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Botnet‐based IoT network traffic analysis using deep learning

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols

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A Taxonomy of Side Channel Attacks on Critical Infrastructures and Relevant Systems

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Practical Techniques for Securing the Internet of Things (IoT) Against Side Channel Attacks

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IoT Botnet: The Largest Threat to the IoT Network

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Side-Channel Attack in Internet of Things: A Survey

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Cyber Threat Intelligence Sharing Scheme Based on Federated Learning for Network Intrusion Detection

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Effective network intrusion detection by addressing class imbalance with deep neural networks multimedia tools and applications

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Distributed denial of service attacks and its defenses in IoT: a survey

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Hybrid intrusion detection model based on a designed autoencoder

inferredcites →https://doi.org/10.1016/j.comnet.2018.03.012openalex-snapshot

Internet of things security: A top-down survey

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Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges

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Federated learning for malware detection in IoT devices

inferredcites →https://doi.org/10.1016/j.comnet.2023.109725openalex-snapshot

Real-time bot infection detection system using DNS fingerprinting and machine-learning

inferredcites →https://doi.org/10.1016/j.comnet.2023.110015openalex-snapshot

DDoS attacks in Industrial IoT: A survey

inferredcites →https://doi.org/10.1016/j.compeleceng.2022.107810openalex-snapshot

Anomaly-based intrusion detection system for IoT networks through deep learning model

inferredcites →https://doi.org/10.1016/j.cose.2011.12.012openalex-snapshot

Toward developing a systematic approach to generate benchmark datasets for intrusion detection

inferredcites →https://doi.org/10.1016/j.cose.2019.05.019openalex-snapshot

Issues and challenges in DNS based botnet detection: A survey

inferredcites →https://doi.org/10.1016/j.dcan.2022.02.007openalex-snapshot

Enhancing IoT anomaly detection performance for federated learning

inferredcites →https://doi.org/10.1016/j.diin.2018.12.005openalex-snapshot

Detecting bot-infected machines using DNS fingerprinting

inferredcites →https://doi.org/10.1016/j.future.2019.05.041openalex-snapshot

Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

inferredcites →https://doi.org/10.1016/j.ins.2018.04.065openalex-snapshot

Modeling and clustering attacker activities in IoT through machine learning techniques

inferredcites →https://doi.org/10.1016/j.iot.2022.100564openalex-snapshot

A survey on security in internet of things with a focus on the impact of emerging technologies

inferredcites →https://doi.org/10.1016/j.jii.2016.03.004openalex-snapshot

The internet of things in healthcare: An overview

inferredcites →https://doi.org/10.1016/j.jisa.2018.05.002openalex-snapshot

Identification of malicious activities in industrial internet of things based on deep learning models

inferredcites →https://doi.org/10.1016/j.jnca.2013.08.001openalex-snapshot

Network attacks: Taxonomy, tools and systems

inferredcites →https://doi.org/10.1016/j.jnca.2019.102481openalex-snapshot

A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT

inferredcites →https://doi.org/10.1016/j.jnca.2020.102662openalex-snapshot

Detecting Internet of Things attacks using distributed deep learning

inferredcites →https://doi.org/10.1016/j.procs.2020.03.367openalex-snapshot

Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT

inferredcites →https://doi.org/10.1016/j.sysarc.2021.102143openalex-snapshot

The evolution of IoT Malwares, from 2008 to 2019: Survey, taxonomy, process simulator and perspectives

inferredcites →https://doi.org/10.1016/s1353-4858(19)30037-6openalex-snapshot

The growth of the hide and seek botnet

inferredcites →https://doi.org/10.1016/s1361-3723(20)30082-8openalex-snapshot

ESET: Threat Report Q2 2020

inferredcites →https://doi.org/10.1080/17517575.2019.1644673openalex-snapshot

DNS rule-based schema to botnet detection

inferredcites →https://doi.org/10.1080/19393555.2015.1075629openalex-snapshot

An Overview of Modern Botnets

inferredcites →https://doi.org/10.1109/access.2019.2916717openalex-snapshot

Forensics and Deep Learning Mechanisms for Botnets in Internet of Things: A Survey of Challenges and Solutions

inferredcites →https://doi.org/10.1109/access.2020.3039985openalex-snapshot

Systematic Literature Review on IoT-Based Botnet Attack

inferredcites →https://doi.org/10.1109/cit.2007.90openalex-snapshot

Botnet Detection by Monitoring Group Activities in DNS Traffic

inferredcites →https://doi.org/10.1109/comst.2015.2444095openalex-snapshot

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

inferredcites →https://doi.org/10.1109/comst.2015.2457491openalex-snapshot

Botnet in DDoS Attacks: Trends and Challenges

inferredcites →https://doi.org/10.1109/comst.2018.2844341openalex-snapshot

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

inferredcites →https://doi.org/10.1109/dsc54232.2022.9888913openalex-snapshot

IoT Botnet Detection Based on the Behaviors of DNS Queries

inferredcites →https://doi.org/10.1109/fit.2012.53openalex-snapshot

Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges

inferredcites →https://doi.org/10.1109/globecom38437.2019.9014337openalex-snapshot

Analyzing Adversarial Attacks against Deep Learning for Intrusion Detection in IoT Networks

inferredcites →https://doi.org/10.1109/i-smac.2017.8058363openalex-snapshot

Security attacks in IoT: A survey

inferredcites →https://doi.org/10.1109/ic-etite47903.2020.id-70openalex-snapshot

A Survey on Botnet Detection Techniques

inferredcites →https://doi.org/10.1109/icacte16799.2010openalex-snapshot

2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)

inferredcites →https://doi.org/10.1109/iccmc.2019.8819688openalex-snapshot

Framework for Detection of Malicious Activities in IoT Networks using Keras Deep Learning Library

inferredcites →https://doi.org/10.1109/iccsit.2010.5563555openalex-snapshot

A taxonomy of Botnet detection techniques

inferredcites →https://doi.org/10.1109/icm50269.2020.9331819openalex-snapshot

Optimized Random Forest Model for Botnet Detection Based on DNS Queries

inferredcites →https://doi.org/10.1109/icmla.2018.00171openalex-snapshot

Unsupervised Anomaly Based Botnet Detection in IoT Networks

inferredcites →https://doi.org/10.1109/icmt.2011.6002149openalex-snapshot

Study and application on the architecture and key technologies for IOT

inferredcites →https://doi.org/10.1109/ijcnn.2018.8489489openalex-snapshot

Botnet Detection in the Internet of Things using Deep Learning Approaches

inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207171openalex-snapshot

IoT Attack Detection with Deep Learning Analysis

inferredcites →https://doi.org/10.1109/infocomwkshps50562.2020.9162668openalex-snapshot

Network Flow based IoT Botnet Attack Detection using Deep Learning

inferredcites →https://doi.org/10.1109/iot52625.2021.9469605openalex-snapshot

Anomaly detection in Internet of Things using feature selection and classification based on Logistic Regression and Artificial Neural Network on N-BaIoT dataset

inferredcites →https://doi.org/10.1109/iscc.2015.7405513openalex-snapshot

Internet of Things: Security vulnerabilities and challenges

inferredcites →https://doi.org/10.1109/isncc49221.2020.9297344openalex-snapshot

Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs

inferredcites →https://doi.org/10.1109/itme.2016.0087openalex-snapshot

A Denial of Service Attack Method for an IoT System

inferredcites →https://doi.org/10.1109/iwcmc58020.2023.10182422openalex-snapshot

IoT botnet detection using deep learning

inferredcites →https://doi.org/10.1109/jiot.2017.2694844openalex-snapshot

A Survey on Security and Privacy Issues in Internet-of-Things

inferredcites →https://doi.org/10.1109/jiot.2021.3077803openalex-snapshot

Federated-Learning-Based Anomaly Detection for IoT Security Attacks

inferredcites →https://doi.org/10.1109/mc.2017.201openalex-snapshot

DDoS in the IoT: Mirai and Other Botnets

inferredcites →https://doi.org/10.1109/mc.2017.62openalex-snapshot

Botnets and Internet of Things Security

inferredcites →https://doi.org/10.1109/milcom.2017.8170867openalex-snapshot

The Mirai botnet and the IoT Zombie Armies

inferredcites →https://doi.org/10.1109/mprv.2018.03367731openalex-snapshot

N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders

inferredcites →https://doi.org/10.1109/mwc.2017.1600421openalex-snapshot

Internet of Things Architecture: Recent Advances, Taxonomy, Requirements, and Open Challenges

inferredcites →https://doi.org/10.1109/nca.2018.8548330openalex-snapshot

Monitoring IoT Networks for Botnet Activity

inferredcites →https://doi.org/10.1109/percomw.2019.8730787openalex-snapshot

Protecting IoT-environments against Traffic Analysis Attacks with Traffic Morphing

inferredcites →https://doi.org/10.1109/qrs-c.2019.00088openalex-snapshot

10 Years of IoT Malware: A Feature-Based Taxonomy

inferredcites →https://doi.org/10.1109/securware.2009.48openalex-snapshot

A Survey of Botnet and Botnet Detection

inferredcites →https://doi.org/10.1109/tc.2013.13openalex-snapshot

A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns

inferredcites →https://doi.org/10.1109/tetc.2016.2606384openalex-snapshot

A Comprehensive Study of Security of Internet-of-Things

inferredcites →https://doi.org/10.1109/tii.2021.3088938openalex-snapshot

FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT

inferredcites →https://doi.org/10.1109/wcnc45663.2020.9120761openalex-snapshot

Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0

inferredcites →https://doi.org/10.1109/wf-iot.2019.8767194openalex-snapshot

EDIMA: Early Detection of IoT Malware Network Activity Using Machine Learning Techniques

inferredcites →https://doi.org/10.1145/3292006.3300024openalex-snapshot

Detection of Threats to IoT Devices using Scalable VPN-forwarded Honeypots

inferredcites →https://doi.org/10.1145/3603257openalex-snapshot

FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning

inferredcites →https://doi.org/10.1155/2018/7178164openalex-snapshot

DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation

inferredcites →https://doi.org/10.1155/2020/6689134openalex-snapshot

Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Efficient Feature Engineering

inferredcites →https://doi.org/10.1155/2021/6640499openalex-snapshot

Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

inferredcites →https://doi.org/10.1155/2021/7154587openalex-snapshot

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

inferredcites →https://doi.org/10.11591/ijai.v10.i1.pp110-120openalex-snapshot

Toward a deep learning-based intrusion detection system for IoT against botnet attacks

inferredcites →https://doi.org/10.1186/s40064-016-3569-3openalex-snapshot

A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems

inferredcites →https://doi.org/10.1186/s40537-023-00694-8openalex-snapshot

IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset

inferredcites →https://doi.org/10.1201/b20614openalex-snapshot

DDoS Attacks

inferredcites →https://doi.org/10.1631/jzus.c1300242openalex-snapshot

Botnet detection techniques: review, future trends, and issues

inferredcites →https://doi.org/10.23919/chicc.2019.8866088openalex-snapshot

Detection of IoT Botnet Based on Deep Learning

inferredcites →https://doi.org/10.32628/cseit1835141openalex-snapshot

IOT Malware : An Analysis of IOT Device Hijacking

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DeepDCA: Novel Network-Based Detection of IoT Attacks Using Artificial Immune System

inferredcites →https://doi.org/10.3390/app11125713openalex-snapshot

Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research

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A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection

inferredcites →https://doi.org/10.3390/electronics10091104openalex-snapshot

Memory-Efficient Deep Learning for Botnet Attack Detection in IoT Networks

inferredcites →https://doi.org/10.3390/electronics11091502openalex-snapshot

Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects

inferredcites →https://doi.org/10.3390/electronics12143103openalex-snapshot

DDoS Attack Detection in IoT-Based Networks Using Machine Learning Models: A Survey and Research Directions

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A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries

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Fog-Assisted Deep-Learning-Empowered Intrusion Detection System for RPL-Based Resource-Constrained Smart Industries

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A Hybrid Deep Learning Approach for Intrusion Detection in IoT Networks

inferredcites →https://doi.org/10.52866/ijcsm.2022.01.01.009openalex-snapshot

A Survey of Intrusion Detection Using Deep Learning in Internet of Things

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Attack Classification Analysis of IoT Network via Deep Learning Approach

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