000			01425nam a2200169 a 4500
005			20260901030328.0
008			250101s2020 xx o 000 0 eng d
100	1		_aMohammadNoor Injadat
245	1	0	_aDetecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach
264		1	_barXiv _c2020
336			_atext
338			_aonline resource
520			_aThe 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
506	0		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/2012.11325v1 _yRead the full paper (PDF)
942			_cERES
999			_c640 _d640