基于深度学习的SDN-IoT网络攻击检测与QoS感知安全路由协议

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Manvitha Gali, Aditya Mahamkali
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引用次数: 0

摘要

物联网网络和基于软件的控制器组成了基于软件定义网络的物联网(SDN-IoT)。SDN-IoT广泛应用于交通控制与管理、智能建筑与家居、安全应用、医疗监控与自动化等多个应用领域。SDN-IoT网络场景中最具挑战性的方面是高效路由和安全问题。因此,提出了一种基于深度学习的攻击检测和qos感知安全路由协议的SDN-IoT新框架。针对用户请求,首先采用深度信念网络(Deep Belief Network, DBN)进行攻击检测。检测到的恶意请求被丢弃,对正常的数据包进行路由处理。本文介绍了基于安全QoS因素的高效路由的非洲Aquila优化算法。基于端到端时延、能耗、网络生命周期、数据包传送率和吞吐量等评估指标对所提出的方法进行分析,得到的值分别为1.86、5.22、1632.84、0.9959和0.96。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Based Attack Detection and QoS Aware Secure Routing Protocol for SDN-IoT Network

The IoT network and the software-based controller comprise the Software-Defined Network-based IoT (SDN-IoT). SDN-IoT is widely utilized in traffic control and management, smart buildings and homes, safety applications, health care monitoring and automation, and several application domains. The most challenging aspects of the SDN-IoT network scenario are efficient routing and security issues. Hence, a novel framework of SDN-IoT with deep learning-based attack detection and a QoS-aware secure routing protocol is proposed. The attack detection is employed initially for the user request using the Deep Belief Network (DBN). The detected malicious request is dropped, and the routing is developed for the normal data packet. Here, African Aquila Optimization is introduced for efficient routing based on secure QoS factors. The analysis of the proposed method based on the assessment measures such as end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput acquired the values of 1.86, 5.22, 1632.84, 0.9959, and 0.96, respectively.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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