Machine Learning DDoS Detection for Consumer Internet of Things Devices

An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g. limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.Comment: 7 pages, 3 figures, 3 tables, appears in the 2018 Workshop on Deep Learning and Security (DLS '18

Security of Internet of Things (IoT) Using Federated Learning and Deep Learning — Recent Advancements, Issues and Prospects

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. In this context, this review focuses on the implementation of federated learning (FL) and deep learning (DL) algorithms for IoT security. Unlike conventional ML techniques, FL models can maintain the privacy of data while sharing information with other systems. The study suggests that FL can overcome the drawbacks of conventional ML techniques in terms of maintaining the privacy of data while sharing information with other systems. The study discusses different models, overview, comparisons, and summarization of FL and DL-based techniques for IoT security

Towards Effectiveness Of Detecting IoT Botnet Attacks Using Supervised Machine Learning

With the rapid explosion of Internet of Things (IoT), IoT enabled devices have reached every corner of the globe, connecting billions of devices to the global internet. Botnets remain as one that can profit the most from IoT security susceptibility among other threats. Machine Learning based techniques to detect malicious traffic has been a widely researched topic in the last few years. Most of the studies simply use these techniques in an isolated fashion, where the machine learning algorithms are trained and tested on the same stream of labeled traffic. This study will attempt to analyze the use of supervised machine learning technique from practical aspects. To be practical and effective, a machine learning algorithm should be able to go beyond that and be able to classify traffic coming from a different sources, networks or software. Moreover we see that network traffic based datasets are usually in the order of millions of records which are then used to train the machine learning models. This study will also explore the impact of training size to find out how much data is actually required to train an effective machine learning model

Machine Learning in IoT Security:Current Solutions and Future Challenges

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security

Safety, security and privacy in machine learning based Internet of Things

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems

A systematic literature review

Bahaa, A., Abdelaziz, A., Sayed, A., Elfangary, L., & Fahmy, H. (2021). Monitoring real time security attacks for iot systems using devsecops: A systematic literature review. Information (Switzerland), 12(4), 1-23. [154]. https://doi.org/10.3390/info12040154In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.publishersversionpublishe

Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks

The rapid proliferation of IOT (Internet of Things) networks has brought transformative benefits to industries and everyday life. However, it has also introduced unprecedented cyber security challenges, necessitating advanced techniques for anomaly detection. This research focuses on enhancing cyber security through the application of machine learning-based anomaly detection methods, specifically One-Class Support Vector Machine (SVM) and Isolation Forest, in the context of IOT networks. While Isolation Forest effectively isolates anomalies by building isolation trees, One-Class SVM models the normal data distribution, effectively separating anomalies. To provide a strong security framework for IoT networks, we suggest a comprehensive strategy that combines both algorithms. Our method enables the detection of anomalies in real-time IOT data streams, facilitating prompt responses to new threats. Data collection, preprocessing, and model training are key components. This study helps protect IOT ecosystems and maintain data integrity and privacy in an increasingly connected world by utilizing the benefits of One-Class SVM and Isolation Forest