Spinalfgnet Based Intrusion Detection for IoT Enabled Smart Irrigation

Smart agriculture is important in achieving the SDGs, especially food and water. The transition to using smart irrigation systems is highly recommended, considering that this transition can optimize agricultural production. A smart irrigation system is a unified irrigation process integrated with automated technology and IoT (Internet of Things) that drives a new direction of smart agricultural technology development. This study highlights the smart irrigation system as the smart agricultural transition concerning SDGs. The main method was a qualitative review based on a literature study with the criteria for exclusion consisting of indexed journals, review papers or research articles, and publications published within the last decade. The review shows the irrigation control and monitoring used in recent years. Other than that, as a brief report on the current status of IoT-related smart irrigation development, several types of effective smart irrigation that have been done before are compared. Discussed the perspective of SDGs, the goal indicators achieved through smart irrigation showed good results; 11 indicators were achieved. Thus, the challenges and prospects of IoT-related development for smart irrigation are discussed. This study will help researchers and farmers understand clever irrigation more effectively and precisely.

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM).

Water management in agriculture has been transformed by smart irrigation systems that use the Internet of Things (IoT) to maximize resource use and boost crop yields. IoT-based irrigation, which reduces waste and improves sustainability in the face of growing water scarcity, combines sensors, actuators and real-time data analytics to deliver accurate water flow. The use of IoT in agriculture is reviewed in this paper, which also covers AI-driven decision-making algorithms and communication technologies like LoRa, Zigbee and Wi-Fi. In agriculture, IoT has been widely employed in agriculture to enable better farming and redefine conventional practices. Some of the necessary applications include monitoring crops, monitoring livestock, automation of the supply of water through real-time data, promotion of optimal water consumption and wastage minimization. Furthermore, increased agricultural yields, cost savings and water conservation are just a few benefits of using IoT in irrigation systems. Research also claimed that when compared to traditional methods, IoT-based irrigation systems can save up to 30% on water usage. IoT-based irrigation has drawbacks despite its benefits, including costly upfront costs, complicated technology and cyber security threats. The use of self-powered sensors, AI-powered predictive irrigation and blockchain-enhanced data security are some of the upcoming trends. Smart irrigation systems have the ability to transform precision agriculture and guarantee both environmental preservation and food security by removing implementation obstacles. Future research should focus on enhancing interoperability among IoT devices, improving decision-making algorithms and developing cost-effective solutions for small-scale farmers. With continuous innovation and widespread adoption, IoT-driven smart irrigation has the potential to address global food security concerns while preserving water resources, creating a more sustainable and technologically advanced agriculture industry.

Today the use of Internet of Things (IoT) applications increasing rapidly to use automation in the areas including home, hospital, office, and others. In the future, the people expected to use IoT applications in the year 2025 by 11 trillion dollars business worth with use. The problem is to develop an Intelligent IoT system application based on user specifications, and the demand for application with the use of elements, models, and providing services. The objective of the paper is to explore IoT elements, framework-architecture, analysis, and monitoring, and case study applications, such as, Heath care System, Pilotless plane, Smart lighting, Home IDS, Smart Parking, Smart Irrigation, and Intelligent Transport System applications. Finally, effective utilization of elements, protocols, components, and sensors networks, AI applications, and Machine learning applications predict the future patterns based on present and past data of applications. The conclusion of the proposed model improves the quality of the IoT system.

The Internet of Things (IoT) is regarded as one of the advanced models for developing a global network of interconnected devices and machines used in nearly every aspect of our lives. In IoT, intrusion detection and prevention are essential to safeguard network systems from unauthorized access. While firewalls protect network servers from unwanted or malicious traffic, securing IoT networks remains complex due to their unique characteristics and architecture. To solve these challenges, the Convolutional Spinal Zeiler and Fergus Network (ConvSpinal_ZFNet) is proposed for enhancing firewall-based attack prevention in IoT systems. In attack detection, data is first normalized by the Quantile normalization. Relevant features are then selected using the Skill Optimization Algorithm (SOA). The attack detection is carried out by the Spiking Visual Geometry Group 16 (Spiking VGG-16). The attacks are classified using the Fractional Artificial Protozoa Optimization enabled Spiking VGG-16 (FAPO Spiking VGG-16), then attack mitigation is performed. Furthermore, to validate the effectiveness of ConvSpinal_ZFNet-based attack detection, key performance metrics are reported. The method achieves optimal values of 92.89% accuracy, 91.46% sensitivity, 90.57% specificity, and 1.352 MB memory consumption.

<abstract><p>With the widespread application of smart devices, the security of internet of things (IoT) systems faces entirely new challenges. The IoT data stream operates in a non-stationary, dynamic environment, making it prone to concept drift. This paper focused on addressing the issue of concept drift in data streams, with a key emphasis on introducing an innovative drift detection method-ensemble multiple non-parametric concept localization detectors, abbreviated as EMNCD. EMNCD employs an ensemble of non-parametric statistical methods, including the Kolmogorov-Smirnov, Wilcoxon rank sum and Mann-Kendall tests. By comparing sample distributions within a sliding window, EMNCD accurately detects concept drift, achieving precise localization of drift points, and enhancing overall detection reliability. Experimental results demonstrated the superior performance of EMNCD compared to classical methods on artificial datasets. Simultaneously, to enhance the robustness of data stream processing, we presented an online anomaly detection method based on the isolation forest (iForest). Additionally, we proposedwhale optimization algorithm (WOA)-extreme gradient boosting (XGBoost), a drift adaptation model employing XGBoost as a base classifier. This model dynamically updates using drift points detected by EMNCD and fine-tunes parameters through the WOA. Real-world applications on the edge-industrial IoTset (IIoTset) intrusion dataset explore the impact of concept drift on intrusion detection, where IIoT is a subclass of IoT. In summary, this paper focused on EMNCD, introducing innovative approaches for drift detection, anomaly detection, and drift adaptation. The research provided practical and viable solutions to address concept drift in data streams, enhancing security in IoT systems.</p></abstract>

The integration of 5G technology and the Internet of Things (IoT) was redefining modern agricultural practices, facilitating precision farming, smart irrigation, and sustainable resource management. This book chapter explores the transformative impact of 5G and IoT in advancing agricultural efficiency, resilience, and sustainability. By leveraging real-time data collection and low-latency communication, 5G networks enable the seamless operation of IoT devices, fostering data-driven decision-making and automated solutions. Case studies from regions with significant water scarcity and arid climates demonstrate the tangible benefits of smart irrigation systems, including substantial water savings and increased crop yields. The chapter delves into the role of emerging technologies such as artificial intelligence and machine learning, which, when integrated with IoT and 5G, provide predictive insights and adaptive strategies for crop management. Future trends emphasize the importance of scalable 5G infrastructure, advanced edge computing, and comprehensive data analytics to enhance agricultural productivity and sustainability. This chapter underscores the synergy between technology and agricultural practices, offering insights into overcoming challenges such as connectivity gaps, high implementation costs, and the need for technical training. Ultimately, the discussion highlights how these innovations can contribute to global food security and sustainable agriculture, presenting a blueprint for the future of smart farming.

Food security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology. To meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of (1) cloud computing, (2) embedded systems, and (3) Internet-of- Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology. This paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as (1) moisture, (2) humidity, (3) temperature, and (4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach. and conclusion. Farmers can access comprehensive farm data from anywhere in the world as the sensor data is transmitted in real-time to both the ThingSpeak cloud and the ThingView. This capability allows for more precise crop irrigation and increased production. The study’s findings demonstrate a striking 70% reduction in water consumption for soil irrigation when utilizing the proposed smart irrigation system. This paper underscores the significant promise of smart irrigation systems, driven by IoT, embedded systems, and cloud computing, to conserve water resources and advance food security. This article proposes an innovative solution that reduces soil irrigation water consumption by 70% compared to traditional methods. It explores how smart irrigation can improve the sustainability of agriculture and positively influence food security.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The advancement of Internet of Things (IoT) technologies leads to a wide penetration and large-scale deployment of IoT systems across an entire city or even country. While IoT systems are capable of providing intelligent services, the large amount of data collected and processed in IoT systems also raises serious security concerns. Many research efforts have been devoted to design intelligent network intrusion detection system (NIDS) to prevent misuse of IoT data across smart applications. However, existing approaches may suffer from the issue of limited and imbalanced attack data when training the detection model, which make the system vulnerable especially for those unknown type attacks. In this study, a novel hierarchical adversarial attack (HAA) generation method is introduced to realize the level-aware black-box adversarial attack strategy, targeting the graph neural network (GNN)-based intrusion detection in IoT systems with a limited budget. By constructing a shadow GNN model, an intelligent mechanism based on a saliency map technique is designed to generate adversarial examples by effectively identifying and modifying the critical feature elements with minimal perturbations. A hierarchical node selection algorithm based on random walk with restart (RWR) is developed to select a set of more vulnerable nodes with high attack priority, considering their structural features, and overall loss changes within the targeted IoT network. The proposed HAA generation method is evaluated using the open-source data set UNSW-SOSR2019 with three baseline methods. Comparison results demonstrate its ability in degrading the classification precision by more than 30% in the two state-of-the-art GNN models, GCN and JK-Net, respectively, for NIDS in IoT environments.

The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

The rise of the Internet of Things (IoT) has transformed our daily lives by connecting objects to the Internet, thereby creating interactive, automated environments. However, this rapid expansion raises major security concerns, particularly regarding intrusion detection. Traditional intrusion detection systems (IDSs) are often ill-suited to the dynamic and varied networks characteristic of the IoT. Machine learning is emerging as a promising solution to these challenges, offering the intelligence and flexibility needed to counter complex and evolving threats. This comprehensive review explores different machine learning approaches for intrusion detection in IoT systems, covering supervised, unsupervised, and deep learning methods, as well as hybrid models. It assesses their effectiveness, limitations, and practical applications, highlighting the potential of machine learning to enhance the security of IoT systems. In addition, the study examines current industry issues and trends, highlighting the importance of ongoing research to keep pace with the rapidly evolving IoT security ecosystem.

Herein, an Internet of Things (IoT) system with remote reconfigurable wireless sensor network (WSN) nodes is presented and its application to measure group work activity in a classroom is discussed. This IoT system consists of wiki pages, bots, (defined as remotely controlled devices), gateways, and remotely reconfigurable WSN Nodes. The bots/gateways and WSN nodes of the IoT system are controlled by scripts written on wiki pages that are read by the bot/gateways, which then forward the commands contained in the scripts to the WSN nodes. Each WSN node then executes the commands, obtains the resulting sensor values, and then returns those values to the bot/gateways. The bot/gateways relay the values and write them onto the wiki page. Each WSN node is flexible and reconfigurable because its behavior can be changed by the script commands written onto the wiki page. Using this IoT system, we created a measurement system to evaluate activity levels of group work in a classroom. After developing the measurement system, we found it was possible to adjust the sampling rate for obtaining sensor values simply by rewriting the script on the wiki page without directly manipulating the WSN.

Smart irrigation systems, using AI, machine learning, sensors, and IoT, revolutionize agricultural water management. These systems offer precise water usage tailored to crop needs and environmental conditions, addressing issues like water scarcity, growing agricultural demand, and sustainability. This chapter examines smart irrigation components, such as sensors, data analytics, weather forecasting, and automation, emphasizing AI's role in optimizing practices. It also highlights benefits like water conservation, improved yields, cost savings, and sustainability, supported by case studies in precision agriculture and urban farming. Challenges include high costs, technical expertise, data management, and connectivity. Future prospects focus on advanced AI models, better sensors, renewable energy, and policy support to enhance these systems' effectiveness and accessibility. The chapter underscores the critical role of smart irrigation in sustainable agriculture and food security.

Privacy preservation in blockchain based IoT systems: Integration issues, prospects, challenges, and future research directions