-based bot detection.

ACS Style

Abdelouahid Derhab; Rahaf Alawwad; Khawlah Dehwah; Noshina Tariq; Farrukh Aslam Khan; Jalal Al-Muhtadi. Tweet-Based Bot Detection Using Big Data Analytics. IEEE Access 2021, 9, 65988 -66005.

AMA Style

Abdelouahid Derhab, Rahaf Alawwad, Khawlah Dehwah, Noshina Tariq, Farrukh Aslam Khan, Jalal Al-Muhtadi. Tweet-Based Bot Detection Using Big Data Analytics. IEEE Access. 2021; 9 ():65988-66005.

Chicago/Turabian Style

Abdelouahid Derhab; Rahaf Alawwad; Khawlah Dehwah; Noshina Tariq; Farrukh Aslam Khan; Jalal Al-Muhtadi. 2021. "Tweet-Based Bot Detection Using Big Data Analytics." IEEE Access 9, no. : 65988-66005.

Research article
Published: 21 April 2021 in Wireless Communications and Mobile Computing
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Multicontroller software-defined networks have been widely adopted to enable management of large-scale networks. However, they are vulnerable to several attacks including false data injection, which creates topology inconsistency among controllers. To deal with this issue, we propose BMC-SDN, a security architecture that integrates blockchain and multicontroller SDN and divides the network into several domains. Each SDN domain is managed by one master controller that communicates through blockchain with the masters of the other domains. The master controller creates blocks of network flow updates, and its redundant controllers validate the new block based on a proposed reputation mechanism. The reputation mechanism rates the controllers, i.e., block creator and voters, after each voting operation using constant and combined adaptive fading reputation strategies. The evaluation results demonstrate a fast and optimal detection of fraudulent flow rule injection.

ACS Style

Abdelouahid Derhab; Mohamed Guerroumi; Mohamed Belaoued; Omar Cheikhrouhou. BMC-SDN: Blockchain-Based Multicontroller Architecture for Secure Software-Defined Networks. Wireless Communications and Mobile Computing 2021, 2021, 1 -12.

AMA Style

Abdelouahid Derhab, Mohamed Guerroumi, Mohamed Belaoued, Omar Cheikhrouhou. BMC-SDN: Blockchain-Based Multicontroller Architecture for Secure Software-Defined Networks. Wireless Communications and Mobile Computing. 2021; 2021 ():1-12.

Chicago/Turabian Style

Abdelouahid Derhab; Mohamed Guerroumi; Mohamed Belaoued; Omar Cheikhrouhou. 2021. "BMC-SDN: Blockchain-Based Multicontroller Architecture for Secure Software-Defined Networks." Wireless Communications and Mobile Computing 2021, no. : 1-12.

Journal article
Published: 29 March 2021 in Applied Sciences
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Current developments in information technology and increased inclination towards smart cities have led to the initiation of a plethora of features by technology-oriented companies (i.e., car manufacturers) to improve users’ privacy and comfort. The invention of smart vehicle technology paved the way for the excessive use of machine-to-machine technologies. Moreover, third-party sharing of financial services are also introduced that support machine-to-machine (M2M) communication. These monetary systems’ prime focus is on improving reliability and security; however, they overlook aspects like behaviors and users’ need. For instance, people often hand over their bank cards or share their credentials with their colleagues to withdraw money on their behalf. Such behaviors may originate issues about privacy and security that can have severe losses for the card owner. This paper presents a novel blockchain-based strategy for payment of fueling of smart cars without any human interaction while maintaining transparency, privacy, and trust. The proposed system is capable of data sharing among the users of the system while securing sensitive information. Moreover, we also provide a blockchain-based secure privacy-preserving strategy for payment of fueling among the fuel seller and buyer without human intervention. Furthermore, we have also analytically evaluated several experiments to determine the proposed blockchain platform’s usability and efficiency. Lastly, we harness Hyperledger Caliper to assess the proposed system’s performance in terms of transaction latency, transactions per second, and resource consumption.

ACS Style

Faisal Jamil; Omar Cheikhrouhou; Harun Jamil; Anis Koubaa; Abdelouahid Derhab; Mohamed Ferrag. PetroBlock: A Blockchain-Based Payment Mechanism for Fueling Smart Vehicles. Applied Sciences 2021, 11, 3055 .

AMA Style

Faisal Jamil, Omar Cheikhrouhou, Harun Jamil, Anis Koubaa, Abdelouahid Derhab, Mohamed Ferrag. PetroBlock: A Blockchain-Based Payment Mechanism for Fueling Smart Vehicles. Applied Sciences. 2021; 11 (7):3055.

Chicago/Turabian Style

Faisal Jamil; Omar Cheikhrouhou; Harun Jamil; Anis Koubaa; Abdelouahid Derhab; Mohamed Ferrag. 2021. "PetroBlock: A Blockchain-Based Payment Mechanism for Fueling Smart Vehicles." Applied Sciences 11, no. 7: 3055.

Research article
Published: 22 December 2020 in Wireless Communications and Mobile Computing
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In the era of the Internet of Things (IoT), connected objects produce an enormous amount of data traffic that feed big data analytics, which could be used in discovering unseen patterns and identifying anomalous traffic. In this paper, we identify five key design principles that should be considered when developing a deep learning-based intrusion detection system (IDS) for the IoT. Based on these principles, we design and implement Temporal Convolution Neural Network (TCNN), a deep learning framework for intrusion detection systems in IoT, which combines Convolution Neural Network (CNN) with causal convolution. TCNN is combined with Synthetic Minority Oversampling Technique-Nominal Continuous (SMOTE-NC) to handle unbalanced dataset. It is also combined with efficient feature engineering techniques, which consist of feature space reduction and feature transformation. TCNN is evaluated on Bot-IoT dataset and compared with two common machine learning algorithms, i.e., Logistic Regression (LR) and Random Forest (RF), and two deep learning techniques, i.e., LSTM and CNN. Experimental results show that TCNN achieves a good trade-off between effectiveness and efficiency. It outperforms the state-of-the-art deep learning IDSs that are tested on Bot-IoT dataset and records an accuracy of 99.9986% for multiclass traffic detection, and shows a very close performance to CNN with respect to the training time.

ACS Style

Abdelouahid Derhab; Arwa Aldweesh; Ahmed Z. Emam; Farrukh Aslam Khan. Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering. Wireless Communications and Mobile Computing 2020, 2020, 1 -16.

AMA Style

Abdelouahid Derhab, Arwa Aldweesh, Ahmed Z. Emam, Farrukh Aslam Khan. Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering. Wireless Communications and Mobile Computing. 2020; 2020 ():1-16.

Chicago/Turabian Style

Abdelouahid Derhab; Arwa Aldweesh; Ahmed Z. Emam; Farrukh Aslam Khan. 2020. "Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering." Wireless Communications and Mobile Computing 2020, no. : 1-16.

Journal article
Published: 22 December 2020 in Sensors
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A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

ACS Style

Noshina Tariq; Muhammad Asim; Farrukh Aslam Khan; Thar Baker; Umair Khalid; Abdelouahid Derhab. A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things. Sensors 2020, 21, 23 .

AMA Style

Noshina Tariq, Muhammad Asim, Farrukh Aslam Khan, Thar Baker, Umair Khalid, Abdelouahid Derhab. A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things. Sensors. 2020; 21 (1):23.

Chicago/Turabian Style

Noshina Tariq; Muhammad Asim; Farrukh Aslam Khan; Thar Baker; Umair Khalid; Abdelouahid Derhab. 2020. "A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things." Sensors 21, no. 1: 23.

Journal article
Published: 27 October 2020 in Sensors
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In this paper, we investigate the problem of selective routing attack in wireless sensor networks by considering a novel threat, named the upstream-node effect, which limits the accuracy of the monitoring functions in deciding whether a monitored node is legitimate or malicious. To address this limitation, we propose a one-dimensional one-class classifier, named relaxed flow conservation constraint, as an intrusion detection scheme to counter the upstream node attack. Each node uses four types of relaxed flow conservation constraints to monitor all of its neighbors. Three constraints are applied by using one-hop knowledge, and the fourth one is calculated by monitoring two-hop information. The latter is obtained by proposing two-hop energy-efficient and secure reporting scheme. We theoretically analyze the security and performance of the proposed intrusion detection method. We also show the superiority of relaxed flow conservation constraint in defending against upstream node attack compared to other schemes. The simulation results show that the proposed intrusion detection system achieves good results in terms of detection effectiveness.

ACS Style

Abdelouahid Derhab; Abdelghani Bouras; Mohamed Belaoued; Leandros Maglaras; Farrukh Aslam Khan. Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks. Sensors 2020, 20, 6106 .

AMA Style

Abdelouahid Derhab, Abdelghani Bouras, Mohamed Belaoued, Leandros Maglaras, Farrukh Aslam Khan. Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks. Sensors. 2020; 20 (21):6106.

Chicago/Turabian Style

Abdelouahid Derhab; Abdelghani Bouras; Mohamed Belaoued; Leandros Maglaras; Farrukh Aslam Khan. 2020. "Two-Hop Monitoring Mechanism Based on Relaxed Flow Conservation Constraints against Selective Routing Attacks in Wireless Sensor Networks." Sensors 20, no. 21: 6106.

Original research
Published: 29 June 2020 in Journal of Ambient Intelligence and Humanized Computing
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The Android platform is highly targeted by malware developers, which aim to infect the maximum number of mobile devices by uploading their malicious applications to different app markets. In order to keep a healthy Android ecosystem, app-markets check the maliciousness of newly submitted apps. These markets need to (a) correctly detect malicious app, and (b) speed up the detection process of the most likely dangerous applications among an overwhelming flow of submitted apps, to quickly mitigate their potential damages. To address these challenges, we propose TriDroid, a market-scale triage and classification system for Android apps. TriDroid prioritizes apps analysis according to their risk likelihood. To this end, we categorize the submitted apps as: botnet, general malware, and benign. TriDroid starts by performing a (1) Triage process, which applies a fast coarse-grained and less-accurate analysis on a continuous stream of the submitted apps to identify their corresponding queue in a three-class priority queuing system. Then, (2) the Classification process extracts fine-grained static features from the apps in the priority queue, and applies three-class machine learning classifiers to confirm with high accuracy the classification decisions of the triage process. In addition to the priority queuing model, we also propose a multi-server queuing model where the classification of each app category is run on a different server. Experiments on a dataset with more than 24K malicious and 3K benign applications show that the priority model offers a trade-off between waiting time and processing overhead, as it requires only one server compared to the multi-server model. Also it successfully prioritizes malicious apps analysis, which allows a short waiting time for dangerous applications compared to the FIFO policy.

ACS Style

Abdelouahab Amira; Abdelouahid Derhab; ElMouatez Billah Karbab; Omar Nouali; Farrukh Aslam Khan. TriDroid: a triage and classification framework for fast detection of mobile threats in android markets. Journal of Ambient Intelligence and Humanized Computing 2020, 12, 1731 -1755.

AMA Style

Abdelouahab Amira, Abdelouahid Derhab, ElMouatez Billah Karbab, Omar Nouali, Farrukh Aslam Khan. TriDroid: a triage and classification framework for fast detection of mobile threats in android markets. Journal of Ambient Intelligence and Humanized Computing. 2020; 12 (2):1731-1755.

Chicago/Turabian Style

Abdelouahab Amira; Abdelouahid Derhab; ElMouatez Billah Karbab; Omar Nouali; Farrukh Aslam Khan. 2020. "TriDroid: a triage and classification framework for fast detection of mobile threats in android markets." Journal of Ambient Intelligence and Humanized Computing 12, no. 2: 1731-1755.

Journal article
Published: 08 June 2020 in Journal of Information Security and Applications
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False Data Injection Attack (FDIA) is one of the most dangerous cyber attacks against smart power grids, as it could cause severe physical and economic damage. In this paper, we review and compare previous surveys on FDIA, which mostly focus only on the state estimation component. Differently, our survey describes the FDIAs that target the different components of the on-line power system security. It also provides two novel attack classifications. The first classification categorizes the different FDIAs with respect to three levels: targeted systems at the first level, targeted subsystems at the second level, and the attacks targeting the subsystems at the third level. The second classification considers two criteria: targeted sub system and the impact of the attack, which can be physical and/or economic. The countermeasures are classified according to two dimensions: (i) the targeted subsystem and (ii) the class of countermeasure: preventive or detective. Both preventive and detective classes are further categorized according to different approaches. In addition, the countermeasures are presented along with their performance results. Finally, open issues are identified, and future research directions are recommended.

ACS Style

Souhila Aoufi; Abdelouahid Derhab; Mohamed Guerroumi. Survey of false data injection in smart power grid: Attacks, countermeasures and challenges. Journal of Information Security and Applications 2020, 54, 102518 .

AMA Style

Souhila Aoufi, Abdelouahid Derhab, Mohamed Guerroumi. Survey of false data injection in smart power grid: Attacks, countermeasures and challenges. Journal of Information Security and Applications. 2020; 54 ():102518.

Chicago/Turabian Style

Souhila Aoufi; Abdelouahid Derhab; Mohamed Guerroumi. 2020. "Survey of false data injection in smart power grid: Attacks, countermeasures and challenges." Journal of Information Security and Applications 54, no. : 102518.

Journal article
Published: 03 May 2020 in Energies
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Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.

ACS Style

Ghulam Hafeez; Khurram Saleem Alimgeer; Zahid Wadud; Zeeshan Shafiq; Mohammad Usman Ali Khan; Imran Khan; Farrukh Aslam Khan; Abdelouahid Derhab. A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid. Energies 2020, 13, 2244 .

AMA Style

Ghulam Hafeez, Khurram Saleem Alimgeer, Zahid Wadud, Zeeshan Shafiq, Mohammad Usman Ali Khan, Imran Khan, Farrukh Aslam Khan, Abdelouahid Derhab. A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid. Energies. 2020; 13 (9):2244.

Chicago/Turabian Style

Ghulam Hafeez; Khurram Saleem Alimgeer; Zahid Wadud; Zeeshan Shafiq; Mohammad Usman Ali Khan; Imran Khan; Farrukh Aslam Khan; Abdelouahid Derhab. 2020. "A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid." Energies 13, no. 9: 2244.

Journal article
Published: 11 February 2020 in IEEE Access
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ACS Style

Mohamed Amine Ferrag; Lei Shu; Xing Yang; Abdelouahid Derhab; Leandros Maglaras. Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges. IEEE Access 2020, 8, 32031 -32053.

AMA Style

Mohamed Amine Ferrag, Lei Shu, Xing Yang, Abdelouahid Derhab, Leandros Maglaras. Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges. IEEE Access. 2020; 8 ():32031-32053.

Chicago/Turabian Style

Mohamed Amine Ferrag; Lei Shu; Xing Yang; Abdelouahid Derhab; Leandros Maglaras. 2020. "Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges." IEEE Access 8, no. : 32031-32053.

Journal article
Published: 03 February 2020 in IEEE Access
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Security analysts have shown that it is possible to compromise the mobile two-factor authentication applications that employ SMS-based authentication. In this paper, we consider that offloading mobile applications to the cloud, which is resource-rich and provides a more secure environment, represents a good solution when energy limitation and security constraints are raised. To this end, we propose an offloading architecture for the two-factor mutual authentication applications, and a novel two-factor mutual authentication scheme based on a novel mechanism, named virtual smart card. We also propose a decision-making process to offload the authentication application and its virtual smart card, based on three conditions: security, mobile device’s residual energy, and energy cost. We analytically derive from the energy cost formula, the lower-bound on the mobile application running time to perform offloading. We analyze and verify the security properties of the proposed architecture, and provide evaluation results of the two-factor mutual authentication protocol and the offloading decision-making process.

ACS Style

Abdelouahid Derhab; Mohamed Belaoued; Mohamed Guerroumi; Farrukh Aslam Khan. Two-Factor Mutual Authentication Offloading for Mobile Cloud Computing. IEEE Access 2020, 8, 28956 -28969.

AMA Style

Abdelouahid Derhab, Mohamed Belaoued, Mohamed Guerroumi, Farrukh Aslam Khan. Two-Factor Mutual Authentication Offloading for Mobile Cloud Computing. IEEE Access. 2020; 8 (99):28956-28969.

Chicago/Turabian Style

Abdelouahid Derhab; Mohamed Belaoued; Mohamed Guerroumi; Farrukh Aslam Khan. 2020. "Two-Factor Mutual Authentication Offloading for Mobile Cloud Computing." IEEE Access 8, no. 99: 28956-28969.

Journal article
Published: 13 January 2020 in IEEE Access
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ACS Style

Mohamed Belaoued; Abdelouahid Derhab; Smaine Mazouzi; Farrukh Aslam Khan. MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware Assistance. IEEE Access 2020, 8, 14329 -14343.

AMA Style

Mohamed Belaoued, Abdelouahid Derhab, Smaine Mazouzi, Farrukh Aslam Khan. MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware Assistance. IEEE Access. 2020; 8 ():14329-14343.

Chicago/Turabian Style

Mohamed Belaoued; Abdelouahid Derhab; Smaine Mazouzi; Farrukh Aslam Khan. 2020. "MACoMal: A Multi-Agent Based Collaborative Mechanism for Anti-Malware Assistance." IEEE Access 8, no. : 14329-14343.

Review
Published: 04 December 2019 in Neurocomputing
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Deep learning is one of the advanced approaches of machine learning, and has attracted a growing attention in the recent years. It is used nowadays in different domains and applications such as pattern recognition, medical prediction, and speech recognition. Differently from traditional learning algorithms, deep learning can overcome the dependency on hand-designed features. Deep learning experience is particularly improved by leveraging powerful infrastructures such as clouds and adopting collaborative learning for model training. However, this comes at the expense of privacy, especially when sensitive data are processed during the training and the prediction phases, as well as when training model is shared. In this paper, we provide a review of the existing privacy-preserving deep learning techniques, and propose a novel multi-level taxonomy, which categorizes the current state-of-the-art privacy-preserving deep learning techniques on the basis of privacy-preserving tasks at the top level, and key technological concepts at the base level. This survey further summarizes evaluation results of the reviewed solutions with respect to defined performance metrics. In addition, it derives a set of learned lessons from each privacy-preserving task. Finally, it highlights open research challenges and provides some recommendations as future research directions.

ACS Style

Amine Boulemtafes; Abdelouahid Derhab; Yacine Challal. A review of privacy-preserving techniques for deep learning. Neurocomputing 2019, 384, 21 -45.

AMA Style

Amine Boulemtafes, Abdelouahid Derhab, Yacine Challal. A review of privacy-preserving techniques for deep learning. Neurocomputing. 2019; 384 ():21-45.

Chicago/Turabian Style

Amine Boulemtafes; Abdelouahid Derhab; Yacine Challal. 2019. "A review of privacy-preserving techniques for deep learning." Neurocomputing 384, no. : 21-45.

Journal article
Published: 22 November 2019 in IEEE Access
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Underwater Wireless Sensor Networks (UWSNs) face numerous challenges due to small bandwidth, longer propagation delay, limited energy resources and high deployment cost. Development of efficient routing strategies is therefore incumbent and has remained the focus of researchers. Many routing protocols have been proposed to address these challenges and to further improve the performance of state of the art protocols. In Weighting Depth and Forwarding Area Division-Depth Based Routing (WDFAD-DBR), forwarding is decided on the basis of weighting depth difference, which is not an efficient way for void hole avoidance. In this paper we propose a depth based routing mechanism called Energy Balanced Efficient and Reliable Routing (EBER2) protocol for UWSNs. First and foremost, energy balancing among the neighbours and reliability is achieved by considering residual energy and the number of Potential Forwarding Nodes (PFNs) of the forwarder node respectively. Secondly, energy efficiency is enhanced by dividing transmission range into power levels and the forwarders are allowed to adaptively adjust their transmission power according to the farthest node in their neighbour list. Thirdly, duplicate packets are reduced by comparing depth, residual energy and PFNs among the neighbours. Moreover, network latency is decreased by deploying two sinks at those areas of the network which have high traffic density. Simulation results show that EBER2 has higher Packet Delivery Ratio (PDR), lower energy tax, and less duplicate packets than the competing routing protocol WDFAD-DBR.

ACS Style

Zahid Wadud; Muhammad Ismail; Abdul Baseer Qazi; Farrukh Aslam Khan; Abdelouahid Derhab; Ibrar Ahmad; Arbab Masood Ahmad. An Energy Balanced Efficient and Reliable Routing Protocol for Underwater Wireless Sensor Networks. IEEE Access 2019, 7, 175980 -175999.

AMA Style

Zahid Wadud, Muhammad Ismail, Abdul Baseer Qazi, Farrukh Aslam Khan, Abdelouahid Derhab, Ibrar Ahmad, Arbab Masood Ahmad. An Energy Balanced Efficient and Reliable Routing Protocol for Underwater Wireless Sensor Networks. IEEE Access. 2019; 7 (99):175980-175999.

Chicago/Turabian Style

Zahid Wadud; Muhammad Ismail; Abdul Baseer Qazi; Farrukh Aslam Khan; Abdelouahid Derhab; Ibrar Ahmad; Arbab Masood Ahmad. 2019. "An Energy Balanced Efficient and Reliable Routing Protocol for Underwater Wireless Sensor Networks." IEEE Access 7, no. 99: 175980-175999.

Journal article
Published: 16 October 2019 in Knowledge-Based Systems
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The massive growth of data that are transmitted through a variety of devices and communication protocols have raised serious security concerns, which have increased the importance of developing advanced intrusion detection systems (IDSs). Deep learning is an advanced branch of machine learning, composed of multiple layers of neurons that represent the learning process. Deep learning can cope with large-scale data and has shown success in different fields. Therefore, researchers have paid more attention to investigating deep learning for intrusion detection. This survey comprehensively reviews and compares the key previous deep learning-focused cybersecurity surveys. Through an extensive review, this survey provides a novel fine-grained taxonomy that categorizes the current state-of-the-art deep learning-based IDSs with respect to different facets, including input data, detection, deployment, and evaluation strategies. Each facet is further classified according to different criteria. This survey also compares and discusses the related experimental solutions proposed as deep learning-based IDSs. By analysing the experimental studies, this survey discusses the role of deep learning in intrusion detection, the impact of intrusion detection datasets, and the efficiency and effectiveness of the proposed approaches. The findings demonstrate that further effort is required to improve the current state-of-the art. Finally, open research challenges are identified, and future research directions for deep learning-based IDSs are recommended.

ACS Style

Arwa Aldweesh; Abdelouahid Derhab; Ahmed Emam. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems 2019, 189, 105124 .

AMA Style

Arwa Aldweesh, Abdelouahid Derhab, Ahmed Emam. Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowledge-Based Systems. 2019; 189 ():105124.

Chicago/Turabian Style

Arwa Aldweesh; Abdelouahid Derhab; Ahmed Emam. 2019. "Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues." Knowledge-Based Systems 189, no. : 105124.

Journal article
Published: 15 July 2019 in Sensors
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The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

ACS Style

Abdelouahid Derhab; Mohamed Guerroumi; Abdu Gumaei; Leandros Maglaras; Mohamed Amine Ferrag; Mithun Mukherjee; Farrukh Aslam Khan. Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security. Sensors 2019, 19, 3119 .

AMA Style

Abdelouahid Derhab, Mohamed Guerroumi, Abdu Gumaei, Leandros Maglaras, Mohamed Amine Ferrag, Mithun Mukherjee, Farrukh Aslam Khan. Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security. Sensors. 2019; 19 (14):3119.

Chicago/Turabian Style

Abdelouahid Derhab; Mohamed Guerroumi; Abdu Gumaei; Leandros Maglaras; Mohamed Amine Ferrag; Mithun Mukherjee; Farrukh Aslam Khan. 2019. "Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security." Sensors 19, no. 14: 3119.