EPH - International Journal of Science And Engineering最新文献

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AGREGATION DES BANDES PASSANTES DES RESEAUX DES TELECOMMUNICATIONS DE LA RDC 刚果（金）电信网络带宽的整合

EPH - International Journal of Science And Engineering Pub Date : 2024-07-23 DOI: 10.53555/ephijse.v10i3.242

Kitengie Mundala Freddy

引用次数: 0

AI SENTRY: REINVENTING CYBERSECURITY THROUGH INTELLIGENT THREAT DETECTION AI sentry：通过智能威胁检测重塑网络安全

EPH - International Journal of Science And Engineering Pub Date : 2023-12-01 DOI: 10.53555/ephijse.v9i3.211

Sakthiswaran Rangaraju

{"title":"AI SENTRY: REINVENTING CYBERSECURITY THROUGH INTELLIGENT THREAT DETECTION","authors":"Sakthiswaran Rangaraju","doi":"10.53555/ephijse.v9i3.211","DOIUrl":"https://doi.org/10.53555/ephijse.v9i3.211","url":null,"abstract":"In recent years, the escalating complexity and frequency of cyber threats have presented a formidable challenge to traditional cybersecurity measures. The emergence of artificial intelligence (AI) technologies has revolutionized the landscape, offering a promising solution to fortify defenses against evolving threats. This paper introduces AI Sentry, an innovative approach to cybersecurity that leverages the power of AI for intelligent threat detection and prevention. AI Sentry embodies a paradigm shift in cybersecurity, integrating machine learning, neural networks, and advanced algorithms to proactively identify, analyze, and mitigate potential threats in real time. By continuously learning from vast datasets and adapting to new attack vectors, AI Sentry enhances its ability to recognize anomalous patterns and behaviors, thereby thwarting sophisticated cyber assaults. The core strength of AI Sentry lies in its capability to detect anomalies and predict threats with a high degree of accuracy, surpassing the limitations of traditional signature-based systems. Through anomaly detection, behavioral analysis, and contextual understanding, AI Sentry not only identifies known threats but also anticipates zero-day attacks and previously unseen malicious activities. This paper delves into the technical underpinnings of AI Sentry, elucidating its architecture, data processing techniques, machine learning models, and the orchestration of various AI components. Furthermore, it explores the ethical considerations and challenges associated with AI-powered cybersecurity, including issues of privacy, bias mitigation, and transparency in decision-making.","PeriodicalId":476780,"journal":{"name":"EPH - International Journal of Science And Engineering","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138612909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SECURE BY INTELLIGENCE: ENHANCING PRODUCTS WITH AI-DRIVEN SECURITY MEASURES 通过智能确保安全：用人工智能驱动的安全措施提升产品性能

EPH - International Journal of Science And Engineering Pub Date : 2023-12-01 DOI: 10.53555/ephijse.v9i3.212

Sakthiswaran Rangaraju

{"title":"SECURE BY INTELLIGENCE: ENHANCING PRODUCTS WITH AI-DRIVEN SECURITY MEASURES","authors":"Sakthiswaran Rangaraju","doi":"10.53555/ephijse.v9i3.212","DOIUrl":"https://doi.org/10.53555/ephijse.v9i3.212","url":null,"abstract":"In an increasingly interconnected digital landscape, the proliferation of sophisticated cyber threats poses significant challenges to the security and integrity of products and services. As traditional security measures struggle to keep pace with evolving threats, there exists a pressing need for innovative and adaptive approaches to safeguarding digital assets. This abstract introduces the concept of \"Secure by Intelligence,\" a paradigm shift in product security that leverages the power of Artificial Intelligence (AI) to fortify defenses and proactively mitigate risks. This paper explores the integration of AI-driven security measures as a foundational element in enhancing the resilience of various products across industries. It delves into the core principles of AI-powered security, emphasizing the utilization of machine learning, deep learning, natural language processing, and anomaly detection to predict, detect, and respond to potential threats in real time. The key focus areas include Dynamic Threat Detection and Prediction, Behavioral Analysis and Anomaly Detection, and Automated Response and Adaptation: Through AI-based automation, systems can autonomously respond to security incidents, mitigating risks in real-time. Furthermore, adaptive AI systems learn from each encounter, enhancing their ability to preempt future attacks. Privacy-Preserving Solutions, Cross-Industry Applications. This paper illustrates real-world case studies and implementations where AI-driven security measures have significantly bolstered product security and resilience. It highlights the tangible advantages of adopting AI-centric security solutions, including improved threat detection accuracy, reduced response times, and enhanced adaptability to emerging cyber threats.","PeriodicalId":476780,"journal":{"name":"EPH - International Journal of Science And Engineering","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0