Anomaly Detection in IoT Networks: From Architectures to Machine Learning Transparency

Huc, A.; Trček, Denis

doi:10.1109/access.2021.3073785

Cited by 10 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But we have to be mindful of the diminishing importance of packet-payload analysis, due to the increasing use of packet-payload encryption. The results were also published in a peer-reviewed journal paper [5].…”

Section: Discussionmentioning

confidence: 99%

Detecting Temporal and Spatial Anomalies in Users’ Activities for Security Provisioning in Computer Networks

Huč

2023

IJCAI

View full text Add to dashboard Cite

The paper summarizes a Doctoral Thesis that focuses on two new approaches for detecting anomalies in computer networks based on network flows. The approaches use incremental hierarchical clustering algorithms and monitor changes in the data structures to detect anomalies. Both approaches achieved prediction performance comparable to the state-of-the-art supervised approaches (F1 score over 0.90), even when taking into account that our approaches see every data point only once and then discard it and they operate without the prerequisite learning phase with labeled data.Povzetek: Članek povzema vsebino doktorske disertacije, v kateri se osredotočimo na dva nova pristopa za detekcijo anomalij v računalniških omrežjih. Pristopa temeljita na omrežnih tokovih, inkrementalnem hierarhičnem gručenju in spremljanju sprememb v podatkovnih strukturah z namenom detekcije anomalij. Oba pristopa dosežeta primerljivo stopnjo detekcije (mera F1 preko 0.90) v primerjavi z najnovejšimi nadzorovanimi metodami, tudi ko upoštevamo, da naša pristopa vidita vsak podatek le enkrat in ga nato pozabita ter delujeta brez predhodne faze učenja z označenimi podatki.

show abstract

Section: Discussionmentioning

confidence: 99%

Detecting Temporal and Spatial Anomalies in Users’ Activities for Security Provisioning in Computer Networks

Huč

2023

IJCAI

View full text Add to dashboard Cite

show abstract

“…[176], [230] [77] UNSW-NB15 Intrusion Detection 175,341 training 82,332 testing 49 [125], [132], [143], [154], [157], [161], [162], [171], [172], [202], [205], [225], [234] [78] CICIDS2017 Intrusion Detection 30,540 80 [110], [115], [117], [122], [127], [153] [159], [166], [186] [79] N-BaIoT Botnet Detection 7062606 115 [126], [152], [177], [179], [183],…”

Section: Machine Learning and Deep Learningmentioning

confidence: 99%

“…IoT Networks in general Network traffic [105], [107], [109], [110], [111], [115], [116], [118], [119], [120], [126], [127], [129], [132], [136], [137], [139], [140], [142], [145], [146], [147], [149], [151], [153], [154], [158], [159], [162], [165], [166], [175], [178], [182], [183], [185], [186], [187], [190], [192], [194], [195], [196], [199], [200], [201], [202], [204],…”

mentioning

confidence: 99%

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Harahsheh,

Chen

2023

IJCAI

View full text Add to dashboard Cite

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration.Povzetek: Podan je pregled uporabe strojnega in globokega učenja v IoT napravah ter izzivov, rešitev in prihodnjih smeri raziskav.Informatica 47 (2023) 1-54 k. Harahsheh et al.

show abstract

An efficient faults and attacks categorization model in IoT-based cyber physical systems using Dilated CNN and BiLSTM with multi-scale dense Attention module

Laythkhaleel,

Abdu Ibrahim,

Awad Shaban Naseri

et al. 2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Anomaly Detection in IoT Networks: From Architectures to Machine Learning Transparency

Cited by 10 publications

References 18 publications

Detecting Temporal and Spatial Anomalies in Users’ Activities for Security Provisioning in Computer Networks

Detecting Temporal and Spatial Anomalies in Users’ Activities for Security Provisioning in Computer Networks

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

An efficient faults and attacks categorization model in IoT-based cyber physical systems using Dilated CNN and BiLSTM with multi-scale dense Attention module

Contact Info

Product

Resources

About