Dr. Ezhil Selvam Kalaimannan

To make smart cities more sustainable, deep neural networks (DNNs) assess data from sensors, the Internet of Things (IoT), and social media in an effective manner. Swiftly assessing text, images, and time-series data, they aid administrators in understanding complex urban trends and allocating resources. DNNs employ climate predictions and energy usage patterns to enhance energy distribution, minimize trash, and boost green energy resources. This improves grid reliability and reduces greenhouse gas emissions. DNNs trained on real-time traffic data enhance congestion, transportation efficacy, and traffic flow. Intelligent scheduling techniques enhance transportation, shipment, and fuel economy. DNNs enhance smart city garbage collection routes, recycling opportunities, and on-demand services by assessing past and present data. This strategy encourages reducing trash, recycling, and maintaining a clean atmosphere. Nevertheless, there is an urgent need to address data privacy, computational injustice, and the accessibility of computer resources. Responsible and collaborative decision-making is crucial to address these issues and avoid unfairness. By prioritizing ethics and fair distribution of benefits, DNNs can improve transportation, trash management, and energy efficiency, paving the way for more accessible and inclusive digital cities.

In the context of manufacturing, an Industrialized Control and Automation System (ICAS) is a set of interrelated economic processes that allow for the automated, data‐driven tuning of various operational parameters of machinery and other mechanical systems with minimal human intervention. Safety, productivity, and equipment are all in danger from the widespread use of ICAS in areas like manufacturing, logistics, supply chain management, and transportation. Industry Internet Systems (IIS) and the IoT have played a critical role in the evolution of smart factories, which facilitate the autonomous management of resources through the sharing of data between previously isolated devices. Industry 4.0, IIS, Industrial Internet of Things, and ICAS all rely on IoT as their base, anticipating major advancements in design. The IIoT refers to a system where machines may interact with one another and make small adjustments to their environment or behavior with little to no human interaction. Embedded sensor nodes collect data, gateways analyze and send it to the cloud, and higher‐level communication is made possible via networks like Wi‐Fi and Ethernet, making up the three layers of IIoT operations. Focusing on its data analysis and problem‐solving utility within the context of Industry 4.0, this article provides a detailed review of the theoretical and technological framework underlying the corporate implementation of IIoT.

Distributed Denial of Service (DDoS) attack has been used as a weapon by many hackers to cause a network failure thereby preventing the users from using the network service. This attack is perpetrated to create financial loss, to gain money, to seek revenge, etc. Software Defined Network (SDN) has grown widely to gain the hope of many researchers and network administrators to meet the need for maintenance of the data centers. Today’s world mostly prefers SDN due to its flexibility, cost efficiency, manageability, etc. SDN has a network architecture that changes its traffic pattern every time a change is made in its central architecture. The separation of the planes in SDN ensures the security of entire network devices and their functions thereby protecting the network. So, the attackers target the controller to bring it down leading to a network failure. Thus, the single controller has many issues with factors of both performance and scalability. Multiple controllers are used to resolve this issue. This chapter presents a full assessment of the necessity for multiple controllers in SDN, as well as the benefits and drawbacks.

Network intrusion detection systems are vital for network security, tasked with identifying and thwarting malicious activities. This research compares Random Forest (RF) and Support Vector Machines (SVM) in detecting intrusions using the UNSW-NB15 dataset. RF achieves 99.64% accuracy, with 4454 True Positives (TP), 4400 True Negatives (TN), 31 False Positives (FP), and 1 False Negative (FN). In contrast, SVM attains 78.87% accuracy, with 4422 TP, 2586 TN, 63 FP, and 1815 FN. The study also considers percentage changes in TP (0.72%), FP (-50.79%), FN (-99.94%), and TN (70.15%), providing insights into model adaptability. Validating RF with the Synthetic Minority Over-sampling Technique (SMOTE) yields 99.63% accuracy, compared to SVM’s 89%, indicating RF’s robustness with imbalanced datasets. RF’s superiority partly stems from its effective use of feature importance analysis, and its robustness to noise, maximizing the utility of selected features for better predictive performance compared to SVM which does not perform well with feature selection. This provides an understanding of RF and SVM strengths and limitations in network intrusion detection and underlines the importance of selecting appropriate machine learning models for network security applications concerning a particular kind of datase

Modern enterprise networks are complex and present countless security challenges. Understanding the nature of the systems that exist within a network environment is a vital step in securing such environments. Therefore, operating systems on the network must be identified, tracked, and continuously monitored. In this research, we consider the problem of detecting unauthorized operating systems on an enterprise network, which could exist because of the unintentional actions of an authorized user or the unauthorized actions of internal users or external attackers. We intend to utilize an artificial neural network-based classifier [ANN], which will be developed using the PyTorch and fastai deep learning libraries. Simulated network traffic has been generated through the implementation of two separate virtual network environments, and the generated traffic was passively collected and analyzed prior to traversing the network boundary. The performance evaluation of the neural network classifier will be analyzed using the collected data in this research.