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A State-of-the-Art Review on IoT botnet Attack Detection [article]

Zainab Al-Othman, Mouhammd Alkasassbeh, Sherenaz AL-Haj Baddar
2020 arXiv   pre-print
Despite their versatility, several IoT devices are vulnerable from a security perspective, which renders them as a favorable target for multiple security breaches, especially botnet attacks.  ...  In this study, the conceptual frameworks of IoT botnet attacks will be explored, alongside several machinelearning based botnet detection techniques.  ...  studies opted for one family of classification algorithms when they designed their models. • Few studies shed enough light on their feature selection approaches or utilized mainly a single feature selection  ... 
arXiv:2010.13852v1 fatcat:ejvymnp6yfhudo4dkv7k26zbzy

Hardening Machine Learning Denial of Service (DoS) Defences Against Adversarial Attacks in IoT Smart Home Networks

Eirini Anthi, Lowri Williams, Amir Javed, Pete Burnap
2021 Computers & security  
Given the impact that these attacks may have, this paper proposes a rule-based approach towards generating AML attack samples and explores how they can be used to target a range of supervised machine learning  ...  classifiers used for detecting Denial of Service attacks in an IoT smart home network.  ...  for detecting cyber attacks in IoT.  ... 
doi:10.1016/j.cose.2021.102352 fatcat:3p7od6dpnraonbyb3j6ldmytna

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection

Mohammed Al-Sarem, Faisal Saeed, Eman H. Alkhammash, Norah Saleh Alghamdi
2021 Sensors  
A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of "bot" devices for generating  ...  This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks.  ...  and "Bashlite" or "Miria" attack types for multi-class classification.  ... 
doi:10.3390/s22010185 pmid:35009725 pmcid:PMC8749651 fatcat:tnn4t5y37ng3flaq7a2yhg6rui

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

Mujaheed Abdullahi, Yahia Baashar, Hitham Alhussian, Ayed Alwadain, Norshakirah Aziz, Luiz Fernando Capretz, Said Jadid Abdulkadir
2022 Electronics  
This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks.  ...  In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment.  ...  In addition, AI approaches explore IoT data features extraction and provide intrusion detection systems with feature extractions for smart cities based on deep migration learning model [74] .  ... 
doi:10.3390/electronics11020198 fatcat:fzqm47exe5dgjeqlxjhagrypoa

Developing an efficient feature engineering and machine learning model for detecting IoT-Botnet cyber attacks

Mrutyunjaya Panda, Abd Allah A. Mousa, Aboul Ella Hassanien
2021 IEEE Access  
classification using hybrid K-means clustering and genetic algorithm (HGC) on the reduced features for optimal detection of IoT botnet attacks.  ...  To explore further in this emerging area of research, this paper aims to apply feature engineering and machine learning techniques for the efficient detection of IoT botnet attacks in an IoT anomaly detection  ...  Author Name: Preparation of Papers for IEEE Access (February 2017) Author Name: Preparation of Papers for IEEE Access (February 2017) Author Name: Preparation of Papers for IEEE Access (February 2017)  ... 
doi:10.1109/access.2021.3092054 fatcat:imq74uy7cng47fgx33iq5qv25i

Computational Intelligence Approaches in Developing Cyberattack Detection System

Mohammed Saeed Alzahrani, Fawaz Waselallah Alsaade, Deepika Koundal
2022 Computational Intelligence and Neuroscience  
classification was 98.55% and 97.28%, whereas the KNN and LSTM attained a high accuracy for multiple classification (98.28% and 970.7%).  ...  As a result, nine features were selected. A one-hot encoding method was applied to convert the categorical features into numerical features.  ...  Acknowledgments e authors extend their appreciation to the Deanship of Scientific Research at King Faisal University for funding this research work through the project no. NA00078.  ... 
doi:10.1155/2022/4705325 pmid:35341179 pmcid:PMC8956412 fatcat:ctns5b3zwfep3itkgxqy6zwdly

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT [article]

Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
2022 arXiv   pre-print
In this paper, we propose E-GraphSAGE, a GNN approach that allows capturing both the edge features of a graph as well as the topological information for network intrusion detection in IoT networks.  ...  To the best of our knowledge, our proposal is the first successful, practical, and extensively evaluated approach of applying GNNs on the problem of network intrusion detection for IoT using flow-based  ...  However, these approaches mainly focus on node features for node classification, and are currently unable to consider edge features for edge classification.  ... 
arXiv:2103.16329v8 fatcat:tvjjjufp6bglpnm23645rxb3he

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

Md Mamunur Rashid, Joarder Kamruzzaman, Mohammad Mehedi Hassan, Tasadduq Imam, Steven Gordon
2020 International Journal of Environmental Research and Public Health  
Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure.  ...  Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature.  ...  In this paper, we explore a machine learning-based attack and anomaly detection technique in IoT-based smart city applications.  ... 
doi:10.3390/ijerph17249347 pmid:33327468 pmcid:PMC7764956 fatcat:syieikibrvfexjbpg7u5nzf5hm


Nicholas Oluwole Ogini, Wilfred Adigwe, Noah Oghenefego Ogwara
2022 Zenodo  
The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power.  ...  DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services.  ...  In addition, the use of ensemble ML approach has not been explored widely in the detection of DDoS attacks in IoT environment in extant literature and also explored other evaluation metrics to measure  ... 
doi:10.5281/zenodo.7002129 fatcat:6i633jsurzanlk422hdmfmjjii

Survey of Cyber security approaches for Attack Detection and Prevention

MalathiEswaran, Et. al.
2021 Turkish Journal of Computer and Mathematics Education  
This proposed survey focuses on types of attacks and also the methodology involved in detecting such type of attacks.  ...  Since the network has been utilized in communication across the world and also in data sharing, there may be a chance of cyber-attacks and intruding into the personal data of the user.  ...  Also, the feature extraction and classification algorithm specified in this survey helps in choosing a better one in anomaly detection of cyber-attacks in IoT.  ... 
doi:10.17762/turcomat.v12i2.2406 fatcat:gnilkdbry5dtzh6jltpxzlmurq

IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Mnahi Alqahtani, Hassan Mathkour, Mohamed Maher Ben Ismail
2020 Sensors  
Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community.  ...  In this paper, we propose an efficient and effective IoT botnet attack detection approach.  ...  Acknowledgments: The authors would like to thank Deanship of Scientific Research (DSR) at King Saud University for funding and supporting this research through the initiative of the Graduate Students Research  ... 
doi:10.3390/s20216336 pmid:33172023 fatcat:onkiomggtjcerobqvgtyoz7pte

A Report on Botnet Detection Techniques for Intrusion Detection Systems

Sathya D
2022 International Journal for Research in Applied Science and Engineering Technology  
The report presents a survey of various techniques of botnet detection models built using several types of machine learning techniques.  ...  The peer-to-peer attack takes place to by passing botnet attacks from one system to another in a peer-to-peer network while the command-and-control attack takes place by a botmaster attack on a server  ...  For feature reduction, the feature selection is done based on the information gain is obtained. The random forests classifier is a part of the framework that is used for classification.  ... 
doi:10.22214/ijraset.2022.44253 fatcat:iifsrkznwvbc7dt55aghv3t6li

İkili Gri Kurt Optimizasyonu (BGWO) ve Naive Bayes (NB) Kullanılarak Yeni Bir Hibrit IoT Tabanlı IDS

Erkan ÜLKER, Ismail Mohamed NUR
2020 European Journal of Science and Technology  
BGWO is used as feature selection and NB as a classification method. The results are compared with other optimization algorithms. The BoT-IoT data set is used as an experimental data set.  ...  The Internet of Things (IoT) model has newly evolved into the technology for establishing smart environments.  ...  BGWO used as feature selection and NB as classification method. BoT-IoT is one of the most up-to-date data set for intrusion detection.  ... 
doi:10.31590/ejosat.804113 fatcat:qtlew3w7j5ggnlytz2t7vsh47i

Intrusion Detection in the Internet of Things

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The IoT has been booming in recent years and is evolving rapidly, but attacks against it are also continuing to evolve in a worrying way.  ...  The objective of this paper is to provide a general study on IoT IDS and implementation techniques based on IDS specifically classical methods as well as learning methods.  ...  way for a significant illustration of the features.  ... 
doi:10.30534/ijatcse/2020/0191.52020 fatcat:otprcykv2fgmxhlw7cf5nletwq

Robust Intelligent Malware Detection using Light GBM Algorithm

Lastly, the results show the ability of the proposed approach to detect IoT botnet attacks fast, which is a vital feature to end botnet activity before spreading to any new network device.  ...  For examination reasons, the suggested approach serves the LightGBM machine learning algorithm to adopt datasets obtained from real IoT devices using the LightGBM machine learning algorithm.  ...  A predefined dataset [15] related to IoT heterogeneous devices connected to a network used for evaluating our proposed approach.  ... 
doi:10.35940/ijitee.f4043.049620 fatcat:zwhpmud2mbg2ti6pbyx5r3xvfa
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