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RISS 인기검색어
- 검색키워드
- 저자 : Ankit Verma (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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DNS Prevention Using 64-Bit Time Synchronized Public Key Encryption to Isolate Phishing Attacks
Tanvi Gupta,Sumit Kumar,Ankit Tomar,KamalKant Verma 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.8
In this work, a quick authentication scheme is implemented to prevent Phishing and DNS spoofing. In DNS spoofing, attackers inject the fake DNS server by duplicating the IP addresses and fake server redirect network traffic to wrong destinations. In phishing, phishers clone the legitimate website and user think that it is original website and users giving away their username and passwords to attacker’s website and attackers hack their confidential information and they can misuse it for financial gain, identity theft, gaining fame, malware distribution and industrial espionage. We host the phishing website but we cannot pass the link through common hosting websites like Google and Facebook. So, phishers force the legitimate users to open a phished link with the DNS spoofing through fake DNS server then user directly redirect to a fake server. So our proposed work is to prevent DNS spoofing, to prevent the Phishing attacks by isolating it using 64-bit time synchronized public key encryption.
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Predictive Breast Cancer Statistical Modelling for Early Diagnosis
Amit Kumar Gupta,Ankit Verma,Vipin Kumar,Nikhil Kumar,Dowon Kim,Young-Jin Jung,Mangal Sain 한국자기학회 2023 Journal of Magnetics Vol.28 No.4
Breast cancer is a significant global health concern, stressing the urgent need for early detection. Early diagnosis improves access to varied treatments and significantly enhances patient outcomes. This study explores breast cancer detection over two days, aiming to create a precise and efficient machine learning model. The research uses a diverse dataset, combining clinical, genetic, and imaging data, including magnetic resonance imaging (MRI), X-ray, and electromagnetic data. Rigorous data preprocessing, including variable normalization and feature identification, enhances dataset quality. Predictive models use statistical techniques like logistic regression, decision trees, and random forest. Key metrics, such as accuracy, precision, recall, and area under the curve (AUC), assess model efficacy. Results reveal high accuracy and AUC scores, indicating potential for precise breast cancer detection. The study enhances our understanding of breast cancer dynamics, showcasing the effectiveness of machine learning for accurate and efficient early diagnosis. The research underscores diverse datasets and careful statistical modeling as crucial for predictive breast cancer capabilities.
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