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The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed “EdgeSDN-I4COVID” architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.
Anichur Rahman; Chinmay Chakraborty; Adnan Anwar; Razaul Karim; Jahidul Islam; Dipanjali Kundu; Ziaur Rahman; Shahab S. Band. SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Cluster Computing 2021, 1 -18.
AMA StyleAnichur Rahman, Chinmay Chakraborty, Adnan Anwar, Razaul Karim, Jahidul Islam, Dipanjali Kundu, Ziaur Rahman, Shahab S. Band. SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Cluster Computing. 2021; ():1-18.
Chicago/Turabian StyleAnichur Rahman; Chinmay Chakraborty; Adnan Anwar; Razaul Karim; Jahidul Islam; Dipanjali Kundu; Ziaur Rahman; Shahab S. Band. 2021. "SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic." Cluster Computing , no. : 1-18.
With the rapid evolution and proliferation of Artificial Intelligence (AI) throughout the past decade, industry-wide Deep Learning (DL) applications within IIoT ecosystems are expected to rise exponentially within this coming decade. However, recent studies have highlighted the extreme sensitivity and vulnerability of modern Deep Neural Networks (DNNs) to adversarial examples. While being imperceptible and benign to the naked human eye, those adversarial perturbations crafted from one-pixel attacks can drastically impact the accuracy of DNNs. The consequences of those adversarial attacks in critical applications such as healthcare and autonomous vehicles can prove disastrous. Therefore, the urge to ensure the robustness and resilience of DL systems against one-pixel attacks has recently started attracting stupendous attention from both academicians and industry professionals. Throughout this paper, we first develop a one-pixel threat model within an IIoT ecosystem and propose a novel image recovery defense mechanism to detect and mitigate one-pixel attacks based on Accelerated Proximal Gradient approach. Experimental results proved that our proposed solution is highly effective and efficient in detecting and mitigating one-pixel attacks in state-of-the-art neural networks, more specifically LeNet and ResNet, using the CIFAR10 and MNIST datasets.
Muhammad Akbar Husnoo; Adnan Anwar. Do not get fooled: Defense against the one-pixel attack to protect IoT-enabled Deep Learning systems. Ad Hoc Networks 2021, 122, 102627 .
AMA StyleMuhammad Akbar Husnoo, Adnan Anwar. Do not get fooled: Defense against the one-pixel attack to protect IoT-enabled Deep Learning systems. Ad Hoc Networks. 2021; 122 ():102627.
Chicago/Turabian StyleMuhammad Akbar Husnoo; Adnan Anwar. 2021. "Do not get fooled: Defense against the one-pixel attack to protect IoT-enabled Deep Learning systems." Ad Hoc Networks 122, no. : 102627.
Smart meters have ensured effective end-user energy consumption data management and helping the power companies towards network operation efficiency. However, recent studies highlighted that cyber adversaries may launch attacks on smart meters that can cause data availability, integrity, and confidentiality issues both at the consumer side or at a network operator’s end. Therefore, research on smart meter data security has been attributed as one of the top priorities to ensure the safety and reliability of the critical energy system infrastructure. Authentication is one of the basic building blocks of any secure system. Numerous authentication schemes have been proposed for the smart grid, but most of these methods are applicable for two party communication. In this article, we propose a distributed, dynamic multistage authenticated key agreement scheme for smart meter communication. The proposed scheme provides secure authentication between smart meter, NAN gateway, and SCADA energy center in a distributed manner. Through rigorous cryptanalysis we have proved that the proposed scheme resist replay attack, insider attack, impersonation attack and man-in-the-middle attack. Also, it provides perfect forward secrecy, device anonymity and data confidentiality. The proposed scheme security is formally proved in the CK—model and, using BAN logic, it is proved that the scheme creates a secure session between the communication participants. The proposed scheme is simulated using the AVISPA tool and verified the safety against all active attacks. Further, efficiency analysis of the scheme has been made by considering its computation, communication, and functional costs. The computed results are compared with other related schemes. From these analysis results, it is proved that the proposed scheme is robust and secure when compared to other schemes.
Manjunath Hegde; Adnan Anwar; Karunakar Kotegar; Zubair Baig; Robin Doss. A novel multi-stage distributed authentication scheme for smart meter communication. 2021, 7, 1 .
AMA StyleManjunath Hegde, Adnan Anwar, Karunakar Kotegar, Zubair Baig, Robin Doss. A novel multi-stage distributed authentication scheme for smart meter communication. . 2021; 7 ():1.
Chicago/Turabian StyleManjunath Hegde; Adnan Anwar; Karunakar Kotegar; Zubair Baig; Robin Doss. 2021. "A novel multi-stage distributed authentication scheme for smart meter communication." 7, no. : 1.
The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.
Sk. Mehedi; Adnan Anwar; Ziaur Rahman; Kawsar Ahmed. Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks. Sensors 2021, 21, 4736 .
AMA StyleSk. Mehedi, Adnan Anwar, Ziaur Rahman, Kawsar Ahmed. Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks. Sensors. 2021; 21 (14):4736.
Chicago/Turabian StyleSk. Mehedi; Adnan Anwar; Ziaur Rahman; Kawsar Ahmed. 2021. "Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks." Sensors 21, no. 14: 4736.
Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.
Mustain Billah; Adnan Anwar; Ziaur Rahman; Syed Galib. Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes. Energies 2021, 14, 3887 .
AMA StyleMustain Billah, Adnan Anwar, Ziaur Rahman, Syed Galib. Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes. Energies. 2021; 14 (13):3887.
Chicago/Turabian StyleMustain Billah; Adnan Anwar; Ziaur Rahman; Syed Galib. 2021. "Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes." Energies 14, no. 13: 3887.
Continuous Authentication (CA) has been proposed as a potential solution to counter complex cybersecurity threats posed against conventional static authentication mechanisms that merely authenticate at ingress points of a platform. However, widely researched CA mechanisms that rely on user’s behavioural characteristics cannot be extended to continuously authenticate Internet of Things (IoT) devices. Challenges are exacerbated with the increased adoption of device-to-device (d2d) communication in critical infrastructures. Existing d2d authentication protocols proposed in the literature are either prone to subversion or are computationally infeasible to be deployed on constrained IoT devices. In view of these challenges, we propose a novel, Lightweight Continuous Device-to-Device Authentication (LCDA) protocol that leverages communication channel properties and a tunable mathematical function to generate dynamically changing session keys for continuous device authentication. Our extensive informal and formal analysis confirms the efficacy of the proposed LCDA protocol in terms of its resilience to known attack vectors, thereby demonstrating its strong potential for deployment in critical and resource-constrained scenarios for secure d2d communication.
Syed W. Shah; Naeem F. Syed; Arash Shaghaghi; Adnan Anwar; Zubair Baig; Robin Doss. LCDA: Lightweight Continuous Device-to-Device Authentication for a Zero Trust Architecture (ZTA). Computers & Security 2021, 108, 102351 .
AMA StyleSyed W. Shah, Naeem F. Syed, Arash Shaghaghi, Adnan Anwar, Zubair Baig, Robin Doss. LCDA: Lightweight Continuous Device-to-Device Authentication for a Zero Trust Architecture (ZTA). Computers & Security. 2021; 108 ():102351.
Chicago/Turabian StyleSyed W. Shah; Naeem F. Syed; Arash Shaghaghi; Adnan Anwar; Zubair Baig; Robin Doss. 2021. "LCDA: Lightweight Continuous Device-to-Device Authentication for a Zero Trust Architecture (ZTA)." Computers & Security 108, no. : 102351.
Industry 4.0 is mainly recognized as the digital transformation of the industrial sector which is driven through machine learning and artificial intelligence. It includes the historical collection of information, the capture of live data via sensors, data aggregation and connectivity between routing, gateways and other protocols, PLC integration, the dashboard for analysis and monitoring. The convergence of Machine learning (ML) and Artificial Intelligence (AI) has overcome data integration and decision-making challenges with the adoption of Industry 4.0. This article justifies the context and relevance of data sharing in the industrial sectors and the cyber threats in industry 4.0 and also provides the preventive techniques used via AI and ML. In addition, this book chapter illustrates real use cases and potential prospects for both technologies.
Sourabh Kumar Vishavnath; Adnan Anwar; Mohiuddin Ahmed. Machine Learning Based Cybersecurity Defense at the Age of Industry 4.0. Econometrics for Financial Applications 2021, 355 -368.
AMA StyleSourabh Kumar Vishavnath, Adnan Anwar, Mohiuddin Ahmed. Machine Learning Based Cybersecurity Defense at the Age of Industry 4.0. Econometrics for Financial Applications. 2021; ():355-368.
Chicago/Turabian StyleSourabh Kumar Vishavnath; Adnan Anwar; Mohiuddin Ahmed. 2021. "Machine Learning Based Cybersecurity Defense at the Age of Industry 4.0." Econometrics for Financial Applications , no. : 355-368.
Autism Spectrum Disorder (ASD), which is a neuro development disorder, is often accompanied by sensory issues such an over sensitivity or under sensitivity to sounds and smells or touch. Although its main cause is genetics in nature, early detection and treatment can help to improve the conditions. In recent years, machine learning based intelligent diagnosis has been evolved to complement the traditional clinical methods which can be time consuming and expensive. The focus of this paper is to find out the most significant traits and automate the diagnosis process using available classification techniques for improved diagnosis purpose. We have analyzed ASD datasets of toddler, child, adolescent and adult. We have evaluated state-of-the-art classification and feature selection techniques to determine the best performing classifier and feature set, respectively, for these four ASD datasets. Our experimental results show that multilayer perceptron (MLP) classifier outperforms among all other benchmark classification techniques and achieves 100% accuracy with minimal number of attributes for toddler, child, adolescent and adult datasets. We also identify that ‘relief F’ feature selection technique works best for all four ASD datasets to rank the most significant attributes.
Delowar Hossain; Muhammad Ashad Kabir; Adnan Anwar; Zahidul Islam. Detecting autism spectrum disorder using machine learning techniques. Health Information Science and Systems 2021, 9, 1 -13.
AMA StyleDelowar Hossain, Muhammad Ashad Kabir, Adnan Anwar, Zahidul Islam. Detecting autism spectrum disorder using machine learning techniques. Health Information Science and Systems. 2021; 9 (1):1-13.
Chicago/Turabian StyleDelowar Hossain; Muhammad Ashad Kabir; Adnan Anwar; Zahidul Islam. 2021. "Detecting autism spectrum disorder using machine learning techniques." Health Information Science and Systems 9, no. 1: 1-13.
Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).
B. M. Ruhul Amin; M. J. Hossain; Adnan Anwar; Shafquat Zaman. Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems. Electronics 2021, 10, 650 .
AMA StyleB. M. Ruhul Amin, M. J. Hossain, Adnan Anwar, Shafquat Zaman. Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems. Electronics. 2021; 10 (6):650.
Chicago/Turabian StyleB. M. Ruhul Amin; M. J. Hossain; Adnan Anwar; Shafquat Zaman. 2021. "Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems." Electronics 10, no. 6: 650.
IEC 61850 is one of the most prominent communication standards adopted by the smart grid community due to its high scalability, multi-vendor interoperability, and support for several input/output devices. Generic Object-Oriented Substation Events (GOOSE), which is a widely used communication protocol defined in IEC 61850, provides reliable and fast transmission of events for the electrical substation system. This paper investigates the security vulnerabilities of this protocol and analyzes the potential impact on the smart grid by rigorously analyzing the security of the GOOSE protocol using an automated process and identifying vulnerabilities in the context of smart grid communication. The vulnerabilities are tested using a real-time simulation and industry standard hardware-in-the-loop emulation. An in-depth experimental analysis is performed to demonstrate and verify the security weakness of the GOOSE publish-subscribe protocol towards the substation protection within the smart grid setup. It is observed that an adversary who might have familiarity with the substation network architecture can create falsified attack scenarios that can affect the physical operation of the power system. Extensive experiments using the real-time testbed validate the theoretical analysis, and the obtained experimental results prove that the GOOSE-based IEC 61850 compliant substation system is vulnerable to attacks from malicious intruders.
Haftu Reda; Biplob Ray; Pejman Peidaee; Adnan Anwar; Abdun Mahmood; Akhtar Kalam; Nahina Islam. Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid. Sensors 2021, 21, 1554 .
AMA StyleHaftu Reda, Biplob Ray, Pejman Peidaee, Adnan Anwar, Abdun Mahmood, Akhtar Kalam, Nahina Islam. Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid. Sensors. 2021; 21 (4):1554.
Chicago/Turabian StyleHaftu Reda; Biplob Ray; Pejman Peidaee; Adnan Anwar; Abdun Mahmood; Akhtar Kalam; Nahina Islam. 2021. "Vulnerability and Impact Analysis of the IEC 61850 GOOSE Protocol in the Smart Grid." Sensors 21, no. 4: 1554.
The efficiency of cooperative communication protocols to increase the reliability and range of transmission for Vehicular Ad hoc Network (VANET) is proven, but identity verification and communication security are required to be ensured. Though it is difficult to maintain strong network connections between vehicles because of there high mobility, with the help of cooperative communication, it is possible to increase the communication efficiency, minimise delay, packet loss, and Packet Dropping Rate (PDR). However, cooperating with unknown or unauthorized vehicles could result in information theft, privacy leakage, vulnerable to different security attacks, etc. In this paper, a blockchain based secure and privacy preserving authentication protocol is proposed for the Internet of Vehicles (IoV). Blockchain is utilized to store and manage the authentication information in a distributed and decentralized environment and developed on the Ethereum platform that uses a digital signature algorithm to ensure confidentiality, non-repudiation, integrity, and preserving the privacy of the IoVs. For optimized communication, transmitted services are categorized into emergency and optional services. Similarly, to optimize the performance of the authentication process, IoVs are categorized as emergency and general IoVs. The proposed cooperative protocol is validated by numerical analyses which show that the protocol successfully increases the system throughput and decreases PDR and delay. On the other hand, the authentication protocol requires minimum storage as well as generates low computational overhead that is suitable for the IoVs with limited computer resources.
A. Akhter; Mohiuddin Ahmed; A. Shah; Adnan Anwar; A. Kayes; Ahmet Zengin. A Blockchain-Based Authentication Protocol for Cooperative Vehicular Ad Hoc Network. Sensors 2021, 21, 1273 .
AMA StyleA. Akhter, Mohiuddin Ahmed, A. Shah, Adnan Anwar, A. Kayes, Ahmet Zengin. A Blockchain-Based Authentication Protocol for Cooperative Vehicular Ad Hoc Network. Sensors. 2021; 21 (4):1273.
Chicago/Turabian StyleA. Akhter; Mohiuddin Ahmed; A. Shah; Adnan Anwar; A. Kayes; Ahmet Zengin. 2021. "A Blockchain-Based Authentication Protocol for Cooperative Vehicular Ad Hoc Network." Sensors 21, no. 4: 1273.
Existing research shows that Cluster-based Medium Access Control (CB-MAC) protocols perform well in controlling and managing Vehicular Ad hoc Network (VANET), but requires ensuring improved security and privacy preserving authentication mechanism. To this end, we propose a multi-level blockchain-based privacy-preserving authentication protocol. The paper thoroughly explains the formation of the authentication centers, vehicles registration, and key generation processes. In the proposed architecture, a global authentication center (GAC) is responsible for storing all vehicle information, while Local Authentication Center (LAC) maintains a blockchain to enable quick handover between internal clusters of vehicle. We also propose a modified control packet format of IEEE 802.11 standards to remove the shortcomings of the traditional MAC protocols. Moreover, cluster formation, membership and cluster-head selection, and merging and leaving processes are implemented while considering the safety and non-safety message transmission to increase the performance. All blockchain communication is performed using high speed 5G internet while encrypted information is transmitted while using the RSA-1024 digital signature algorithm for improved security, integrity, and confidentiality. Our proof-of-concept implements the authentication schema while considering multiple virtual machines. With detailed experiments, we show that the proposed method is more efficient in terms of time and storage when compared to the existing methods. Besides, numerical analysis shows that the proposed transmission protocols outperform traditional MAC and benchmark methods in terms of throughput, delay, and packet dropping rate.
A. Akhter; Mohiuddin Ahmed; A. Shah; Adnan Anwar; Ahmet Zengin. A Secured Privacy-Preserving Multi-Level Blockchain Framework for Cluster Based VANET. Sustainability 2021, 13, 400 .
AMA StyleA. Akhter, Mohiuddin Ahmed, A. Shah, Adnan Anwar, Ahmet Zengin. A Secured Privacy-Preserving Multi-Level Blockchain Framework for Cluster Based VANET. Sustainability. 2021; 13 (1):400.
Chicago/Turabian StyleA. Akhter; Mohiuddin Ahmed; A. Shah; Adnan Anwar; Ahmet Zengin. 2021. "A Secured Privacy-Preserving Multi-Level Blockchain Framework for Cluster Based VANET." Sustainability 13, no. 1: 400.
Coronavirus disease 2019 (COVID-19) has significantly impacted the entire world today and stalled off regular human activities in such an unprecedented way that it will have an unforgettable footprint on the history of mankind. Different countries have adopted numerous measures to build resilience against this life-threatening disease. However, the highly contagious nature of this pandemic has challenged the traditional healthcare and treatment practices. Thus, artificial intelligence (AI) and machine learning (ML) open up new mechanisms for effective healthcare during this pandemic. AI and ML can be useful for medicine development, designing efficient diagnosis strategies and producing predictions of the disease spread. These applications are highly dependent on real-time monitoring of the patients and effective coordination of the information, where the Internet of Things (IoT) plays a key role. IoT can also help with applications such as automated drug delivery, responding to patient queries, and tracking the causes of disease spread. This paper represents a comprehensive analysis of the potential AI, ML, and IoT technologies for defending against the COVID-19 pandemic. The existing and potential applications of AI, ML, and IoT, along with a detailed analysis of the enabling tools and techniques are outlined. A critical discussion on the risks and limitations of the aforementioned technologies are also included.
S. M. Abu Adnan Adnan Abir; Shama Naz Islam; Adnan Anwar; Abdun Naser Mahmood; Aman Maung Than Oo. Building Resilience against COVID-19 Pandemic using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress. IoT 2020, 1, 506 -528.
AMA StyleS. M. Abu Adnan Adnan Abir, Shama Naz Islam, Adnan Anwar, Abdun Naser Mahmood, Aman Maung Than Oo. Building Resilience against COVID-19 Pandemic using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress. IoT. 2020; 1 (2):506-528.
Chicago/Turabian StyleS. M. Abu Adnan Adnan Abir; Shama Naz Islam; Adnan Anwar; Abdun Naser Mahmood; Aman Maung Than Oo. 2020. "Building Resilience against COVID-19 Pandemic using Artificial Intelligence, Machine Learning, and IoT: A Survey of Recent Progress." IoT 1, no. 2: 506-528.
Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined Network (SDN), Network Function Virtualization (NFV) and Blockchain (BC). These technologies provide a robust, and secured platform to meet all kinds of challenges, such as safety, confidentiality, flexibility, efficiency, and availability. This work suggests a distributed, scalable IoT-SDN with Blockchain-based NFV framework for a smart condominium (DistB-Condo) that can act as an efficient secured platform for a small community. Moreover, the Blockchain-based IoT-SDN with NFV framework provides the combined benefits of leading technologies. It also presents an optimized Cluster Head Selection (CHS) algorithm for selecting a Cluster Head (CH) among the clusters that efficiently saves energy. Besides, a decentralized and secured Blockchain approach has been introduced that allows more prominent security and privacy to the desired condominium network. Our proposed approach has also the ability to detect attacks in an IoT environment. Eventually, this article evaluates the performance of the proposed architecture using different parameters (e.g., throughput, packet arrival rate, and response time). The proposed approach outperforms the existing OF-Based SDN. DistB-Condo has better throughput on average, and the bandwidth (Mbps) much higher than the OF-Based SDN approach in the presence of attacks. Also, the proposed model has an average response time of 5% less than the core model.
Anichur Rahman; Jahidul Islam; Ziaur Rahman; Mahfuz Reza; Adnan Anwar; M. A. Parvez Mahmud; Mostofa Kamal Nasir; Rafidah Md. Noor. DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium. IEEE Access 2020, 8, 209594 -209609.
AMA StyleAnichur Rahman, Jahidul Islam, Ziaur Rahman, Mahfuz Reza, Adnan Anwar, M. A. Parvez Mahmud, Mostofa Kamal Nasir, Rafidah Md. Noor. DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium. IEEE Access. 2020; 8 (99):209594-209609.
Chicago/Turabian StyleAnichur Rahman; Jahidul Islam; Ziaur Rahman; Mahfuz Reza; Adnan Anwar; M. A. Parvez Mahmud; Mostofa Kamal Nasir; Rafidah Md. Noor. 2020. "DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium." IEEE Access 8, no. 99: 209594-209609.
Several approaches and tools have been developed to analyse and detect the presence of malicious content within the PDF; however, the fundamental approach in designing the existing tools and techniques has not been entirely considerate. Existing tools are based on the available datasets and the observation made during the maldoc manual analysis, making them susceptible to various types of attacks such as Mimicry and Parser confusion. We aim to enhance PDF maldoc classification by identifying the most conclusive feature-set required for accurately classifying PDF maldocs. We extract features using two popular PDF analysis tools and derive a set of features backed by data that further complements classification. We subsequently evaluate all features through a wrapper function. The features with the highest importance values are used to construct a classifier that outperforms the baseline models in terms of classification accuracy and efficiency. Our proposed method helps us identify a useful set of tool-independent features that prolong the current tools’ lifespan and usability. It provides us with an in-depth understanding of how these chosen features cumulatively impact the classification. In addition, we evaluate our findings using real-world samples from VirusTotal. Using our proposed technique, we managed to decrease the size of the feature-set by more than 60% while increasing the classification accuracy by around 2%.
Ahmed Falah; Lei Pan; Shamsul Huda; Shiva Raj Pokhrel; Adnan Anwar. Improving malicious PDF classifier with feature engineering: A data-driven approach. Future Generation Computer Systems 2020, 115, 314 -326.
AMA StyleAhmed Falah, Lei Pan, Shamsul Huda, Shiva Raj Pokhrel, Adnan Anwar. Improving malicious PDF classifier with feature engineering: A data-driven approach. Future Generation Computer Systems. 2020; 115 ():314-326.
Chicago/Turabian StyleAhmed Falah; Lei Pan; Shamsul Huda; Shiva Raj Pokhrel; Adnan Anwar. 2020. "Improving malicious PDF classifier with feature engineering: A data-driven approach." Future Generation Computer Systems 115, no. : 314-326.
The smart grid system is one of the key infrastructures required to sustain our future society. It is a complex system that comprises two independent parts: power grids and communication networks. There have been several cyber attacks on smart grid systems in recent years that have caused significant consequences. Therefore, cybersecurity training specific to the smart grid system is essential in order to handle these security issues adequately. Unfortunately, concepts related to automation, ICT, smart grids, and other physical sectors are typically not covered by conventional training and education methods. These cybersecurity experiences can be achieved by conducting training using a smart grid co-simulation, which is the integration of at least two simulation models. However, there has been little effort to research attack simulation tools for smart grids. In this research, we first review the existing research in the field, and then propose a smart grid attack co-simulation framework called GridAttackSim based on the combination of GridLAB-D, ns-3, and FNCS. The proposed architecture allows us to simulate smart grid infrastructure features with various cybersecurity attacks and then visualize their consequences automatically. Furthermore, the simulator not only features a set of built-in attack profiles but also enables scientists and electric utilities interested in improving smart grid security to design new ones. Case studies were conducted to validate the key functionalities of the proposed framework. The simulation results are supported by relevant works in the field, and the system can potentially be deployed for cybersecurity training and research.
Tan Duy Le; Adnan Anwar; Seng W. Loke; Razvan Beuran; Yasuo Tan. GridAttackSim: A Cyber Attack Simulation Framework for Smart Grids. Electronics 2020, 9, 1218 .
AMA StyleTan Duy Le, Adnan Anwar, Seng W. Loke, Razvan Beuran, Yasuo Tan. GridAttackSim: A Cyber Attack Simulation Framework for Smart Grids. Electronics. 2020; 9 (8):1218.
Chicago/Turabian StyleTan Duy Le; Adnan Anwar; Seng W. Loke; Razvan Beuran; Yasuo Tan. 2020. "GridAttackSim: A Cyber Attack Simulation Framework for Smart Grids." Electronics 9, no. 8: 1218.
The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.
Erwin Adi; Adnan Anwar; Zubair Baig; Sherali Zeadally. Machine learning and data analytics for the IoT. Neural Computing and Applications 2020, 32, 16205 -16233.
AMA StyleErwin Adi, Adnan Anwar, Zubair Baig, Sherali Zeadally. Machine learning and data analytics for the IoT. Neural Computing and Applications. 2020; 32 (20):16205-16233.
Chicago/Turabian StyleErwin Adi; Adnan Anwar; Zubair Baig; Sherali Zeadally. 2020. "Machine learning and data analytics for the IoT." Neural Computing and Applications 32, no. 20: 16205-16233.
Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms.
Adnan Anwar; Abdun Mahmood; Biplob Ray; Apel Mahmud; Zahir Tari. Machine Learning to Ensure Data Integrity in Power System Topological Network Database. Electronics 2020, 9, 693 .
AMA StyleAdnan Anwar, Abdun Mahmood, Biplob Ray, Apel Mahmud, Zahir Tari. Machine Learning to Ensure Data Integrity in Power System Topological Network Database. Electronics. 2020; 9 (4):693.
Chicago/Turabian StyleAdnan Anwar; Abdun Mahmood; Biplob Ray; Apel Mahmud; Zahir Tari. 2020. "Machine Learning to Ensure Data Integrity in Power System Topological Network Database." Electronics 9, no. 4: 693.
Concern about cyber-security is growing worldwide with the advancement of smart control and networking systems in the cyber-physical layer of power systems. Detection of the stealthy False Data Injection Attack (FDIA) is getting more complicated when the system's behavior during external disturbances, e.g. faults are considered. In this paper, a machine learning algorithm based approach is proposed to detect and distinguish between stealthy FDIA in the state estimator and faults in power systems. The detection rate and false positive rate obtained by using different state-of-the-art classifiers show that the proposed approach can successfully distinguish between cyber injections and faults in power systems. Cyber injection and faults are introduced in a state estimator model simulated in MATLAB and an open source machine learning tool, WEKA is utilized to distinguish the injection and faults from the developed dataset.
B M Ruhul Amin; Adnan Anwar; M. J. Hossain. Distinguishing Between Cyber Injection and Faults Using Machine Learning Algorithms. 2018 IEEE Region Ten Symposium (Tensymp) 2018, 19 -24.
AMA StyleB M Ruhul Amin, Adnan Anwar, M. J. Hossain. Distinguishing Between Cyber Injection and Faults Using Machine Learning Algorithms. 2018 IEEE Region Ten Symposium (Tensymp). 2018; ():19-24.
Chicago/Turabian StyleB M Ruhul Amin; Adnan Anwar; M. J. Hossain. 2018. "Distinguishing Between Cyber Injection and Faults Using Machine Learning Algorithms." 2018 IEEE Region Ten Symposium (Tensymp) , no. : 19-24.
Recent studies show that smart grid is vulnerable to cyber anomalies. In this paper, an anomaly detection method is proposed to identify the abnormal patterns in the network power flows, which results from the accidental or deliberate changes of the database. The proposed method utilizes a multivariate time series statistical forecasting technique based on vector autoregressive model. To understand the power flow behavior of the system, a multiphase optimal power flow analysis is conducted. The proposed method is validated using IEEE Power Distribution System Analysis Subcommittee recommended 34-node and 123-node test systems. Three different experiments are performed to test the effectiveness of the proposed approach. Vulnerability and computational complexity issues of this paper are also addressed elaborately. Results obtained from this analysis show that the proposed method successfully captures the network anomalies at a high detection rate allowing only a few number of false alarms.
Adnan Anwar; Abdun N. Mahmood; Zahir Tari. Ensuring Data Integrity of OPF Module and Energy Database by Detecting Changes in Power Flow Patterns in Smart Grids. IEEE Transactions on Industrial Informatics 2017, 13, 3299 -3311.
AMA StyleAdnan Anwar, Abdun N. Mahmood, Zahir Tari. Ensuring Data Integrity of OPF Module and Energy Database by Detecting Changes in Power Flow Patterns in Smart Grids. IEEE Transactions on Industrial Informatics. 2017; 13 (6):3299-3311.
Chicago/Turabian StyleAdnan Anwar; Abdun N. Mahmood; Zahir Tari. 2017. "Ensuring Data Integrity of OPF Module and Energy Database by Detecting Changes in Power Flow Patterns in Smart Grids." IEEE Transactions on Industrial Informatics 13, no. 6: 3299-3311.