基于分饥饿水母搜索优化的深度量子神经网络用于恶意流量隔离和攻击检测

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Web Intelligence Pub Date : 2024-07-10 DOI:10.3233/web-230214
Sunil Sonawane, Reshma R. Gulwani, Pooja Sharma
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引用次数: 0

摘要

恶意流量隔离和攻击检测造成了重大经济损失,成为最严重的安全隐患之一。此外,网络安全攻击也是影响网络安全的主要问题。随着技术的发展,网络攻击手段不断升级,如何检测和防范网络攻击仍然是一个重大问题。为此,需要提出一种检测和维护网络安全的有效策略。该作品能及时准确地检测和识别拥塞攻击。在物联网(IoT)云系统中,本研究开发了基于混合优化的深度学习(DL)网络的恶意流量隔离和攻击检测。首先,从物联网传感器的仿真中收集输入日志文件,并通过提出的分数饥饿水母搜索优化(FHGJO)算法选择最优路由。FHGJO 是饥饿游戏水母优化(HGJO)和分数微积分(FC)的集成。此外,HGJO 是饥饿游戏搜索优化(HGS)与水母优化(JSO)的结合。然后,根据适度度量进行分离,并对输入数据进行量化归一化预处理。特征选择过程使用加权欧几里得距离(WED)。通过 SpinalNet,恶意隔离被分为恶意和非恶意两类,拟议的 FHJGO 用于调整 SpinalNet。此外,提议的 FHGJO 训练的深度量子神经网络(DQNN)被用来检测攻击,并将其分为拒绝服务(DOS)攻击、分布式拒绝服务(DDoS)攻击和缓冲区溢出攻击。此外,还使用 NSL-KDD 数据集和 BoT-IoT 数据集对所提出的模型进行了评估。所提出的方法确保了网络安全,准确率为 0.931,灵敏度为 0.923,特异性为 0.936。
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Fractional hunger jellyfish search optimization based deep quantum neural network for malicious traffic segregation and attack detection
Malicious traffic segregation and attack detection caused major financial loss and became one of the most serious security hazards. Moreover, cyber security attack is the major issue, which impacts network security. The network attack methods are constantly being upgraded by the technology development and it remains a major issue for detection and protection against network attacks. For this, it is required to present an effective strategy for detecting and maintaining network security. The work provides timely and accurate congestion attack detection and identification. In the Internet of Things (IoT) cloud system malicious traffic segregation and attack detection based on a hybrid optimization-enabled deep learning (DL) network is developed in this research. At first, the input log files are gathered from the simulation of IoT sensors and the superior route is selected by the proposed Fractional Hunger Jellyfish Search Optimization (FHGJO) algorithm. The FHGJO is the integration of Hunger Game Jelly Fish Optimization (HGJO) and Fractional Calculus (FC). Furthermore, the HGJO is the combination of Hunger Game Search Optimization (HGS) with Jellyfish Optimization (JSO). Then, the segregation is done based on the fitness measures and for preprocessing; the input data is fed using quantile normalization. The feature selection process is employed using the weighted Euclidian distance (WED). With the SpinalNet, the malicious segregation is categorized as malicious and non-malicious and the proposed FHJGO is used to tune the SpinalNet. Furthermore, the proposed FHGJO-trained Deep Quantum Neural Network (DQNN) is utilized to detect the attack and classifies it into a Denial-of-Service (DOS) attack, Distributed Denial of Service (DDoS) attack, and buffer overflow attack. Moreover, the proposed model is evaluated using the NSL-KDD dataset and BoT-IoT dataset. The proposed method ensures network security with 0.931 accuracy, 0.923 sensitivity, and 0.936 specificity.
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
期刊最新文献
Hybrid optimization based deep stacked autoencoder for routing and intrusion detection Fractional hunger jellyfish search optimization based deep quantum neural network for malicious traffic segregation and attack detection Efficient IoT-based heart disease prediction framework with Weight Updated Trans-Bidirectional Long Short Term Memory-Gated Recurrent Unit Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework Business model innovation and creativity impact on entrepreneurship development: An empirical study
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