{"title":"人工智能驱动的网络安全:利用机器学习和深度学习技术在复杂的IT网络中进行实时威胁检测、分析和缓解","authors":"Dabi Dabouabi Dalo Alionsi","doi":"10.54254/2977-3903/3/2023036","DOIUrl":null,"url":null,"abstract":"With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.","PeriodicalId":476183,"journal":{"name":"Advances in Engineering Innovation","volume":"SE-13 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-driven cybersecurity: Utilizing machine learning and deep learning techniques for real-time threat detection, analysis, and mitigation in complex IT networks\",\"authors\":\"Dabi Dabouabi Dalo Alionsi\",\"doi\":\"10.54254/2977-3903/3/2023036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.\",\"PeriodicalId\":476183,\"journal\":{\"name\":\"Advances in Engineering Innovation\",\"volume\":\"SE-13 8\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Engineering Innovation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54254/2977-3903/3/2023036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Engineering Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54254/2977-3903/3/2023036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AI-driven cybersecurity: Utilizing machine learning and deep learning techniques for real-time threat detection, analysis, and mitigation in complex IT networks
With the escalating complexity of IT networks and the surge in cyber threats, the need for advanced, real-time security solutions has never been more paramount. Machine learning (ML) and deep learning (DL) present promising avenues for enhancing the detection, analysis, and mitigation of threats in these intricate networks. The paper delves into the confluence of ML and DL techniques in the realm of cybersecurity, focusing on their application for real-time threat detection within IT infrastructures. Drawing from recent research and developments, the study underscores the potential of these techniques in outmaneuvering conventional security models, while also shedding light on the inherent challenges and areas for future exploration.