ProFiOt: Abnormal Behavior Profiling (ABP) of IoT devices based on a machine learning approach

Lee, Sooyeon; Wi, Sarang; Seo, Eunil; Jung, Jean; Chung, Tai-Myoung

doi:10.1109/atnac.2017.8215434

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…In a previous study [238], the abnormal behaviour of IoT objects was profiled, and the generated dataset from profiling was used to train the classifier to detect abnormal behaviour. The author investigated how a partial variation (assuming that the attacker can utilise such changes for malicious purposes) of sensed data can influence the accuracy of the learning algorithm and used SVM and k-means clustering as experimental cases for examining the impact of such changes on the detection accuracy of both ML algorithms.…”

Section: B Network Layermentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

839

333

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The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT technologies play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT ecosystem effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. The ability to monitor IoT devices intelligently provides a significant solution to new or zero-day attacks. ML/DL are powerful methods of data exploration for learning about 'normal' and 'abnormal' behaviour according to how IoT components and devices perform within the IoT environment. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems.IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

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Section: B Network Layermentioning

confidence: 99%

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Al-Garadi

Mohamed

Al-Ali

et al. 2020

IEEE Commun. Surv. Tutorials

839

333

View full text Add to dashboard Cite

show abstract

“…Lee et al [ 88 ] come up with profiling of abnormal activities of IoT devices via the support of a variety of machine learning algorithms. The approach considers signal injection as a threat to IoT and hence finds it as a principal attack in his research.…”

Section: Learning-based Solutions For Securing Iotmentioning

confidence: 99%

Internet of Things: Evolution, Concerns and Security Challenges

Malhotra

Singh

Anand

et al. 2021

Sensors

156

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The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

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“…This approach can be applied when the communication parties share encryption/decryption keys. In symmetric encryption (i.e., block ciphers, stream ciphers and hash functions), the key must be pre- Perception layer Device security -High computation cost -Communication cost is not considered -Storage cost is not considered [103] Network layer Device security -Medium computation cost -Medium communication cost -Storage cost is not considered [104] Network layer Device security -High computation cost -Communication cost is not considered -Storage cost is not considered [105] Network layer Device security -Medium computation cost -Communication cost is not considered -Storage cost is not considered [106] Network layer Device security -Medium computation cost -Communication cost is not considered -Storage cost is not considered [107] Perception layer Device security -High computation cost -Communication cost is not considered -Storage cost is not considered distributed or securely communicated. However, in scalable IoT environments, key management including distribution, agreement, update and revocation remains a meaningful task.…”

Section: Security Challenges and Future Directionsmentioning

confidence: 99%

Recent Security Trends in Internet of Things: A Comprehensive Survey

et al. 2021

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The Internet of Things (IoT) aims to transform everyday physical objects into an interconnected ecosystem with digital data accessible anywhere and anytime. "Things" in IoT are embedded with sensing, processing and actuating capabilities and cooperate in providing smart and innovative services autonomously. The rapid spread of IoT services arises different security vulnerabilities that need to be carefully addressed. Several emerging and promising technologies and techniques are introduced to improve the security of IoT. This paper aims to provide an up-to-date vision of the current research topics related to IoT security. Initially, we introduce common elements and protocols of IoT to demystify the origins of threats in IoT. Then, we propose a taxonomy of IoT attacks and analyze the security vulnerabilities of IoT at different layers. Subsequently, we povide a comparison of recent security schemes based on emerging solutions including fog computing, edge computing, software defined networking (SDN), blockchain, lightweight cryptography, homomorphic and searchable encryption, and machine learning. Finally, security challenges are discussed and future directions are highlighted for future interested researchers.INDEX TERMS Blockchain, edge computing, fog computing, IoT, lightweight cryptography, machine learning, SDN.

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ProFiOt: Abnormal Behavior Profiling (ABP) of IoT devices based on a machine learning approach

Cited by 26 publications

References 5 publications

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Internet of Things: Evolution, Concerns and Security Challenges

Recent Security Trends in Internet of Things: A Comprehensive Survey

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