{"title":"基于 SHAKE-ESDRL 的高能效入侵检测和散列系统","authors":"Geo Francis E, S. Sheeja","doi":"10.1007/s12243-023-00963-w","DOIUrl":null,"url":null,"abstract":"<div><p>Outstanding progress in unsolicited intrusions along with security threats, which interrupt the normal operations of wireless sensor networks (WSNs), have been attracted by the proliferation of WSNs and their applications. In WSNs, this demands an intrusion detection system (IDS), which can detect such attacks with higher detection accuracy. Designing an effective model for IDS using the SDK-LSHB-based SHAKE-ESDRL algorithm to improve accuracy and lessen training time and response time is the goal of this work. At first, duplicate removal, missing data removal, and data transfer are the steps through which the dataset was processed. From the processed data, by providing the extracted attributes as input to the entropy-based generalized discriminant analysis (E-GDA) method, the number of attributes is reduced. After that, the LogSwish-based deep reinforcement learning algorithm (LS-DRLA) method wielded the reduced attributes for intrusion detection (ID). By utilizing the SHAKE 256 algorithm, the attributes that fall into the attacked class label are hashed and stored in the hash table during this process. Next, to test the real-time data with the trained IDS, the WSN nodes are initialized. For this, by utilizing the supremum distance (SD-K-Means) algorithm, the sensor nodes (SNs) are clustered centered on the cluster heads (CHs) selected by the linear scaling-based honey badger optimization algorithm (LS-HBOA) method. At last, utilizing real-world-based datasets, the proposed algorithms are evaluated and the results are compared using statistical metrics.</p></div>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SHAKE-ESDRL-based energy efficient intrusion detection and hashing system\",\"authors\":\"Geo Francis E, S. Sheeja\",\"doi\":\"10.1007/s12243-023-00963-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Outstanding progress in unsolicited intrusions along with security threats, which interrupt the normal operations of wireless sensor networks (WSNs), have been attracted by the proliferation of WSNs and their applications. In WSNs, this demands an intrusion detection system (IDS), which can detect such attacks with higher detection accuracy. Designing an effective model for IDS using the SDK-LSHB-based SHAKE-ESDRL algorithm to improve accuracy and lessen training time and response time is the goal of this work. At first, duplicate removal, missing data removal, and data transfer are the steps through which the dataset was processed. From the processed data, by providing the extracted attributes as input to the entropy-based generalized discriminant analysis (E-GDA) method, the number of attributes is reduced. After that, the LogSwish-based deep reinforcement learning algorithm (LS-DRLA) method wielded the reduced attributes for intrusion detection (ID). By utilizing the SHAKE 256 algorithm, the attributes that fall into the attacked class label are hashed and stored in the hash table during this process. Next, to test the real-time data with the trained IDS, the WSN nodes are initialized. For this, by utilizing the supremum distance (SD-K-Means) algorithm, the sensor nodes (SNs) are clustered centered on the cluster heads (CHs) selected by the linear scaling-based honey badger optimization algorithm (LS-HBOA) method. At last, utilizing real-world-based datasets, the proposed algorithms are evaluated and the results are compared using statistical metrics.</p></div>\",\"PeriodicalId\":50761,\"journal\":{\"name\":\"Annals of Telecommunications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Telecommunications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12243-023-00963-w\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Telecommunications","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s12243-023-00963-w","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
SHAKE-ESDRL-based energy efficient intrusion detection and hashing system
Outstanding progress in unsolicited intrusions along with security threats, which interrupt the normal operations of wireless sensor networks (WSNs), have been attracted by the proliferation of WSNs and their applications. In WSNs, this demands an intrusion detection system (IDS), which can detect such attacks with higher detection accuracy. Designing an effective model for IDS using the SDK-LSHB-based SHAKE-ESDRL algorithm to improve accuracy and lessen training time and response time is the goal of this work. At first, duplicate removal, missing data removal, and data transfer are the steps through which the dataset was processed. From the processed data, by providing the extracted attributes as input to the entropy-based generalized discriminant analysis (E-GDA) method, the number of attributes is reduced. After that, the LogSwish-based deep reinforcement learning algorithm (LS-DRLA) method wielded the reduced attributes for intrusion detection (ID). By utilizing the SHAKE 256 algorithm, the attributes that fall into the attacked class label are hashed and stored in the hash table during this process. Next, to test the real-time data with the trained IDS, the WSN nodes are initialized. For this, by utilizing the supremum distance (SD-K-Means) algorithm, the sensor nodes (SNs) are clustered centered on the cluster heads (CHs) selected by the linear scaling-based honey badger optimization algorithm (LS-HBOA) method. At last, utilizing real-world-based datasets, the proposed algorithms are evaluated and the results are compared using statistical metrics.
期刊介绍:
Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.