{"title":"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","authors":"Sunil Sonawane, Reshma R. Gulwani, Pooja Sharma","doi":"10.3233/web-230214","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":42775,"journal":{"name":"Web Intelligence","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fractional hunger jellyfish search optimization based deep quantum neural network for malicious traffic segregation and attack detection\",\"authors\":\"Sunil Sonawane, Reshma R. Gulwani, Pooja Sharma\",\"doi\":\"10.3233/web-230214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":42775,\"journal\":{\"name\":\"Web Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Web Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/web-230214\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Web Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/web-230214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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[...]