Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework

Jullian, Olivia; Otero, Beatriz; Rodríguez, Eva; Gutiérrez, Norma; Antona, Héctor; Canal, Ramón

doi:10.1007/s10922-023-09722-7

Cited by 24 publications

(4 citation statements)

References 23 publications

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“…In this subsection, we provide answers found regarding RQ2: "What potential user authentication cyber threats affect online banking?". Based on the literature [4], [55], [74], [87], [18], [89]- [104] the following are the most serious threats that could face online banking: online banking:…”

Section: B Threat-facing Online Bankingmentioning

confidence: 99%

Online Banking User Authentication Methods: A Systematic Literature Review

Karim,

Khashan,

Kanaker

et al. 2024

IEEE Access

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Online banking has become increasingly popular in recent years, making it a target for cyberattacks. Banks have implemented various user authentication methods to protect their customers' online accounts. This paper reviews the state-of-the-art user authentication methods used in online banking and potential cyber threats. This paper starts by exploring different user authentication methods, such as knowledge-based authentication (KBA), biometrics-based authentication (BBA), possession-based authentication (PBA), and other methods. The advantages and disadvantages of each user authentication method are then discussed. Furthermore, the paper discusses the various cyber threats that can compromise user authentication for online banking systems, such as malware attacks, social engineering, phishing attacks, man-in-the-middle (MiTM) attacks, denial of service (DoS) attacks, session hijacking, weak passwords, keyloggers, SQL injection, and replay attacks. Also, the paper explores the user authentication methods used by popular banks, which can provide insights into best practices for safeguarding online banking accounts and future user authentication methods in online banking and cyber threats. It states that the increasing use of BBA, two-factor authentication (2FA), and multi-factor authentication (MFA) will help improve the security of online banking systems. However, the paper also warns that new cyber challenges will emerge, and banks need to be vigilant in protecting their customers' online banking accounts.

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Section: B Threat-facing Online Bankingmentioning

confidence: 99%

Online Banking User Authentication Methods: A Systematic Literature Review

Karim,

Khashan,

Kanaker

et al. 2024

IEEE Access

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“…Using the NID and BoT-IoT datasets, the model is trained and evaluated. In [92] Long short-term memory and feedforward neural networks have been introduced. The performance and detection of various types of assaults are assessed using two distinct datasets, NSL-KDD and BoT-IoT.…”

Section: B Features Selection Optimizedmentioning

confidence: 99%

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Hind Ali abdul Hassan

2024

jes

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The proliferation of Internet users has coincided with a commensurate increase in the amount of very important, sensitive, and private information being transferred across the Internet. Malicious actors are increasingly targeting networks to breach them and obtain illegal access to critical information since this trend has revealed holes in security systems. In addition to endangering the privacy of the data concerned, these breaches disrupt the smooth functioning of systems. Therefore, in light of these dangers, intrusion detection systems (IDSs) are now an essential part of any cybersecurity program. The goal of these systems is to detect and report any suspicious activity by constantly monitoring and analyzing network traffic. Numerous review articles have investigated various methods for network intrusion detection. To improve detection accuracy while keeping computing efficiency high, this survey study investigates lightweight deep learning techniques for intrusion detection systems. These techniques include pruning, quantization, clustering, and collaborative optimization. This study analyzes five different types of new real-world traffic datasets (i.e., CSE-CIC- IDS2018, NSL-KDD, Bot-IoT, ToN IoT Network, and UNSW-NB15) and evaluates the performance of several machine learning and deep learning techniques. This survey provides metrics for measuring the accuracy of intrusion detection across various systems, which may be used to assess performance.

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“…Jullian et al recommended an evolving distributed system utilizing deep learning (DL) for cyberattack detection [11]. The structure is based on assessing the Long Short-Term Memory (LSTM) and Feed Forward Neural Network (FFNN) computational models for the NSL-KDD and BoT-IoT data sets.…”

Section: Related Workmentioning

confidence: 99%

Enhancing IoT Botnet Detection through Machine Learning-based Feature Selection and Ensemble Models

Sharma,

Mohi ud din,

Sharma

et al. 2023

ICST Transactions on Scalable Information Systems

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An increase in cyberattacks has coincided with the Internet of Things (IoT) expansion. When numerous systems are connected, more botnet attacks are possible. Because botnet attacks are constantly evolving to take advantage of security holes and weaknesses in internet traffic and IoT devices, they must be recognized. Voting ensemble (VE), Ada boost, K-Nearest Neighbour (KNN), and bootstrap aggregation are some methods used in this work for botnet detection. This study aims to first incorporate feature significance for enhanced efficacy, then estimate effectiveness in IoT botnet detection using traditional model-based machine learning, and finally evaluate the outcomes using ensemble models. It has been demonstrated that applying feature importance increases the effectiveness of ensemble models. VE algorithm provides the best botnet traffic detection compared to all currently used approaches.

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Deep-Learning Based Detection for Cyber-Attacks in IoT Networks: A Distributed Attack Detection Framework

Cited by 24 publications

References 23 publications

Online Banking User Authentication Methods: A Systematic Literature Review

Online Banking User Authentication Methods: A Systematic Literature Review

Exploring Lightweight Deep Learning Techniques for Intrusion Detection Systems in IoT Networks: A Survey

Enhancing IoT Botnet Detection through Machine Learning-based Feature Selection and Ensemble Models

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