Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols

The integration of the Internet of Things (IoT) has ushered in a transformative era for both residential environments and urban landscapes, re-defining the way people live, work, and interact within them. This chapter delves into the profound significance of IoT in the realm of smart homes and cities, exploring the multifaceted impact it has on enhancing efficiency, sustainability, and quality of life. In the context of smart homes, IoT technology seamlessly intertwines household devices, appliances, and systems, creating a networked ecosystem that enables automation, remote control, and intelligent decision-making. This interconnectivity offers residents unprecedented levels of convenience, energy efficiency, and security while paving the way for innovative services like predictive maintenance and health monitoring. Extending the scope to smart cities, this chapter explains how IoT transforms urban environments into dynamic, data-driven entities. Through an intricate web of sensors, actuators, and data analytics, cities can optimize resource allocation, traffic management, waste disposal, and energy consumption. This leads to reduced congestion, improved air quality, and a more sustainable urban infrastructure. However, the integration of IoT into the fabric of smart homes and cities also raises significant challenges pertaining to data privacy, security, and interoperability. These complexities necessitate robust governance frameworks and technological solutions to ensure the responsible and secure implementation of IoT technologies.

As smart homes and cyber-physical systems become increasingly integrated into our daily lives, they also become susceptible to new and sophisticated forms of cyberattacks. This chapter explores the emerging landscape of new age attacks targeting these interconnected environments. It delves into the diverse range of threats that exploit vulnerabilities in smart home devices and their underlying cyber-physical components. Through comprehensive analysis and case studies, this chapter sheds light on the potential consequences of such attacks and emphasizes the importance of proactive measures to safeguard these systems. By understanding these evolving threats, researchers, practitioners, and policymakers can collectively work toward fortifying the security and resilience of smart homes and cyber-physical systems.

Water is the most important natural element present on earth for humans, yet the availability of pure water is becoming scarce and decreasing. An increase in population and a rise in temperatures are two major factors contributing to the water crisis worldwide. Desalinated, brackish water from the sea, lake, estuary, or underground aquifers is treated to maximize freshwater availability for human consumption. However, mismanagement of water storage, distribution, or quality leads to serious threats to human health and ecosystems. Sensors and embedded and smart devices in water plants require proactive monitoring for optimal performance. Traditional quality and device management requires huge investments in time, manual efforts, labor, and resources. This research presents an IoT-based real-time framework to perform water quality management, monitor, and alert for taking actions based on contamination and toxic parameter levels and device and application performance as the first part of the proposed work. Machine learning models analyze water quality trends and device monitoring and management architecture. The results display how the proposed method manages water monitoring and accesses water parameters more efficiently than other works. 

The demand for and pressure on natural resources worldwide is rising, placing more emphasis on the availability of clean, safe drinking water. Modern technologies like cloud computing, embedded systems, and smart sensors can provide safe and effective control of the supply of drinking water for agriculture and consumer usage. By combining proactive alerting, monitoring, and real-time data collection with proactive management measures, problems are avoided before they arise. This study offers a clever and safe foundation for future research aimed at improving the current irrigation system. This is a low-cost irrigation strategy that uses smart device sensors and cloud connectivity to give automated management and needs according to the environment and season. In order to address the operational failures, this also provides alerting scenarios for device and component failures as well as water leaks by immediately switching to an alternate mode and delivering alert messages about the problems.

Robot-based platforms and processes have integrated the security and efficiency of data into a comprehensive range of domains like manufacturing, industrial, logistical, agricultural, healthcare, and Internet services. Smart cyberattacks have been on the rise, specifically targeting corporate and industrial robotic systems. These attacks are executed once the IoT, Internet, and organization integration is implemented with the industrial units. The authors implemented security criteria-based indices for Cyber-Physical systems (CPS) with industrial components and embedded sensors that process the information logs and processes. The authors proposed an attack tree-based secure framework that does not include every CPS device; however, it takes into consideration the critical exploitable vulnerabilities to execute the attacks. The authors categorized each physical device and integrated sensors based on logs and information in a sensor indices device library. This research simulated the real-time exploitation of vulnerabilities in CPS robotic systems using the proposed framework in the form of a two-phased process. This validates the enhanced data security output of the integrated sensor and physical nodes with the intelligent monitor and controller system health monitor during real-time cyberattacks. This research simulated common cyberattacks on cyber-physical controller servers based on cross-site scripting and telnet pivoting. The authors gathered known and unknown vulnerabilities and exploited them with a tree-based attack algorithm. The authors calculated the average time for cyberattackers with different skills when trying to compromise CPS devices and systems.

Machine learning and artificial intelligence-based sentiment analysis are crucial for companies to automatically predict whether the customers are happy with their products. In this paper, a deep learning model is built to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company that has an online presence to automatically detect customer sentiment from their reviews. The objective of this research work is to propose a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative reviews. In this research work, a dataset containing reviews of 3150 users has been used. Initially, a word cloud of positive and negative reviews has been plotted that gave a lot of insight from the text data. After that, a deep learning model using a multinomial naïve Bayesian classifier has been built and trained by using 80% of the dataset, and then the remaining 20% of the dataset has been used for testing the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, and it outperformed three of them.

The Internet of Things (IoT) has redefined several aspects of our daily lives, including automation and control of the living environment, innovative healthcare services, and much more. Digital IoT devices and sensors, when integrated with home appliances, industrial systems, and online services in the physical world, have brought intense, disruptive changes in our lives. The industry and home users have widely embraced Internet of things or IoT. However, the innate, intrinsic repercussions regarding security and data privacy are not evaluated. Security applies to Industrial IoT (IIoT), which is in its infancy stage. Techniques from security and privacy research promise to address broad security goals, but attacks continue to emerge in industrial devices. This research explores the vulnerabilities of IIoT ecosystems not just as individual nodes but as the integrated infrastructure of digital and physical systems interacting with the domains. The authors propose a unique threat model framework to analyze the attacks on IIoT application environments. The authors identified sensitive data flows inside the IIoT devices to determine privacy risks at the application level and explored the device exchanges at the physical level. Both these risks lead to insecure ecosystems. The authors also performed a security analysis of physical domains and digital domains.

The authors implemented an attack scenario simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generate automated malicious commands, which conform to device protocol standards and bypass compromise detection. The authors performed attack detection testing with three different home setup simulations and referred to accuracy of detection, ease of precision, and attack recall, with F1-score as the parameters. The results obtained for anomaly detection of IoT logs and messages used K-nearest neighbor, multi-layer perceptron, logistic regression, random forest, and linear support vector classifier models. The attack results presented false-positive responses with and without the proposed framework and false-negative responses for different models. This research calculated precision, accuracy, F1-score, and recall as attack detection performance models. Finally, the authors evaluated the performance of the proposed IoT communication protocol attack framework by evaluating a range of anomalies and compared them with the maliciously generated log messages. IoT Home #1 in which the model involved IP Camera and NAS device traffic displayed 97.7% Accuracy, 96.54% Precision, 97.29% Recall, and 96.88% F-1 Score. This demonstrated the model classified the Home #1 dataset consistently.

In the early 2000s, the Internet meant being able to connect different communication devices, whereas the focus in the last few years has been on connecting ‘things’ to the Internet. Although there is no distinct classification for these devices and things on the Internet, the Internet of Things (IoT) ecosystem primarily consists of a complex network of devices, sensors, and things. These ‘things’ are controlled by humans and utilize the existing cloud infrastructure. These devices provide facilities and benefits to make our lives comfortable. IoT domains include smart homes, healthcare, manufacturing, smart wearables, smart cities, smart grids, industrial IoT, connected vehicles, and smart retail. Different IoT models involve human-to-IoT, IoTto-IoT, and IoT-to-traditional system architectures. In most scenarios, the architecture ends up connecting to the unsecured Internet. This has thrown open several critical issues leading to cybersecurity attacks on IoT devices. IoT communications, protocols, or architecture have never been conceptualized to handle the new age of cybersecurity attacks. IoT devices have limited computing, storage, networks, and memory. In this research, the authors present a unique IoT attack framework named IAF, focusing on the impact of IoT attacks on IoT applications and service levels. The authors also propose an all-inclusive attack taxonomy classifying various attacks on IoT ecosystems.

An increase in cyber-physical systems and IoT has increased the output of the industry, and these systems have become the backbone of the industry. However, these systems are vulnerable to various cyber-attacks. The increasing number of IoT and cyber-physical systems has called for interventions in the way cybersecurity system works. This paper evaluates the effectiveness of various feature selection techniques– NB, LR, DT and SVM, ensembled shallow – RF and Adaboost with RBF and uses the statistical techniques Chi and Pearson correlation coefficient for choosing top features and applies them to the traditional machine learning algorithm to get accuracy and detection rate. The machine learning algorithms are trained and evaluated on the KDDCup ’99’ dataset. The study shows that machine learning algorithms work perfectly and provide higher accuracy if the feature vector consists of a few significant features.