Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues

Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps

Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey

The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.Comment: The paper is published in Elsevier's Internet of Things journal. 25 pages + 20 pages of reference

Enhancing cybersecurity in smart grids: Deep black box adversarial attacks and quantum voting ensemble models for blockchain privacy-preserving storage

Smart grids are getting important in today’s power management, so with that, smart grid technologies are increasingly important too. There have been a lot of concerns about smart grid technologies being hacked, and as a result, some deep black box adversarial attacks have been conducted and presented. We propose a new experimental methodology for benchmarking smart grid security with black box attacks. Additionally, concerning the type of smart grids, Smart Power Grids, deep black box adversarial attacks which can be crafted using virtually no knowledge about the target due to the inherent complexity of content available in cryptographic libraries like SecLib or Bouncy Castle how it affects security of cyber-physical power systems. We identify potential impacts of deep black box attacks on Smart Power Grids as implemented by the Department of Energy in 1996, we evaluate existing protection methods, and we find out the pitfalls thereof. With the aim of overcoming the aforementioned drawbacks, we initiate a study on deep black box adversarial attacks against Smart Power Grids showing that statistically significant effects against a national Smart Power Grid are achievable with absolute security. We also probe detection of cyber security attacks on Smart Power Grids. We illustrate landscape of smart grids with numerous cyber threats and demonstrate the limitations of traditional security practices. We show the importance of machine learning to detect attacks and the unlikelihood of identification of dependable and efficient detection schemes. We describe quantum voting ensemble models as one of the most powerful techniques in the detection of cyber security attacks. Finally, we propose an experimental setup and evaluation criteria to detect cyber security attacks in smart grids using quantum voting ensemble models. Then, we talk about private data storage in blockchain based smart grid infrastructure. We give an introduction of block chain and its essentiality in smart grids. We discuss privacy issues in block chain based smart grids. We acknowledge the strength of privacy safeguards, but on the same wavelength, we realize their weaknesses. Next, we propose a quantum resistant encryption technique that enhances the privacy of smart grids. We propose quantum voting ensemble models as one of the most promising techniques to address the issue of private data storage in block chains. As a result, we provide a comparison between the proposed models and traditional approaches to privacy protection in smart grids based on an experimental performance review. Then, we propose a unified strategy to improve smart grid cyber security by incorporating deep black box attacks with quantum voting ensemble models. Finally, we disclose several benefits of such integration and perform an experimental evaluation to investigate the effectiveness of the unified approach. The results of our study identify security gaps in smart grids and propose state-of-the-art mechanisms to address them. The challenges of smart grids system require the amalgamation of blockchain, quantum voting ensemble models and deep black box adversarial attacks. We achieve this objective proposing a unified strategy. The results of this study will equally be helpful for future research and smart grid cyber security implementations

Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study

The file attached to this record is the author's final peer reviewed version.In this paper, we present a survey of deep learning approaches for cyber security intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods

Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms

With the use of cloud computing, which provides the infrastructure necessary for the efficient delivery of smart city services to every citizen over the internet, intelligent systems may be readily integrated into smart cities and communicate with one another. Any smart system at home, in a car, or in the workplace can be remotely controlled and directed by the individual at any time. Continuous cloud service availability is becoming a critical subscriber requirement within smart cities. However, these cost-cutting measures and service improvements will make smart city cloud networks more vulnerable and at risk. The primary function of Intrusion Detection Systems (IDS) has gotten increasingly challenging due to the enormous proliferation of data created in cloud networks of smart cities. To alleviate these concerns, we provide a framework for automatic, reliable, and uninterrupted cloud availability of services for the network data security of intelligent connected devices. This framework enables IDS to defend against security threats and to provide services that meet the users' Quality of Service (QoS) expectations. This study's intrusion detection solution for cloud network data from smart cities employed Spark and Waikato Environment for Knowledge Analysis (WEKA). WEKA and Spark are linked and made scalable and distributed. The Hadoop Distributed File System (HDFS) storage advantages are combined with WEKA's Knowledge flow for processing cloud network data for smart cities. Utilizing HDFS components, WEKA's machine learning algorithms receive cloud network data from smart cities. This research utilizes the wrapper-based Feature Selection (FS) approach for IDS, employing both the Pigeon Inspired Optimizer (PIO) and the Particle Swarm Optimization (PSO). For classifying the cloud network traffic of smart cities, the tree-based Stacking Ensemble Method (SEM) of J48, Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) are applied. Performance evaluations of our system were conducted using the UNSW-NB15 and NSL-KDD datasets. Our technique is superior to previous works in terms of sensitivity, specificity, precision, false positive rate (FPR), accuracy, F1 Score, and Matthews correlation coefficient (MCC)

POWER DISTRIBUTION SYSTEM RELIABILITY AND RESILIENCY AGAINST EXTREME EVENTS

The objective of a power system is to provide electricity to its customers as economically as possible with an acceptable level of reliability while safeguarding the environment. Power system reliability has well-established quantitative metrics, regulatory standards, compliance incentives and jurisdictions of responsibilities. The increase in occurrence of extreme events like hurricane/tornadoes, floods, wildfires, storms, cyber-attacks etc. which are not considered in routine reliability evaluation has raised concern over the potential economic losses due to prolonged and large-scale power outages, and the overall sustainability and adaptability of power systems. This concern has motivated the utility planners, operators, and policy makers to acknowledge the importance of system resiliency against such events. However, power system resiliency evaluation is comparatively new, and lacks widely accepted standards, assessment methods and metrics. The thesis presents comparative review and analysis of power system resilience models, methodologies, and metrics in present literature and utility applications. It presents studies on two very different types of extreme events, (i) man-made and (ii) natural disaster, and analyzes their impacts on the resiliency of a distribution system. It draws conclusions on assessing and improving power system resiliency based on the impact of the extreme event, response from the distribution system, and effectiveness of the mitigating measures to tackle the extreme event. The advancement in technologies has seen an increasing integration of cyber and physical layer of the distribution system. The distribution system operators avails from the symbiotic relation of the cyber-physical layer, but the interdependency has also been its Achilles heel. The evolving infrastructure is being exposed to increase in cyber-attacks. It is of paramount importance to address the aforementioned issue by developing holistic approaches to comprehensibly upgrade the distribution system preventing huge financial loss and societal repercussions. The thesis models a type of cyber-attack using false data injection and evaluates its impact on the distribution system. It does so by developing a resilience assessment methodology accompanied by quantitative metrics. It also performs reliability evaluation to present the underlying principle and differences between reliability and resiliency. The thesis also introduces new indices to demonstrate the effectiveness of a bad-data detection strategy against such cyber-attacks. Extreme events like hurricane/tornadoes, floods, wildfires, storm, cyber-attack etc. are responsible for catastrophic damage to critical infrastructure and huge financial loss. Power distribution system is an important critical infrastructure driving the socio-economic growth of the country. High winds are one of the most common form of extreme events that are responsible for outages due to failure of poles, equipment damage etc. The thesis models effective extreme wind events with the help of fragility curves, and presents an analysis of their impacts on the distribution system. It also presents infrastructural and operational resiliency enhancement strategies and quantifies the effectiveness of the strategy with the metrics developed. It also demonstrates the dependency of resiliency of distribution system on the structural strength of transmission lines and presents measures to ensure the independency of the distribution system. The thesis presents effective resilience assessment methodology that can be valuable for distribution system utility planners, and operators to plan and ensure a resilient distribution system