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\"This book addresses the need for literature on modeling and simulation techniques for distributed systems. For simulation modeling of distributed systems in the book, a specific class of extended Petri nets is used that allows to easily represent the fundamental processes of any distributed system. The book is intended, first of all, as a text for related graduate-level university courses on distributed systems in computer science and computer engineering. ... Containing a large number of models, with commented source texts and simulation results on the attached CD-ROM, it can also serve as valuable reference book for researchers who want to develop their own models in terms of Petri nets.\"--P. 4 of cover.

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization.The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms.

Artificial Intelligence (AI) gets your system to think smart and learn intelligently. This book is packed with some of the smartest trending examples with which you will learn the fundamentals of AI. By the end, you will have acquired the basics of AI by practically applying the examples in this book.

5G and related digital revolutions will require tens of thousands of edge data centers. This book tells you how they work and how to get them built. We are in the middle of the edge computing revolution. Responding to demand for lower latency, telcos and others are moving servers and storage closer to end users—away from the \"core\" to \"the edge.\" This requires the deployment of many thousands of tiny edge data centers. The edge is a big, growing business. Driven by 5G, connected vehicles, and industrial automation, the \"edge economy\" is projected to reach $4.1 trillion by 2030, with investment in edge data centers set to exceed $140 billion by 2028. What exactly is an edge data center? This book explains what they are and how they work. It's early in the edge computing life cycle, so there's time to get prepared for what's coming. If you work in an industry that's transforming through mobility, or any field that will leverage the edge for competitive advantage, this book will help you understand how the edge data center advances your strategic agenda.

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner. Table of Contents: A Deep Learning-Based System for Network Cyber Threat Detection Angel Luis Perales Gomez, Lorenzo Fernandez Maimo and Felix J. Garcia Clemente Machine learning for phishing detection and mitigation Mohammad Alauthman, Ammar Almomani, Mohammed Alweshah, Waleed Alomoush and Kamal Alieyan Next Generation Adaptable Opportunistic Sensing Based Wireless Sensor Networks: A Machine Learning Perspective Jasminder Sandhu, Anil Verma and Prashant Rana A Bio-inspired Approach to Cyber Security Siyakha Mthunzi, Elhadj Benkhelifa, Tomasz Bosakowski and Salim Hariri Applications of a Model to Evaluate and Utilize Users’ Interactions in Online Social Networks Izzat Alsmadi and Muhammad Al-Abdullah A deep-dive on Machine learning for Cybersecurity use cases Vinayakumar R, Soman Kp, Prabaharan Poornachandran and Pradeep Menon Droid-Sec: A Prototype Method to Discover Malwares in Android-Based Smart Phones through System Calls B. B. Gupta, Shashank Gupta, Shubham Goel, Nihit Bhardwaj, and Jaiveer Singh Metaheuristic Algorithms Based Feature Selection Approach for Intrusion Detection Mohammed Al-Weshah, Saleh Al Khalayleh, Ammar Almomani, Mohammed Al-Refai and Riyadh Qashi A Taxonomy of Bitcoin Security Issues and Defense Mechanisms Prachi Gulihar and B. B. Gupta Early Detection and Prediction of Lung Cancer using Machine Learning Algorithms Applied on a Secure Healthcare Data System Architecture Mohamed Alloghani, Thar Baker, Dhiya Al-Jumeily, Abir Hussain, Ahmed Kaky and Jamila Mustafina Preventing Black Hole Attack in AODV Routing Protocol using Dynamic Trust Handshake Based Malicious Behavior Detection Mechanism Bhawna Singla, A.K. Verma and L.R. Raheja Detecting Controller Interlock based Tax Evasion Groups in a Corporate Governance Network Jianfei Ruan, Zheng Yan, Bo Dong and Qinghua Zheng 　 Defending Web Applications against JavaScript Worms on Core Network of Cloud Platforms Shashank Tripathi, Pranav Saxena, Harsh Dwivedi and Shashank Gupta Importance of Providing Incentives and Economic Solutions in IT Security A. Dhahiya, and B. B. Gupta Teaching Johnny to Thwart Phishing Attacks: Incorporating the Role of Self-Efficacy into a Game Application Nalin Asanka Gamagedara Arachchilage, and Mumtaz Abdul Hameed Brij B. Gupta received PhD degree from Indian Institute of Technology Roorkee, India in Information and Cyber Security. He published more than 175 research papers in International Journals and Conferences of high repute including IEEE, Elsevier, ACM, Springer, Wiley, Taylor & Francis, Inderscience, etc. He has visited several countries, i.e. Canada, Japan, Malaysia, Australia, China, Hong-Kong, Italy, Spain etc to present his research work. His biography was selected and published in the 30th Edition of Marquis Who's Who in the World, 2012. Dr. Gupta also received Young Faculty research fellowship award from Ministry of Electronics and Information Technology, Government of India in 2017. He is also working as principal investigator of various R&D projects. He is serving as associate editor of IEEE Access, IEEE TII, and Executive editor of IJITCA, Inderscience, respectively. At present, Dr. Gupta is working as Assistant Professor in the Department of Computer Engineering, National Institute of Technology Kurukshetra India. His research interest includes Information security, Cyber Security, Mobile security, Cloud Computing, Web security, Intrusion detection and Phishing. Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University. Before moving to Macquarie, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Michael holds a PhD degree in computer science from the University of New South Wales (UNSW) and did his post-doc as a research scientist at CSIRO ICT Centre. From 1999 to 2001, Sheng also worked at UNSW as a visiting research fellow. Prior to that, he spent 6 years as a senior software engineer in industries. Prof. Sheng has more than 280 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences including ACM Computing Surveys, ACM TOIT, ACM TOMM, ACM TKDD, VLDB Journal, Computer (Oxford), IEEE TPDS, TKDE, DAPD, IEEE TSC, WWWJ, IEEE Computer, IEEE Internet Computing, Communications of the ACM, VLDB, ICDE, ICDM, CIKM, EDBT, WWW, ICSE, ICSOC, ICWS, and CAiSE. Dr. Michael Sheng is the recipient of the ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). He is a member of the IEEE and the ACM. Homepage: https://web.science.mq.edu.au/~qsheng/

Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage facilities towards the edge of a network. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. We schedule tasks in fog computing devices based on classification data mining technique. A key contribution is that a novel classification mining algorithm I-Apriori is proposed based on the Apriori algorithm. Another contribution is that we propose a novel task scheduling model and a TSFC (Task Scheduling in Fog Computing) algorithm based on the I-Apriori algorithm. Association rules generated by the I-Apriori algorithm are combined with the minimum completion time of every task in the task set. Furthermore, the task with the minimum completion time is selected to be executed at the fog node with the minimum completion time. We finally evaluate the performance of I-Apriori and TSFC algorithm through experimental simulations. The experimental results show that TSFC algorithm has better performance on reducing the total execution time of tasks and average waiting time.

Edge computing has emerged as a transformative data processing method by decentralizing computations and bringing them toward the data source, significantly reducing latency and enhancing response times. However, this shift introduces unique security challenges, especially within the detection and prevention of cyberattacks. This paper gives a comprehensive evaluation of the edge security landscape in peripheral computing, with specialized expertise in identifying and mitigating various types of attacks. We explore the challenges associated with detecting and preventing attacks in edge computing environments, acknowledging the limitations of existing approaches. One of the very interesting novelties that we include in this survey article is, that we designed a Web application that runs on an edge network and simulates SQL injection attacks-a common threat in edge computing. Through this simulation, we examined every one of the cleanup strategies used to discover and prevent such attacks using input sanitization techniques, ensuring that the malicious SQL code turned neutralized. Our studies contribute to deeper know-how of the security landscape in edge computing by providing meaningful insights into the effectiveness of multiple prevention strategies.

LEACH routing protocol equalizes the energy consumption of the network by randomly selecting cluster head nodes in a loop, which will lead to the defect of unstable network operation. Therefore, in order to solve this problem, it is necessary to reduce the energy consumption of data transmission in the routing protocol and increase the network life cycle. However, there is also a problem that cluster heads count with a wide range and the cluster head forwarding data consumed greatly power in the LEACH, which remains to be solved. In this paper, we put forward an approach to optimize the routing protocol. Firstly, the optimal number of cluster head is calculated according to the overall energy consumption per round to reduce the probability of excessive cluster head distribution. Then, the cluster head is used as the core to construct the Voronoi Diagram. The nodes in the same Voronoi diagram become a cluster, that the energy consumption communication in intra-cluster would be less. Finally, in order to optimize the multi-hop routing protocol, an ant colony algorithm is added using a cluster head near the BS to receive and forward it from a remote cluster head. According to the MATLAB simulation data, the protocol can significantly prolong the lifetime of WSNs compared with the LEACH protocol and increase the energy efficiency per unit node in per round. Energy consumption of the proposed approach is only. The approach improved the First Node Death (FND) time by 127%, 22.2%, and 14.5% over LEACH, LEACH-C, and SEP, respectively.

This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.

As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using high-fidelity disaster models and real-time observational data with distributed computing schemes. However, the previous schemes take little account of the correlations between environmental data of disasters, such as landscapes and weather. This causes inaccurate computing load predictions resulting in unbalanced load partitioning, which increases the prediction service times of the disaster management agencies. In this paper, we propose a novel distributed computing framework to accelerate the prediction services through semantic analyses of correlations between the environmental data. The framework combines the data into disaster semantic data to represent the initial disaster states, such as the sizes of wildfire burn scars and fuel models. With the semantic data, the framework predicts computing loads using the convolutional neural network-based algorithm, partitions the simulation model into balanced sub-models, and allocates the sub-models into distributed computing nodes. As a result, the proposal shows up to 38.5% of the prediction time decreases, compared to the previous schemes.