Securing the Iot Ecosystem: Challenges and Innovations in Smart Device Cybersecurity (original) (raw)
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Towards Development of Machine Learning Framework for Enhancing Security in Internet of Things
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An IoT system is a smart network that connects all items to the Internet and exchanges data using Internet Engineering Task Force established protocols. As a consequence, everything is instantly accessible from any place and at any time. The Internet of Things (IoT) network is built on the backbone of tiny sensors embedded in common objects. There is no need for human intervention in the interactions of IoT devices. The Internet of Things (IoT) security risk cannot be ignored. Untrusted networks, such as the Internet, are utilized to provide remote access to IoT devices. As a result, IoT systems are susceptible to a broad range of harmful activities, including cyberattacks. If security problems are not addressed, critical information may be hacked at any time. This article describes a feature selection and machine learning-based paradigm for improving security in the Internet of Things. Because network data are inherently abundant, it must be reduced in size before processing. Dimen...
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Billions of IoT devices are in use worldwide and generate a humongous amount of data for the IoT system. This continuous stream of data is open to attack during its collection, transportation, processing, dissemination and storage cycle. Also, IoT devices themselves are points of system vulnerability through which the system can be attacked. Machine learning (ML), due to its ability to identify inherent patterns and behaviour in data, has been applied by many researchers to IoT data such that strange patterns or intrusions into IoT systems can be speedily detected and real-time decisions on security and privacy (S&P) protection implemented in a timely manner. Different ML techniques with their different algorithms have provided solutions in various scenarios such that security and privacy requirements for the IoT system can be met. In particular, ML has been successfully applied in intrusion detection and has been shown to perform better than traditional means in flagging new trend...
IDS Based on Machine Learning in Reaction to IoT Attacks: Review and Empirical Evaluation
International Journal on Advanced Science, Engineering and Information Technology, 2023
Recently, connected objects have been the subject of cyber-attacks at an alarming rate. These devices connected to a vast volume data stream have insufficient resources and are not manually configured. Typically, attacks target the usability and exploitation of these vulnerabilities. These attacks make the mission of traditional intrusion detection (IDS) systems more challenging to limit intrusion threats. Machine learning (ML) can solve this problem, mainly since the Internet of Things (IoT) can collect and transfer massive amounts of data. This data is the essence of ML, enabling it to build security and privacy models which can predict or classify malicious nodes and network traffic in the IoT. This article looks at the more common forms of cyberattacks, which could lead to an IoT system failure, as well as a countermeasure capable of limiting their damage. First, we present a general review of IDS and these evaluation measures as a solution to limit these attacks. After reviewing the ML domain and these often-used algorithms, on which the IDS can be based to accomplish its mission, we examine the different datasets researchers use to form their IDS. Finally, we look at a practical example of using Python to evaluate ML methods on a current dataset (TON IoT). The research is based on previous research on the topic. The results enable us to choose the appropriate algorithms for the IDS to achieve the best binary and multi-classification based on the evaluation parameters.
Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020
The insecure growth of Internet-of-Things (IoT) can threaten its promising benefits to our daily life activities. Weak designs, low computational capabilities, and faulty protocol implementations are just a few examples that explain why IoT devices are nowadays highly prone to cyber-attacks. In this survey paper, we review approaches addressing this problem. We focus on machine learningbased solutions as a representative trend in the related literature. We survey and classify Machine Learning (ML)-based techniques that are suitable for the construction of Intrusion Detection Systems (IDS) for IoT. We contribute with a detailed classification of each approach based on our own taxonomy. Open issues and research challenges are also discussed and provided.
Cyber-security is a fast-changing area of discussion due to advanced technologies that have given room for many cybercriminals and attacks in recent years. Cloud computing and increased levels of machine-to-machine interactions have created potential loopholes for invasion. Therefore, it is important to secure the user's privacy, data, and devices in cyberspace. Recent years have seen tremendous change and increase in machine learning (ML) algo rithms that are solving four main cyber-security issues, such as IDS, Android malware detection, spam detection, and malware analysis. The study analyzed these algorithms using a knowledge base system and data mining of information collected from various sources. T he outcomes have shown that ML is an efficient wa y to address cyber-security risks.
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Physical objects that may communicate with one another are referred to “things” throughout the Internet of Things (IoT) concept. It introduces a variety of services and activities that are both available, trustworthy and essential for human life. The IoT necessitates multifaceted security measures that prioritize communication protected by confidentiality, integrity and authentication services; data inside sensor nodes are encrypted and the network is secured against interruptions and attacks. As a result, the issue of communication security in an IoT network needs to be solved. Even though the IoT network is protected by encryption and authentication, cyber-attacks are still possible. Consequently, it’s crucial to have an intrusion detection system (IDS) technology. In this paper, common and potential security threats to the IoT environment are explored. Then, based on evaluating and contrasting recent studies in the field of IoT intrusion detection, a review regarding the IoT IDSs...
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In the recent years a huge growth has been observed in the area of Internet of Things (IoT). IoT is used as a term where things can communicate with each other without human intervention. Security is one of the key concerns when devices are communicating through Internet. There are many systems that have been made to counter these security issues. In this paper, we have evaluated Machine Learning algorithms on newly created synthetic dataset to counter the IoT security issues. This dataset contains the traces of different types of attacks in IoT. The different classification algorithms are evaluated based on different parameters i.e. accuracy, precision, recall and f-measure. After evaluation we have observed that support vector machine shows better performance on this dataset and also shows 94% accuracy, 0.95 precision, 0.94 recall and 0.94 Fmeasure.
Machine Learning based Intrusion Detection for Cyber-Security in IoT Networks
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IoT network is a promising technology, IoT implementation is growing rapidly but cybersecurity is still a loophole, detection of attacks in IOT infrastructures is a growing concern in the field of IoT. With the increased use of Internet of Things in different areas, cyber-attacks are also increasing proportionately and can cause failures in the system. IDS becomes the leading security solution. Anomaly based network intrusion detection (IDS) detection plays a major role in protecting networks against various malicious activities. Improving the security of loT networks has become one of the most critical issues. This is due to the large-scale development and deployment of loT devices and the insufficiency of Intrusion Detection Systems (IDS) to be deployed for the use of special purpose networks. In this article, the performance of several machine learning models has been compared to accurately predict attacks on IoT systems, the case of imbalanced classes was subsequently treated us...
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The evolution of communications systems with the advent of IoT is leading to an increase in attacks against them. This is due to the fact that the security of connected objects in the IoT is an emerging area which still requires preventive solutions against various attacks. At the network security level, Intrusion Detection Systems (IDS) are used to analyze network data and detect abnormal behavior in the network. In this work, we implemented different machine learning models to build an intrusion detection system based on the UNSW NB15 dataset. To do this, we did data cleaning and feature engineering on the data in the pre-processing phase. Then we used various models such as logistic regression, support vector machine (SVM) classifier, decision tree, random forest, eXtreme Gradient Boosting (XGBoost) in order to predict attacks. Finally, an intrusion detection system is trained on various machine learning algorithms and we selected the most effective model. Experiments were carried out on the UNSW-NB15 dataset and subsequently we compared other machine learning algorithms, and this means that the random forest model on important parameters has a clear advantage in the detection of rare abnormal behaviors.
A novel intrusion detection framework for optimizing IoT security
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The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion detection systems (IDS) to mitigate security risks effectively. However, existing approaches often struggle with adaptability to emerging threats and fail to account for IoT-specific complexities. To address these challenges, this study proposes a novel approach by hybridizing convolutional neural network (CNN) and gated recurrent unit (GRU) architectures tailored for IoT intrusion detection. This hybrid model excels in capturing intricate features and learning relational aspects crucial in IoT security. Moreover, we integrate the feature-weighted synthetic minority oversampling technique (FW-SMOTE) to handle imbalanced datasets, which commonly afflict intrusion detection tasks. Validation using the IoTID20 dataset, designed to emulate IoT environments, yields exceptional results with 99.60% accuracy in attack detection, surpassing existing benchmarks. Additionally, evaluation on the network domain dataset, UNSW-NB15, demonstrates robust performance with 99.16% accuracy, highlighting the model's applicability across diverse datasets. This innovative approach not only addresses current limitations in IoT intrusion detection but also establishes new benchmarks in terms of accuracy and adaptability. The findings underscore its potential as a versatile and effective solution for safeguarding IoT ecosystems against evolving security threats.