A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Thaseen, I. Sumaiya; Mohanraj, V.; Sakthivel, R.; Sanapala, Kishore; Yeo, Sang-Soo

doi:10.3390/electronics10161955

Cited by 13 publications

(5 citation statements)

References 59 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar works related to anomaly detection can be found in Internet of Things [46], and automotive systems [47]. In the remainder of this section, however, we focus on privacypreserving anomaly detection.…”

Section: Privacy-preserving Detection Techniquesmentioning

confidence: 86%

Privacy-Preserving Tampering Detection in Automotive Systems

et al. 2021

View full text Add to dashboard Cite

Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data.

show abstract

Section: Privacy-preserving Detection Techniquesmentioning

confidence: 86%

Privacy-Preserving Tampering Detection in Automotive Systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This learning paradigm was considered in several research studies and yielded improved IDS robustness. Thaseen et al [ 32 ] employed ensemble learning methods based on KNN, SVM, and LR and obtained better results compared to the involved baseline approaches. The ensemble model achieved an accuracy of around 99% using the BoT-IoT [ 33 ] and ToN_IoT [ 24 ] datasets.…”

Section: Background and Related Workmentioning

confidence: 99%

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Alkadi,

Al-Ahmadi,

Ben Ismail

2024

Sensors

View full text Add to dashboard Cite

Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well.

show abstract

“…IoT devices are susceptible to security breaches because of their constrained feature and resource sets. One study [ 52 ] sought to find irregularities in IoT devices. It suggested a Hadoop-based architecture for IoT anomaly detection that makes use of ML classifiers, evaluated its efficacy, and contrasted it with other solutions already in place.…”

Section: Machine Learning—iot Network Anomaly Detectionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

View full text Add to dashboard Cite

With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

show abstract

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

Cited by 13 publications

References 59 publications

Privacy-Preserving Tampering Detection in Automotive Systems

Privacy-Preserving Tampering Detection in Automotive Systems

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Contact Info

Product

Resources

About