Data Analytics and Modeling in IoT-Fog Environment for Resourceconstrained IoT-Applications: A Review

A distributed denial of service (DDoS) attack represents a major threat to service providers. More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by overwhelming the target with a massive number of malicious requests. A cyberattack of this kind is likely to result in tremendous economic losses for businesses and service providers due to increasing both operating and financial costs. In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks. Indeed, many defense systems have been transformed into smart and intelligent systems through the use of ML techniques, which allow them to defeat DDoS attacks. This paper analyzes recent studies concerning DDoS detection methods that have adapted single and hybrid ML approaches in modern networking environments. Additionally, the paper discusses different DDoS defense systems based on ML techniques that make use of a virtualized environment, including cloud computing, software-defined network, and network functions virtualization environments. As the development of the Internet of Things (IoT) has been the subject of significant research attention in recent years, the paper also discusses ML approaches as security solutions against DDoS attacks in IoT environments. Furthermore, the paper recommends a number of directions for future research. This paper is intended to assist the research community with the design and development of effective defense systems capable of overcoming different types of DDoS attacks.

Smart objects have grown in popularity and acceptance over the past period due to their decreasing size, greater intelligence, and lower costs. The Internet of Things (IoT) connects physical devices, including actuators, sensors, and cameras, to a network via the Internet. The widespread use of IoT devices has led to an exponential rise in network traffic volume and complexity, creating new challenges for real-time network security and threat detection. This study attempts to design an intrusion detection system which is scalable and capable of handling the vast number and variety of IoT traffic. It is based on improving the scalability and precision of the detection of threats by employing machine learning (ML) and deep learning (DL) techniques and hybrid model. The model is trained and tested on the CIC IoT DIAD 2024 dataset, a large high-volume dataset consisting of diversified IoT traffic, benign and malicious activity. It includes extensive data preprocessing, feature selection, and training of various models. Features were selected using an Analysis of Variance (ANOVA)–based feature selection technique to reduce computational overhead and time complexity while mitigating the curse of dimensionality and enhancing model accuracy. The resulting optimal feature subset was then used to train and evaluate several classifiers, including Decision Trees, K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN–LSTM model. The models are compared using typical measures of performance such as accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that hybrid deep learning models specifically the CNN-LSTM outperformed the other models in recognizing binary attacks achieving the highest performance with accuracy of 94.08% followed by CNN and LSTM with accuracies of 93.37% and 93.24% respectively. In contrast, the traditional machine learning model, Decision trees demonstrated superior performance in multi-class classification, achieving an accuracy of 98.25% defeating KNN (90%) as well as the hybrid deep learning model (CNN-LSTM – 88.30%). This, work paves the foundation for the implementation of scalable intrusion detection models in real IoT infrastructures. The future of the work is to integrate the developed models using massive data streaming infrastructures in support of real-time intrusion detection in large-scale, dynamic IoT infrastructures.

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

At present recommender systems (RS) are incorporating contextual and social data of the client, delivering context aware RS where it take request response approach in which the recommendations are given to the client upon his solicitation. Later on they will utilize verifiable data of the client anywhere from the Internet of Things (IoT). Currently, a proactive RS that pushes proposals to the client when the present circumstance appears proper, without unequivocal client demand has been presented in the exploration research field of RS. In this paper, an outline of a context aware RS that prescribes recommendations under the IoT worldview is proposed. We also did the empirical analysis of the Machine Learning (ML) techniques like Genetic Algorithm optimized Artificial Neural Network (GA-ANN), decision tree, ANN, bagging, boosting which will perform the reasoning of the context. All inputs are virtually derived from the IoT and its output scores are calculated based on ML techniques which will decide if to push a recommendation to the user or not. Benchmark data of the Chicago restaurant is analyzed and it was observed that for the 98% of the contexts, GA-ANN produced correct recommendations with higher accuracy and efficiency in the correct times and context.

Today’s Machine Learning (ML) in a blend with Internet of Things (IoT)-based cloud applications plays a significant role in our everyday life. As indicated by Gartner’s recent study, there are around 25 billion devices and a gadget interfacing with IoT including wearables and automated vehicles to smart homes and smart cities applications. All such connected (smart) devices generate immense data that needs to be examined and analysed, to ensure that they continually learn from the available datasets and better themselves without any manual interference. This is where the prerequisite for machine learning comes into being. Several ML algorithms and techniques are introduced in a short time to easily evaluate big data measurements, increasing the IoT’s productivity. Similarly, special ML techniques, such as decision trees, clustering and neural and Bayesian networks, allow devices and gadgets to discern trends from various sources in different kinds of datasets and take appropriate decisions based on their analysis.It would really be difficult for smart devices to make smart decisions gradually without including and enforcing ML. The IoT helps to interconnect various hardware devices, such as houses, cars, electronic gadgets and other devices that are integrated with actuators, sensors and software, so that data can be collected and shared. As various organizations understand the progressive capability of the IoT, they have begun finding various obstructions they have to deliver to use it productively. Numerous organizations and businesses use ML to exploit the IoT’s latent capacity. This chapter evaluates the different methods of machine learning that deal with the challenges posed in the handling of IoT data. Note that this big data is generated through the communication of Internet of Things/smart devices, and this data stored at cloud. The taxonomy of machine learning algorithms is described in this chapter, explaining how different techniques are applied to data generated using IoT devices. It will also address the future problems of machine learning for IoT data analytics.KeywordsInternet of Things (IoT)-based cloud applicationsMachine learning techniquesArtificial intelligenceClassificationSmart era

The interconnected system consisting of tangible items featuring the capacity to transmit information is called the Internet of Things (IoT). The design encompasses various gadgets, including industrial machinery and electrical appliances. IoT devices are unprotected from safety hazards; eventually, it is important to remain vigilant about this provision to preserve data and establish that IoT systems can withstand new hacking attempts. The research seeks to provide a novel approach for machine learning (ML)--based danger indicator identification in IoT environments. Thus, a Binary Chimpanzee-Optimized Fine-Tuned Linear Least Squares Twin Support Vector Machine (BC-FTLLS-TSVM) method is introduced for detecting attack signs in IoT settings. To improve the efficiency of the proposed threat identification mechanism, an N-BaIoT dataset that accommodates the unpredictability of damaging attacks against IoT gadgets is utilized. The method includes the BCOA to feed the selection of features and greater parameters using the FTLLS-TSVM, to advance the accuracy of monitoring IoT threat signs. The proposed framework is performed by applying Python programming. Various metrics, such as Accuracy (97.50%), Precision (97.85%), F1 Score (98.35%), and False Positive Rate (FPR) (2.5%), were evaluated during the validation phase to evaluate the proposed BC-FTLLS-TSVM method. The results of the experiment show that the BC-FTLLS-TSVM paradigm procures improved classification of hazard signs in an IoT environment compared to other existing techniques. By leveraging optimized feature selection and fine-tuning through BCOA and FTLLS-TSVM, the proposed method creates effective detection of security threats. The results emphasize the ability of this approach to increase the defense and IoT systems' resistance to emerging cyber threats.

Background: The rapidly expanding concept, the Internet of Things (IoT), connects a large number of devices to the internet for effective real-time data sharing and communication. But as IoT technology advances, new security risks also arise, and for this reason, machine learning (ML)-based intrusion detection systems (IDS), particularly anomaly-based ones, have emerged as a crucial defence against these new dangers to IoT networks. Objective: The primary goal of this study is to develop and assess anomaly-based IDS based on ML techniques for use in Internet of Things environments. The goal is to improve the models' performance in terms of accuracy and usefulness. Methods: The first phase of the study is a thorough review of the recent studies in the field to support the model design. The steps of the experimental stage include data preprocessing, encoding, and normalization. Data balancing was achieved using the SMOTEENN technique. The experiments and validation studies were performed on the UNSW-NB15 dataset. The employed ML algorithms for the study include DT, DNN, RF, XGBoost, and KNN, and the performance was in terms of precision, recall, accuracy, and F1-score. Results: XGBoost recorded the best detection accuracy (96.37%), followed by RF and DNN. The models were evaluated on both normal and attack traffic, and the suggested model outperformed many existing approaches in recent literature. Conclusion: The experimental findings confirm the suitability of hybrid and ensemble ML models in improving intrusion detection performance in IoT systems. Future research should consider integrating real-time datasets and combining deep learning with ensemble methods to develop more robust and adaptive models.

The growth of the Internet of Things (IoT) and smart systems has paved the way for the integration of machine learning (ML) and optimization techniques to enhance their efficiency, security, and scalability. This survey provides a comprehensive overview of recent advancements in applying machine learning algorithms and optimization strategies to IoT and smart systems. It highlights key areas where these technologies intersect, such as energy efficiency, intelligent decision-making, resource management, and anomaly detection. The paper discusses various machine learning approaches, including deep learning, reinforcement learning, and supervised/unsupervised models, that have been applied to optimize system performance in real-time applications. Furthermore, the survey examines optimization techniques like metaheuristics, evolutionary algorithms, and gradient-based methods that improve IoT systems' reliability and resource utilization. The integration of these techniques offers significant advantages, including reduced latency, enhanced security, and increased adaptability in smart systems. The findings emphasize the importance of adopting ML and optimization methods to address the challenges posed by the growing complexity of IoT environments, fostering future innovations in connected and autonomous systems. This survey serves as a resource for researchers and practitioners seeking to explore the synergies between IoT, ML, and optimization techniques.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

여러 사물 인터넷 기기들을 조합, 활용하여 다양한 서비스를 제공할 수 있게 된 환경에서 추천시스템은 사용자가 원하는 서비스를 선택하는 데 있어 도움을 줄 수 있다. 기존의 추천 시스템에 대한 많은 연구는 주로 개인 사용자 대상의 추천에 집중되어 있으나 사물 인터넷 환경에서는 개인과 그룹 모두 사용자가 될 수 있으므로 그룹에 대한 추천 방법이 필요로 하다. 본 연구는 사물 인터넷 환경에서 그룹사용자들의 서비스 선호도를 분석하고, 이를 바탕으로 특정 장소에서 서비스를 이용한 적이 없는 새로운 그룹 사용자에게 서비스를 추천할 수 있는 방법을 개발하였다. 본 연구에서는 그룹의 구성 정보를 기반으로 그룹들 간의 유사도를 측정하여 사용자 기반 협업 필터링을 적용하였다. 실험에서는 실제 사물 인터넷 테스트 베드 환경에서 수집된 데이터를 사용하였으며 실험 결과를 통해 제안한 서비스 추천 방법이 효과적임을 확인할 수 있었다.

Recently, IETF (Internet Engineering Task Force) presented CoAP (Constrained Application Protocol) for the communication between sensor and actuator nodes in a constrained environment such as small amount of memory and low power. IETF CoAP protocol can convert easily, and can use to monitor or control infrastructure utilities through low-power sensor and actuator networks in IoT (Internet of Things) and M2M (Machine-to-Machine) environment. IETF CoAP protocol provides the special requirements of this constrained environment, especially considering energy, automation, and other IoT applications. In this paper, we design and implement a node registration and discovering based CoAP in IoT environment. The node is a CoAP node which is designed for working in constrained environment. For node registration in IoT, we used CoRE RD (Resource Directory) functionalities to register CoAP node’s information which works in IoT environment.

This chapter introduces data science with its history and importance in this modern era briefly. This chapter also elaborates the discussion by relating data science to various modern fields like big data analytics, artificial intelligence, deep learning, and machine learning. This chapter also discuss the necessary of data analytics in this big data era. This chapter also briefly introduces another emerging field, Internet of Things (IoT) and explores the contribution IoT towards big data analytics and data science in research perspective. It also briefly introduces the programming and non-programming tools used in the data science field.

The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

In the modern era, web data retrieval and data analytics play a crucial role for taking intelligent decisions in Internet of Things (IoT) environment. In IoT environment, various objects perform the tasks of sensing and computation for providing uninterrupted services (e.g., e-health, e- transportation, security access, etc.) to the end users. However, accessing the relevant and accurate information with reduced delay is still a challenging task in IoT environment. Although this aspect has been explored in the literature, the existing proposals have high complexity and require long time for accessing the relevant information from different IoT objects located across the globe. The information may be located across different web pages, which are linked together irrespective of their geographical locations. So, this paper addresses the issues such as accuracy, context- aware, reduced delay with low complexity in accessing the information from a remote device by the end users. In the proposed scheme, the strength of a web page which contains the relevant information to be fetched is judged by the quality of content and the inter- connections between different web pages. The proposed scheme simplifies the rank score calculation of these web pages and provides quality web pages at the top of the search result pages by demoting spam web pages. Bias connected web pages are verified by the linkage information of spam web pages. The Quality Assessment for Information Retrieval (QAIR) algorithm is proposed for the classification of web pages. The proposed algorithm computes the QAIR score by evaluating the web page quality. Microsoft Learning to Rank dataset has been used for the experiments, which consists of 239092 query-url pairs. By using this dataset, the computed QAIR score is compared with the PageRank score. This comparison determines the category of web page, i.e., either the page is strong or weak. The proposed scheme has been validated with decision tree followed by ten- fold cross validation, which results in an accuracy of 92.4%.