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Hadis Karimipour
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada

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Journal article
Published: 16 August 2021 in Applied Sciences
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In recent years, Smart Farming (SF) and Precision Agriculture (PA) have attracted attention from both the agriculture industry as well as the research community. Altogether, SF and PA aim to help farmers use inputs (such as fertilizers and pesticides) more efficiently through using Internet of Things (IoT) devices, but in doing so, they create new security threats that can defeat this purpose in the absence of adequate awareness and proper countermeasures. A survey on different security-related challenges is required to raise awareness and pave they way for further research in this area. In this paper, we first itemize the security aspects of SF and PA. Next, we review the types of cyber attacks that can violate each of these aspects. Accordingly, we present a taxonomy on cyber-threats to SF and PA on the basis of their relations to different stages of Cyber-Kill Chain (CKC). Among cyber-threats, we choose Advanced Persistent Threats (APTs) for further study. Finally, we studied related risk mitigation strategies and countermeasure, and developed a future road map for further study in this area. This paper’s main contribution is a categorization of security threats within the SF/PA areas and provide a taxonomy of security threats for SF environments so that we may detect the behavior of APT attacks and any other security threat in SF and PA environments.

ACS Style

Abbas Yazdinejad; Behrouz Zolfaghari; Amin Azmoodeh; Ali Dehghantanha; Hadis Karimipour; Evan Fraser; Arthur G. Green; Conor Russell; Emily Duncan. A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures. Applied Sciences 2021, 11, 7518 .

AMA Style

Abbas Yazdinejad, Behrouz Zolfaghari, Amin Azmoodeh, Ali Dehghantanha, Hadis Karimipour, Evan Fraser, Arthur G. Green, Conor Russell, Emily Duncan. A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures. Applied Sciences. 2021; 11 (16):7518.

Chicago/Turabian Style

Abbas Yazdinejad; Behrouz Zolfaghari; Amin Azmoodeh; Ali Dehghantanha; Hadis Karimipour; Evan Fraser; Arthur G. Green; Conor Russell; Emily Duncan. 2021. "A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats and Countermeasures." Applied Sciences 11, no. 16: 7518.

Journal article
Published: 15 July 2021 in Ad Hoc Networks
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Machine learning is significantly used for malware and adversary detection in the industrial internet of things networks. However, majority of these methods require a significant prior knowledge of malware properties to identify optimal features for malware detection. This is a more significant challenge in IoT environment due to limited availability of malware samples. Some researchers utilized data deformation techniques such as converting malware to images or music to generate features that can be used for malware detection. However, these processes can be time-consuming and require a significant amount of data. This paper proposes MalGan, a framework for detecting and generating new malware samples based on the raw byte code at the edge layer of the Internet of Things (IoT) networks. Convolutional Neural Network (CNN) was utilized to extract high-level features, and boundary-seeking Generative Adversarial Network technique was used to generate new malware samples. Thus, even with a few malware samples, a significant number of previously unseen malware samples are detectable with high accuracy. To capture the short-term and long-term dependency of features, we employed an attention-based model, a combination of CNN and Long Short Term Memory. The attention mechanism improves the model’s performance by increasing or decreasing attention to certain parts of the features. The proposed method is examined extensively using standard Windows and IoT malware datasets. The experimental results indicate that our proposed MalGan is the method of choice, as it offers a higher detection rate compared to the previous malware detection algorithms.

ACS Style

Zahra Moti; Sattar Hashemi; Hadis Karimipour; Ali Dehghantanha; Amir Namavar Jahromi; Lida Abdi; Fatemeh Alavi. Generative adversarial network to detect unseen Internet of Things malware. Ad Hoc Networks 2021, 122, 102591 .

AMA Style

Zahra Moti, Sattar Hashemi, Hadis Karimipour, Ali Dehghantanha, Amir Namavar Jahromi, Lida Abdi, Fatemeh Alavi. Generative adversarial network to detect unseen Internet of Things malware. Ad Hoc Networks. 2021; 122 ():102591.

Chicago/Turabian Style

Zahra Moti; Sattar Hashemi; Hadis Karimipour; Ali Dehghantanha; Amir Namavar Jahromi; Lida Abdi; Fatemeh Alavi. 2021. "Generative adversarial network to detect unseen Internet of Things malware." Ad Hoc Networks 122, no. : 102591.

Review article
Published: 05 June 2021 in Physical Communication
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The massive integration of low-cost communication networks and Internet of Things (IoT) in today’s cyber–physical grids has been accompanied by significant concerns regarding potential security threats. Specifically, wireless communication technology introduces additional vulnerability in terms of network security. In addition to cyber-security issues that have been investigated extensively, we must consider physical layer security. As such, considerable efforts have been employed toward developing a solution to address cyber-security issues. However, there are limited efforts on developing intrusion detection systems for physical layer security. In this paper, we propose an intelligent attack detection and identification model capable of classifying the attack type in the physical layer based on an ensemble of machine learning methods. Furthermore, the proposed model localizes the attack or fault to specific features or measurements in the system to assist cyber-security professionals in mitigating the effect of the attack in communication networks. The proposed model is evaluated on a smart grids dataset simulated by the Oak Ridge National Laboratories and is compared with traditional machine learning classifiers. The localization of attacks and faults is tested by splitting the data and measuring the correlation of the localization metrics produced by the proposed model. The results demonstrate the effectiveness of the proposed method at classifying and localizing attacks compared to peer approaches.

ACS Style

Jacob Sakhnini; Hadis Karimipour; Ali Dehghantanha; Reza M. Parizi. Physical layer attack identification and localization in cyber–physical grid: An ensemble deep learning based approach. Physical Communication 2021, 47, 101394 .

AMA Style

Jacob Sakhnini, Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi. Physical layer attack identification and localization in cyber–physical grid: An ensemble deep learning based approach. Physical Communication. 2021; 47 ():101394.

Chicago/Turabian Style

Jacob Sakhnini; Hadis Karimipour; Ali Dehghantanha; Reza M. Parizi. 2021. "Physical layer attack identification and localization in cyber–physical grid: An ensemble deep learning based approach." Physical Communication 47, no. : 101394.

Journal article
Published: 22 April 2021 in Energies
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The transition towards the massive penetration of Renewable Energy Resources (RESs) into the electricity system requires the implementation of the Smart Grid (SG) paradigm with innovative control systems and equipment. In this new context, Distributed Energy Resources (DERs), including renewable sources and responsive loads, should be redesigned to enable aggregators to provide ancillary services. In fact, by using the Internet of Things (IoT) systems, aggregators can explore energy usage patterns from residential users, also known as prosumers and predict their services. This is undoubtedly important especially for SGs facing the presence of several RESs, where understanding the optimal match between demand and production is desirable from several points of view. However, revealing energy patterns and information can be of concern for privacy if the entire system is not properly designed. In this article, by assuming that the security of low-level communication protocols is guaranteed, we focus our attention at higher levels, in particular at the application level of managed IoT systems used by aggregators. In this regard, we provide an overview of the best practices and outline possible privacy leakages risks along with a list of correlated attacks.

ACS Style

Giuseppe Marco; Vincenzo Loia; Hadis Karimipour; Pierluigi Siano. Assessing Insider Attacks and Privacy Leakage in Managed IoT Systems for Residential Prosumers. Energies 2021, 14, 2385 .

AMA Style

Giuseppe Marco, Vincenzo Loia, Hadis Karimipour, Pierluigi Siano. Assessing Insider Attacks and Privacy Leakage in Managed IoT Systems for Residential Prosumers. Energies. 2021; 14 (9):2385.

Chicago/Turabian Style

Giuseppe Marco; Vincenzo Loia; Hadis Karimipour; Pierluigi Siano. 2021. "Assessing Insider Attacks and Privacy Leakage in Managed IoT Systems for Residential Prosumers." Energies 14, no. 9: 2385.

Editorial
Published: 12 April 2021 in Computers & Electrical Engineering
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ACS Style

Raymond Choo; Ali Dehghantanha; Hadis Karimipour. Introduction to the special section on application of artificial intelligence in security of cyber physical systems (VSI-aicps). Computers & Electrical Engineering 2021, 92, 107145 .

AMA Style

Raymond Choo, Ali Dehghantanha, Hadis Karimipour. Introduction to the special section on application of artificial intelligence in security of cyber physical systems (VSI-aicps). Computers & Electrical Engineering. 2021; 92 ():107145.

Chicago/Turabian Style

Raymond Choo; Ali Dehghantanha; Hadis Karimipour. 2021. "Introduction to the special section on application of artificial intelligence in security of cyber physical systems (VSI-aicps)." Computers & Electrical Engineering 92, no. : 107145.

Journal article
Published: 29 March 2021 in IEEE Access
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Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

ACS Style

Arezoo Jahani; Kazem Zare; Leyli Mohammad Khanli; Hadis Karimipour. Optimized Power Trading of Reconfigurable Microgrids in Distribution Energy Market. IEEE Access 2021, 9, 48218 -48235.

AMA Style

Arezoo Jahani, Kazem Zare, Leyli Mohammad Khanli, Hadis Karimipour. Optimized Power Trading of Reconfigurable Microgrids in Distribution Energy Market. IEEE Access. 2021; 9 ():48218-48235.

Chicago/Turabian Style

Arezoo Jahani; Kazem Zare; Leyli Mohammad Khanli; Hadis Karimipour. 2021. "Optimized Power Trading of Reconfigurable Microgrids in Distribution Energy Market." IEEE Access 9, no. : 48218-48235.

Journal article
Published: 18 January 2021 in IEEE Access
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Integrating microgrids within distribution systems can significantly improve the power system’s reliability while reducing operating costs. However, due to the unintentional disaster conditions, sometimes distribution systems and microgrids cannot support each other, and the microgrids are forced to work in the islanded mode. Accordingly, we developed an optimal resilient scheduling scheme that guarantees networked microgrids (NMGs) reliable operation in the normal and islanding modes. To achieve this aim, the problem is decomposed into day-ahead normal operation (grid-connected) and real-time islanded examination by benders decomposition algorithm perspective. The specified scheme of NMGs in the normal operation will be scrutinized in the real-time islanded mode. According to the benders decomposition theory, the scheduling of NMGs would be revised in the next iteration if the current schedule is not feasible for possible real-time islanding conditions. The status of thermal units, charging, and discharging of energy storage systems respecting their other constraints are changed depending on the type and severity of mismatches between generation and demand. Three different interconnection topologies are tested for assessing the performance of the proposed method and the impact of transaction energy between NMGs on that. Numerical simulations illustrate the advantages of the proposed scheme and explain its merits.

ACS Style

Hadi Safari Fesagandis; Mehdi Jalali; Kazem Zare; Mehdi Abapour; Hadis Karimipour. Resilient Scheduling of Networked Microgrids Against Real-Time Failures. IEEE Access 2021, 9, 21443 -21456.

AMA Style

Hadi Safari Fesagandis, Mehdi Jalali, Kazem Zare, Mehdi Abapour, Hadis Karimipour. Resilient Scheduling of Networked Microgrids Against Real-Time Failures. IEEE Access. 2021; 9 ():21443-21456.

Chicago/Turabian Style

Hadi Safari Fesagandis; Mehdi Jalali; Kazem Zare; Mehdi Abapour; Hadis Karimipour. 2021. "Resilient Scheduling of Networked Microgrids Against Real-Time Failures." IEEE Access 9, no. : 21443-21456.

Journal article
Published: 25 September 2020 in IEEE Internet of Things Journal
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Internet of Things (IoT) devices are increasingly targeted, partly due to their presence in a broad range of applications. In this paper, we propose a multi-kernel Support Vector Machine (SVM) for IoT cloud-edge gateway malware hunting, using the Grey Wolves Optimization (GWO) technique. This meta-heuristic approach is used for optimum selection of features distinguishing between malicious and benign applications at the IoT cloud-edge gateway. The model is trained with the Opcode and Bytecode of IoT malware samples (i.e., the training dataset comprises 271 benign and 281 malicious Cortex A9 samples) and evaluated using K-fold cross-validation technique. We validate the robustness of the proposed model, in terms of its ability to detect previously unseen IoT malware samples. We achieve an accuracy of 99.72% on the combination of Radial Basis Function (RBF) and Polynomial Kernels. Moreover, our proposed model only requires 20 seconds for training in comparison to previous deep neural network model that requires over 80 seconds to be trained on the same data. Overall, the proposed multi-kernel SVM approach outperforms Deep Neural Networks (DNN) and fuzzy-based IoT malware hunting techniques, in terms of accuracy, while significantly reducing the computational cost and the training time.

ACS Style

Hamed Haddadpajouh; Alireza Mohtadi; Ali Dehghantanaha; Hadis Karimipour; Xiaodong Lin; Kim-Kwang Raymond Choo. A Multikernel and Metaheuristic Feature Selection Approach for IoT Malware Threat Hunting in the Edge Layer. IEEE Internet of Things Journal 2020, 8, 4540 -4547.

AMA Style

Hamed Haddadpajouh, Alireza Mohtadi, Ali Dehghantanaha, Hadis Karimipour, Xiaodong Lin, Kim-Kwang Raymond Choo. A Multikernel and Metaheuristic Feature Selection Approach for IoT Malware Threat Hunting in the Edge Layer. IEEE Internet of Things Journal. 2020; 8 (6):4540-4547.

Chicago/Turabian Style

Hamed Haddadpajouh; Alireza Mohtadi; Ali Dehghantanaha; Hadis Karimipour; Xiaodong Lin; Kim-Kwang Raymond Choo. 2020. "A Multikernel and Metaheuristic Feature Selection Approach for IoT Malware Threat Hunting in the Edge Layer." IEEE Internet of Things Journal 8, no. 6: 4540-4547.

Journal article
Published: 02 September 2020 in Computers & Electrical Engineering
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The ever-growing expansion of smart cities and the Internet of Things (IoT) offer a promising solution to many contemporary urban challenges. However, this digital transformation also results in cyber-security loopholes which can be exploited by malicious hackers to wreak substantial digital and physical damage. Malware is the primary tool of cyber-criminals for attacking digital systems. In this paper, a multi-view ensemble threat hunting model based on Sparse Representation based Classifier (SRC) is proposed to use in IoT systems that are finding domain space in the advent of Smart Cities. An ensemble of SRCs is considered where every individual SRC classifies malware by Opcode, Bytecode and system call views of several standard IoT and Ransomware datasets. The final decision is made through weighted majority voting. SRC is employed to alleviate the complexity of the base classifiers. Experimental results verify the efficiency and robustness of the proposed model in different balanced and imbalanced environments. The proposed model outperforms all base classifiers and several well-known works in current literature.

ACS Style

Seyed Mehdi Hazrati Fard; Hadis Karimipour; Ali Dehghantanha; Amir Namavar Jahromi; Gautam Srivastava. Ensemble sparse representation-based cyber threat hunting for security of smart cities. Computers & Electrical Engineering 2020, 88, 106825 .

AMA Style

Seyed Mehdi Hazrati Fard, Hadis Karimipour, Ali Dehghantanha, Amir Namavar Jahromi, Gautam Srivastava. Ensemble sparse representation-based cyber threat hunting for security of smart cities. Computers & Electrical Engineering. 2020; 88 ():106825.

Chicago/Turabian Style

Seyed Mehdi Hazrati Fard; Hadis Karimipour; Ali Dehghantanha; Amir Namavar Jahromi; Gautam Srivastava. 2020. "Ensemble sparse representation-based cyber threat hunting for security of smart cities." Computers & Electrical Engineering 88, no. : 106825.

Journal article
Published: 10 August 2020 in IEEE Internet of Things Journal
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There is currently widespread use of drones and drone technology due to their rising applications that have come into fruition in the military, safety surveillance, agriculture, smart transportation, shipping, and delivery of packages in our Internet of Things global landscape. However, there are security-specific challenges with the authentication of drones while airborne. The current authentication approaches, in most drone-based applications, are subject to latency issues in real-time with security vulnerabilities for attacks. To address such issues, we introduce a secure authentication model with low-latency for drones in smart cities that looks to leverage blockchain technology. We apply a zone-based architecture in a network of drones, and use a customized decentralized consensus, known as DDPOS (Drone-based Delegated Proof of Stake), for drones among zones in a smart city that does not require re-authentication. The proposed architecture aims for positive impacts on increased security and reduced latency on the Internet of Drones (IoDs). Moreover, we provide an empirical analysis of the proposed architecture compared to other peer models previously proposed for IoDs to demonstrate its performance and security authentication capability. The experimental results clearly show that not only does the proposed architecture have low packet loss rate, high throughput, and low end-to-end delay in comparison to peer models, but also can detect 97.5% of attacks by malicious drones while airborne.

ACS Style

Abbas Yazdinejad; Reza M. Parizi; Ali Dehghantanha; Hadis Karimipour; Gautam Srivastava; Mohammed Aledhari. Enabling Drones in the Internet of Things With Decentralized Blockchain-Based Security. IEEE Internet of Things Journal 2020, 8, 6406 -6415.

AMA Style

Abbas Yazdinejad, Reza M. Parizi, Ali Dehghantanha, Hadis Karimipour, Gautam Srivastava, Mohammed Aledhari. Enabling Drones in the Internet of Things With Decentralized Blockchain-Based Security. IEEE Internet of Things Journal. 2020; 8 (8):6406-6415.

Chicago/Turabian Style

Abbas Yazdinejad; Reza M. Parizi; Ali Dehghantanha; Hadis Karimipour; Gautam Srivastava; Mohammed Aledhari. 2020. "Enabling Drones in the Internet of Things With Decentralized Blockchain-Based Security." IEEE Internet of Things Journal 8, no. 8: 6406-6415.

Chapter
Published: 24 July 2020 in Security of Cyber-Physical Systems
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According to the increasing demand for electrical energy, the development of power systems and using smart grid technologies are vital. Smart grids are known as the new generation of power systems applying intelligent tools and features to provide higher performance, stability, reliability, and manageability. For these purposes, power systems face two major challenges. The first one is the systems are more vulnerable to a cyberattack. This vulnerability originates from relying on Information and Communication Technology (ICT) systems. The second challenge is the consumption of fossil fuels as a major power source increases, which is costly and pollutes the environment, so it is necessary to use renewable energy like the wind as an alternative. Regarding enormous fluctuations in wind speed at different months and even each minute of a day, whereas it is impossible to store electrical energy on a massive scale, prediction plays a major rule to integrate the power grid and wind energy. Therefore, to address these challenges, some of the Machine Learning algorithms are tested to detect attacks and predict wind power generation. This chapter first detects attacks on a dataset that comes from a smart grid, the results are compared based on F-Score (Precision/Recall) Accuracy and also considering the velocity. The results show the best performance belongs to the Random forest if test time (score time) is ignored otherwise the K-Nearest Neighbor (KNN) has great performance. Towards the end, predict wind power generation by using a dataset that has been collected over a period of six years, by considering the nature of the dataset as a time series pattern, several methods of learning like Neural network (NN), Long short-term memory (LSTM) have been applied. Finally, Mean Absolute Error (MAE) and accuracy are chosen to evaluate the performance of the methods.

ACS Style

Hossein Mohammadi Rouzbahani; Zahra Faraji; Mohammad Amiri-Zarandi; Hadis Karimipour. AI-Enabled Security Monitoring in Smart Cyber Physical Grids. Security of Cyber-Physical Systems 2020, 145 -167.

AMA Style

Hossein Mohammadi Rouzbahani, Zahra Faraji, Mohammad Amiri-Zarandi, Hadis Karimipour. AI-Enabled Security Monitoring in Smart Cyber Physical Grids. Security of Cyber-Physical Systems. 2020; ():145-167.

Chicago/Turabian Style

Hossein Mohammadi Rouzbahani; Zahra Faraji; Mohammad Amiri-Zarandi; Hadis Karimipour. 2020. "AI-Enabled Security Monitoring in Smart Cyber Physical Grids." Security of Cyber-Physical Systems , no. : 145-167.

Chapter
Published: 24 July 2020 in Security of Cyber-Physical Systems
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Cyber-physical system (CPS) has been one of the important parts of infrastructures and industrial control systems for years. Its footprint is seen in a wide range of domains including medical systems, automotive systems, environmental control, avionics, robotics, energy conservation, smart structure, etc. Due to the recent large-scale distribution, CPSs are more complicated than before, and they are facing a considerable number of challenges. For instance, the rate of uncertainty that they should handle has increased, and they have become more vulnerable to cyber-attacks. In order to manage unprecedented situations and uncertainty, CPS-run software should be able to control the operations of CPS, while remaining self-adaptive and goal-aware. Moreover, due to the devastating and irrecoverable consequences of CPS-specific attacks, there is a huge need to be able to detect and predict such attacks before happening. Anomaly detection is considered as a decent technique in identifying vulnerabilities in software systems. The best method that provides novel security technologies is machine-learning algorithms, which are widely used in anomaly detection in CPS networks. In this paper, different algorithms are implemented for detecting anomalies in order to confirm that machine learning and deep learning methods have strong potential to be used for attack detection in critical cyber- physical infrastructures.

ACS Style

Farnaz Seyyed Mozaffari; Hadis Karimipour; Reza M. Parizi. Learning Based Anomaly Detection in Critical Cyber-Physical Systems. Security of Cyber-Physical Systems 2020, 107 -130.

AMA Style

Farnaz Seyyed Mozaffari, Hadis Karimipour, Reza M. Parizi. Learning Based Anomaly Detection in Critical Cyber-Physical Systems. Security of Cyber-Physical Systems. 2020; ():107-130.

Chicago/Turabian Style

Farnaz Seyyed Mozaffari; Hadis Karimipour; Reza M. Parizi. 2020. "Learning Based Anomaly Detection in Critical Cyber-Physical Systems." Security of Cyber-Physical Systems , no. : 107-130.

Chapter
Published: 24 July 2020 in Security of Cyber-Physical Systems
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The smart grid is a complex Cyber-Physical System (CPS) which integrates distributed energy resources, includes different interaction between customers and utility, and facilitates the participation of customers, in the electricity market. Despite all the technical, environmental and economic benefits of the smart grid, it is vulnerable to cyber-attacks because of the two-way communications. Cyber-attacks on the smart grid result in erroneous decisions by the control center. The consequences of these decisions could be transmission congestion or even worse consequences like cascading failures which causes catastrophic blackouts. State estimation is an important part of smart grid operation and control that critical power system applications such as optimal power flow calculation and contingency analysis depend on it. Consequently, the security of the state estimator is vital for maintaining the reliable and safe operation of the smart grid. The state estimator receives various real-time measurement data with errors from the smart grid and determines the best estimation of system state variables. The growing size and complexity of the smart grid have made state estimation a computationally expensive slow process and Traditional state estimation resulting in a slow calculation by iteration are not efficient. Also, Bad Data Detection (BDD) procedure is also included in the process of state estimation in order to identify cyber-attacks and protect the system. However, existing BDD mechanisms are not capable of detecting new types of attacks, which are injected in carefully planned efforts. The application of machine learning techniques in state estimation is a promising solution to deal with these issues. Machine learning improves the performance of state estimation and it is a good alternative for BDD techniques.

ACS Style

Shahrzad Hadayeghparast; Hadis Karimipour. Application of Machine Learning in State Estimation of Smart Cyber-Physical Grid. Security of Cyber-Physical Systems 2020, 169 -194.

AMA Style

Shahrzad Hadayeghparast, Hadis Karimipour. Application of Machine Learning in State Estimation of Smart Cyber-Physical Grid. Security of Cyber-Physical Systems. 2020; ():169-194.

Chicago/Turabian Style

Shahrzad Hadayeghparast; Hadis Karimipour. 2020. "Application of Machine Learning in State Estimation of Smart Cyber-Physical Grid." Security of Cyber-Physical Systems , no. : 169-194.

Chapter
Published: 24 July 2020 in Security of Cyber-Physical Systems
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Much of the recent innovation and development in technology is geared towards the integration of communication networks among systems and devices. Various applications of technology are witnessing a shift to internet-linked components and integrating cyber and physical systems together; such phenomenon is often referred to as Cyber Physical Systems (CPS). CPS is used in many applications including industrial control systems and critical infrastructure such as health-care and power generation. The increased integration of CPS and internet networks raises security concerns and vulnerabilities. This book delves into some of the security challenges associated with CPS as well as intelligent methods used to secure CPS in various applications. The book also discusses various AI-based methods for enhanced CPS security and performance and presents case studies and proof of concepts in simulated environments.

ACS Style

Jacob Sakhnini; Hadis Karimipour. AI and Security of Cyber Physical Systems: Opportunities and Challenges. Security of Cyber-Physical Systems 2020, 1 -4.

AMA Style

Jacob Sakhnini, Hadis Karimipour. AI and Security of Cyber Physical Systems: Opportunities and Challenges. Security of Cyber-Physical Systems. 2020; ():1-4.

Chicago/Turabian Style

Jacob Sakhnini; Hadis Karimipour. 2020. "AI and Security of Cyber Physical Systems: Opportunities and Challenges." Security of Cyber-Physical Systems , no. : 1-4.

Journal article
Published: 24 June 2020 in Computers & Electrical Engineering
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Wireless communication on the Internet of Things (IoT) requires context-aware data transmission protocols. Developing an energy-efficient clustering mechanism is the primary challenge in data transmission over IoT. The existing approaches struggle with the short lifetime of IoT, imbalance load distribution, and high transmission delay. This paper proposes a novel cluster-head selection and clustering mechanism on IoT. It is composed of two main phases. The first phase selects the near-optimal cluster-heads using Artificial Bee Colony (ABC) algorithm. Performance criteria include the residual energy of the devices, the number of neighbors, Euclidean distance between devices and the sink, and Euclidean distance between each device and its neighbors. The principal objective of the second phase is to group devices into some clusters based on Euclidean distance between each cluster-head and its members, and the data volume generated by clusters. Simulation results verify that our mechanism improves energy consumption, lifetime, and transmission delay.

ACS Style

Shamim Yousefi; Farnaz Derakhshan; Hadi S. Aghdasi; Hadis Karimipour. An energy-efficient artificial bee colony-based clustering in the internet of things. Computers & Electrical Engineering 2020, 86, 106733 .

AMA Style

Shamim Yousefi, Farnaz Derakhshan, Hadi S. Aghdasi, Hadis Karimipour. An energy-efficient artificial bee colony-based clustering in the internet of things. Computers & Electrical Engineering. 2020; 86 ():106733.

Chicago/Turabian Style

Shamim Yousefi; Farnaz Derakhshan; Hadi S. Aghdasi; Hadis Karimipour. 2020. "An energy-efficient artificial bee colony-based clustering in the internet of things." Computers & Electrical Engineering 86, no. : 106733.

Article
Published: 27 May 2020 in IET Smart Grid
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The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control. Smart power grids with the integration of information and communication technologies are one of the most important types of CPS. Proper monitoring and control of the smart grid are highly dependent on the transient stability assessment (TSA). Effective TSA can provide system operators with insightful information on stability statuses and causes under various contingencies and cyber-attacks. In this study, a real-time stability condition predictor based on a feedforward neural network is proposed. The conjugate gradient backpropagation algorithm and Fletcher–Reeves updates are used for training, and the Kohonen learning algorithm is utilised to improve the learning process. By real-time assessment of the network features based on the minimum redundancy maximum relevancy algorithm, the proposed method can successfully predict transient stability and out of step conditions for the network and generators, respectively. Simulation results on the IEEE 39-bus test system indicate the superiority of the proposed method in terms of accuracy, precision, false positive rate, and true positive rate.

ACS Style

Farzad Darbandi; Amirreza Jafari; Hadis Karimipour; Ali Dehghantanha; Farnaz Derakhshan; Kim‐Kwang Raymond Choo. Real‐time stability assessment in smart cyber‐physical grids: a deep learning approach. IET Smart Grid 2020, 3, 454 -461.

AMA Style

Farzad Darbandi, Amirreza Jafari, Hadis Karimipour, Ali Dehghantanha, Farnaz Derakhshan, Kim‐Kwang Raymond Choo. Real‐time stability assessment in smart cyber‐physical grids: a deep learning approach. IET Smart Grid. 2020; 3 (4):454-461.

Chicago/Turabian Style

Farzad Darbandi; Amirreza Jafari; Hadis Karimipour; Ali Dehghantanha; Farnaz Derakhshan; Kim‐Kwang Raymond Choo. 2020. "Real‐time stability assessment in smart cyber‐physical grids: a deep learning approach." IET Smart Grid 3, no. 4: 454-461.

Journal article
Published: 04 May 2020 in IEEE Access
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ACS Style

Abdulrahman Al-Abassi; Hadis Karimipour; Ali Dehghantanha; Reza M. Parizi. An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System. IEEE Access 2020, 8, 83965 -83973.

AMA Style

Abdulrahman Al-Abassi, Hadis Karimipour, Ali Dehghantanha, Reza M. Parizi. An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System. IEEE Access. 2020; 8 ():83965-83973.

Chicago/Turabian Style

Abdulrahman Al-Abassi; Hadis Karimipour; Ali Dehghantanha; Reza M. Parizi. 2020. "An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System." IEEE Access 8, no. : 83965-83973.

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Published: 19 March 2020 in Handbook of Big Data Privacy
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Nowadays, the efficiency of Machine Learning (ML) mechanisms in the Internet of Things (IoT) prompts the researchers and developers to use these emerging technology in different academic and real-world applications. IoT systems could be integrated with the ML-based approaches to map the real-world challenges into the artificial intelligence world. Machine learning mechanisms have been applied to several types of IoT applications, including data analysis, wireless communication, healthcare systems, industrial systems, and security. However, the extensive use of ML-based approaches on the internet of things has posed different challenges on systems, including lack of standard datasets, trust, and resource limitation. In this chapter, we review recent ML-based approaches on IoT systems, in which a set of common issues and challenges are discussed. Our review might provide new research directions about machine learning mechanisms on the internet of things for interested researchers and developers.

ACS Style

Shamim Yousefi; Farnaz Derakhshan; Hadis Karimipour. Applications of Big Data Analytics and Machine Learning in the Internet of Things. Handbook of Big Data Privacy 2020, 77 -108.

AMA Style

Shamim Yousefi, Farnaz Derakhshan, Hadis Karimipour. Applications of Big Data Analytics and Machine Learning in the Internet of Things. Handbook of Big Data Privacy. 2020; ():77-108.

Chicago/Turabian Style

Shamim Yousefi; Farnaz Derakhshan; Hadis Karimipour. 2020. "Applications of Big Data Analytics and Machine Learning in the Internet of Things." Handbook of Big Data Privacy , no. : 77-108.

Chapter
Published: 19 March 2020 in Handbook of Big Data Privacy
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Smart grid is a combination of traditional power grid and many systems and networks including a variety of energy and operational measures such as smart meters, smart supplies, renewable energy sources and efficient energy sources. All these facilities and systems in smart grid, are integrated to provide a supervised and two-way commutation network for the grid. This new infrastructure of developing power distribution grid, utilizes a digital collection system called smart grid. Due to the rapid development and the sensitive data transmitted in this grid, different security challenges arise. Among them are privacy of smart meters, secure data transmission and a variety of attacks which may threaten network security. Given the security challenges in the smart grid, there are a variety of attacks e.g. denial of service (DoS), man in the middle (MITM), replay attack and spoofing that can affect the integrity of network data and the authentication of network devices and users. Further, there are other threats such as various viruses and attacks which may compromise the security and confidentiality of network data. In this paper, we have proposed a robust ECC-based mutual authentication and key exchange scheme to generate a separate session key for each session in communication. The aim of the proposed scheme is to enable smart grid entities to establish a low-cost and secure 2-step handshake communication. Moreover, it supports a robust mutual authentication mechanism. It consists of two steps, registration process and key agreement.

ACS Style

Mostafa Farhdi Moghadam; Amirhossein Mohajerzdeh; Hadis Karimipour; Hamid Chitsaz; Roya Karimi; Behzad Molavi. A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid. Handbook of Big Data Privacy 2020, 63 -76.

AMA Style

Mostafa Farhdi Moghadam, Amirhossein Mohajerzdeh, Hadis Karimipour, Hamid Chitsaz, Roya Karimi, Behzad Molavi. A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid. Handbook of Big Data Privacy. 2020; ():63-76.

Chicago/Turabian Style

Mostafa Farhdi Moghadam; Amirhossein Mohajerzdeh; Hadis Karimipour; Hamid Chitsaz; Roya Karimi; Behzad Molavi. 2020. "A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid." Handbook of Big Data Privacy , no. : 63-76.

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Published: 19 March 2020 in Handbook of Big Data Privacy
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The development of AI has made some major advances in recent years and its potential appears to be promising. In the healthcare sector, scientific competitions like ImageNet Large Scale Visual Recognition Challenges are providing evidence that computers can achieve human-like competence in image recognition. There are numerous computer models in medical diagnosis to help physicians. Among different models, deep learning algorithms, in particular convolutional neural networks are among the first choices for medical images analysis. This paper use one of the largest dataset of open-source musculoskeletal radiographs (MURA) for abnormality detection of thousands of musculoskeletal radiographs based on the deep learning to build models for detecting and localizing the abnormalities.

ACS Style

Wenyu Han; Amin Azmoodeh; Hadis Karimipour; Simon Yang. Privacy Preserving Abnormality Detection: A Deep Learning Approach. Handbook of Big Data Privacy 2020, 285 -303.

AMA Style

Wenyu Han, Amin Azmoodeh, Hadis Karimipour, Simon Yang. Privacy Preserving Abnormality Detection: A Deep Learning Approach. Handbook of Big Data Privacy. 2020; ():285-303.

Chicago/Turabian Style

Wenyu Han; Amin Azmoodeh; Hadis Karimipour; Simon Yang. 2020. "Privacy Preserving Abnormality Detection: A Deep Learning Approach." Handbook of Big Data Privacy , no. : 285-303.