01425nam a2200169 a 4500 20260901030328.0 250101s2020 xx o 000 0 eng d MohammadNoor Injadat Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach arXiv 2020 text online resource The increased reliance on the Internet and the corresponding surge in connectivity demand has led to a significant growth in Internet-of-Things (IoT) devices. The continued deployment of IoT devices has in turn led to an increase in network attacks due to the larger number of potential attack surfaces as illustrated by the recent reports that IoT malware attacks increased by 215.7% from 10.3 million in 2017 to 32.7 million in 2018. This illustrates the increased vulnerability and susceptibility of IoT devices and networks. Therefore, there is a need for proper effective and efficient attack detection and mitigation techniques in such environments. Machine learning (ML) has emerged as one potential solution due to the abundance of data generated and available for IoT devices and networks. Hence, they have significant potential to be adopted for intrusion detection for IoT environments. To Open access — freely available to read. https://arxiv.org/pdf/2012.11325v1 Read the full paper (PDF) ERES 640 640 0 0 0 3 CYB MAIN MAIN SCICOMP 2026-09-01 0 YGE000901 2026-09-01 03:03:28 https://arxiv.org/pdf/2012.11325v1 2026-09-01 PAPER