Mar 1, 2025 · Current detection methods, although effective, often face challenges in accuracy. This work proposes a new framework for IoT botnet detection utilizing an optimized hybrid classification technique. The framework comprises two primary phases: feature extraction and attack detection.

Apr 18, 2024 · Implementing a Comprehensive Node-Level Intrusion Detection System (IDS) Framework for IoT networks presents several practical challenges and requires careful consideration of potential mitigation strategies.

Oct 15, 2020 · We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam....

In this paper, we propose and evaluate a novel framework, the IoT-BDA framework, for automated capturing, analysis, identification, and reporting of IoT botnets.

In this paper, we propose and evaluate a novel framework, the IoT-BDA framework, for automated capturing, analysis, identification, and reporting of IoT botnets.

Jun 29, 2022 · To build a Real-time dataset in IoT botnet (RI_BoT) with Raspberry pi module and Real-time configured architecture. To review and classify several machine learning algorithms for the classification of performance between RI_BoT …

May 25, 2021 · In this paper, cooperative game theory in combination with three approaches—long short term memory (LSTM), Autoencoder, and support vector machine (SVM)—are applied to detect IoT botnet attacks.

Detecting and classifying malicious traffic is key to ensure the security of those systems. This paper implements a distributed framework based on deep learning (DL) to prevent...

Sep 1, 2021 · We propose a statistical learning-based anomaly detection framework, named IoTBoT-IDS, which can effectively discover botnets from networks of smart cities. We develop a lightweight statistical feature extraction model that represents the network data and improves botnet classification.

To that end, this paper proposes an optimized ML-based framework consisting of a combination of Bayesian optimization Gaussian Process (BO-GP) algorithm and decision tree (DT) classification model to detect attacks on IoT devices in an effective and efficient manner.