Internet of things consumer devices integrated with intrusion detection system based on deep learning for spotting irregularities in traffic patterns involves deep learning algorithms that can learn interpretations of real data at different levels of complexity
	KAVITHA P; POONGODI A; PRIYALAKSHMI V; THILAGAVATHY R
	2025-04-02
专利权人	KAVITHA P (KAVI-Individual) ; POONGODI A (POON-Individual) ; PRIYALAKSHMI V (PRIY-Individual) ; THILAGAVATHY R (THIL-Individual)
申请日期	2025-04-02
专利号	IN202541032479-A
成果简介	NOVELTY - Internet of things (IoT) consumer devices integrated with an intrusion detection system based on deep learning to spot irregularities in traffic patterns involves deep learning algorithms can learn interpretations of real data at different levels of complexity with little assistance from the user. A deep learning-based intrusion detection system (IDS) technique that remote monitors the traffic patterns of a large number of IoT user devices looking for anomalies. A multi-layered protection strategy includes intrusions. USE - Internet of things (IoT) consumer devices integrated with an intrusion detection system based on deep learning for spotting irregularities in traffic patterns. ADVANTAGE - The system reduces the typical security risks and vulnerabilities, this paper addresses network intrusion detection and integrate pertinent characteristic. A vast number of non-linear levels independently produces the features that improve the classification task's generalization.
IPC 分类号	G06F-021/55 ; G06F-021/57 ; G06N-003/08 ; H04L-009/40 ; H04W-012/121
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/13239
专题	中国科学院新疆生态与地理研究所
作者单位	1.KAVITHA P (KAVI-Individual) 2.POONGODI A (POON-Individual) 3.PRIYALAKSHMI V (PRIY-Individual) 4.THILAGAVATHY R (THIL-Individual)
推荐引用方式 GB/T 7714	KAVITHA P,POONGODI A,PRIYALAKSHMI V,et al. Internet of things consumer devices integrated with intrusion detection system based on deep learning for spotting irregularities in traffic patterns involves deep learning algorithms that can learn interpretations of real data at different levels of complexity. IN202541032479-A[P]. 2025.

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