يعرض 1 - 3 نتائج من 3 نتيجة بحث عن '"Barreiro Herrera, Daniel Alejandro"', وقت الاستعلام: 0.51s تنقيح النتائج
  1. 1
  2. 2
    Dissertation/ Thesis

    المساهمون: Camargo Mendoza, Jorge Eliecer, Unsecurelab Cybersecurity Research Group

    وصف الملف: xiv, 64 páginas; application/pdf

    Relation: [A, 2020] A, A. A. (2020). Towards the Detection of Phishing Attacks Praveen K TIFAC- CORE in Cyber Security Amrita Vishwa Vidyapeetham; [Adil et al., 2020] Adil, M., Khan, R., and Ghani, M. A. N. U. (2020). Preventive Techniques of Phishing Attacks in Networks. In 2020 3rd International Conference on Advancements in Computational Sciences (ICACS), pages 1–8.; [Ali and Ahmed, 2019] Ali, W. and Ahmed, A. A. (2019). Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Information Security, 13(6):659–669.; [Anand et al., 2018] Anand, A., Gorde, K., Moniz, J. R. A., Park, N., Chakraborty, T., and Chu, B. (2018). Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks. In 2018 IEEE International Conference on Big Data (Big Data), pages 1168–1177.; [apwg, 2022] apwg (2022). PHISHING ACTIVITY TRENDS REPORT Q4 2021.; [Aung and Yamana, 2019] Aung, E. S. and Yamana, H. (2019). URL-Based Phishing Detec- tion Using the Entropy of Non-Alphanumeric Characters. In Proceedings of the 21st Inter- national Conference on Information Integration and Web-Based Applications & Services, iiWAS2019, page 385–392, New York, NY, USA. Association for Computing Machinery.; [Baig et al., 2021] Baig, M. S., Ahmed, F., and Memon, A. M. (2021). Spear-phishing campaigns: Link vulnerability leads to phishing attacks, spear-phishing electronic/uav communication-scam targeted. In 2021 4th International Conference on Computing In- formation Sciences (ICCIS), pages 1–6.; [Balim and Gunal, 2019] Balim, C. and Gunal, E. S. (2019). Automatic Detection of Smishing Attacks by Machine Learning Methods. In 2019 1st International Informatics and Software Engineering Conference (UBMYK), pages 1–3.; [Barreiro and Camargo, 2022] Barreiro, D. A. and Camargo, J. E. (2022). A systematic review on phishing detection: A perspective beyond a high accuracy in phishing detection. pages 173–188.; [Baykara and G ̈urel, 2018] Baykara, M. and G ̈urel, Z. Z. (2018). Detection of phishing at- tacks. In 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pages 1–5.; [Buber et al., 2017] Buber, E., Demir, , and Sahingoz, O. K. (2017). Feature selections for the machine learning based detection of phishing websites. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pages 1–5.; Concone et al., 2019] Concone, F., Re, G. L., Morana, M., and Ruocco, C. (2019). Assisted Labeling for Spam Account Detection on Twitter. In 2019 IEEE International Conference on Smart Computing (SMARTCOMP), pages 359–366.; [Dalgic et al., 2018] Dalgic, F. C., Bozkir, A. S., and Aydos, M. (2018). Phish-IRIS: A New Approach for Vision Based Brand Prediction of Phishing Web Pages via Compact Visual Descriptors. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pages 1–8.; [Das et al., 2020] Das, A., Baki, S., Aassal, A. E., Verma, R., and Dunbar, A. (2020). SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective. IEEE Communications Surveys & Tutorials, 22(1):671–708.; [DomainWatch, ] DomainWatch. DomainWatch - Domain WHOIS Search, Website Infor- mation.; [Eshmawi and Nair, 2019] Eshmawi, A. and Nair, S. (2019). The Roving Proxy Framewrok for SMS Spam and Phishing Detection. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pages 1–6.; [Ginsberg and Yu, 2018] Ginsberg, A. and Yu, C. (2018). Rapid Homoglyph Prediction and Detection. In 2018 1st International Conference on Data Intelligence and Security (ICDIS), pages 17–23; [Huang et al., 2019] Huang, Y., Qin, J., and Wen, W. (2019). Phishing URL Detection Via Capsule-Based Neural Network. In 2019 IEEE 13th International Conference on Anti- counterfeiting, Security, and Identification (ASID), pages 22–26.; [JAMES, 2005] JAMES, L. (2005). Phishing Exposed.; [Li and Wang, 2017] Li, J. and Wang, S. (2017). PhishBox: An Approach for Phishing Va- lidation and Detection. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Con- gress(DASC/PiCom/DataCom/CyberSciTech), pages 557–564.; [Li et al., 2020] Li, Q., Cheng, M., Wang, J., and Sun, B. (2020). LSTM based Phishing Detection for Big Email Data. IEEE Transactions on Big Data, page 1; [Li et al., 2016] Li, X., Geng, G., Yan, Z., Chen, Y., and Lee, X. (2016). Phishing detection based on newly registered domains. In 2016 IEEE International Conference on Big Data (Big Data), pages 3685–3692; [Lingam et al., 2018] Lingam, G., Rout, R. R., and Somayajulu, D. V. L. N. (2018). Detec- tion of Social Botnet using a Trust Model based on Spam Content in Twitter Network. In 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pages 280–285.; [Lingam et al., 2019] Lingam, G., Rout, R. R., and Somayajulu, D. V. L. N. (2019). Deep Q- Learning and Particle Swarm Optimization for Bot Detection in Online Social Networks. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–6.; McGahagan et al., 2019] McGahagan, J., Bhansali, D., Gratian, M., and Cukier, M. (2019). A Comprehensive Evaluation of HTTP Header Features for Detecting Malicious Websites. In 2019 15th European Dependable Computing Conference (EDCC), pages 75–82; [Megha et al., 2019] Megha, N., Babu, K. R. R., and Sherly, E. (2019). An Intelligent Sys- tem for Phishing Attack Detection and Prevention. In 2019 International Conference on Communication and Electronics Systems (ICCES), pages 1577–1582.; [Mondal et al., 2019] Mondal, S., Maheshwari, D., Pai, N., and Biwalkar, A. (2019). A Review on Detecting Phishing URLs using Clustering Algorithms. In 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), pages 1–6; [Nakamura and Dobashi, 2019] Nakamura, A. and Dobashi, F. (2019). Proactive Phishing Sites Detection. In IEEE/WIC/ACM International Conference on Web Intelligence, WI ’19, page 443–448, New York, NY, USA. Association for Computing Machinery.; [Nathezhtha et al., 2019] Nathezhtha, T., Sangeetha, D., and Vaidehi, V. (2019). WC-PAD: Web Crawling based Phishing Attack Detection. In 2019 International Carnahan Confe- rence on Security Technology (ICCST), pages 1–6; [Pande and Voditel, 2017] Pande, D. N. and Voditel, P. S. (2017). Spear phishing: Diag- nosing attack paradigm. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pages 2720–2724; [Patil et al., 2018] Patil, V., Thakkar, P., Shah, C., Bhat, T., and Godse, S. P. (2018). De- tection and Prevention of Phishing Websites Using Machine Learning Approach. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pages 1–5.; [Sahoo, 2018] Sahoo, P. K. (2018). Data mining a way to solve Phishing Attacks. In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pages 1–5.; [Sharma et al., 2017] Sharma, H., Meenakshi, E., and Bhatia, S. K. (2017). A comparative analysis and awareness survey of phishing detection tools. In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pages 1437–1442; [Spaulding et al., 2016] Spaulding, J., Upadhyaya, S., and Mohaisen, A. (2016). The Lands- cape of Domain Name Typosquatting: Techniques and Countermeasures. In 2016 11th International Conference on Availability, Reliability and Security (ARES), pages 284–289.; [Starov et al., 2019] Starov, O., Zhou, Y., and Wang, J. (2019). Detecting Malicious Cam- paigns in Obfuscated JavaScript with Scalable Behavioral Analysis. In 2019 IEEE Security and Privacy Workshops (SPW), pages 218–223.; [urlscan, ] urlscan. URL and website scanner.; [Xiang et al., 2011] Xiang, G., Hong, J., Rose, C. P., and Cranor, L. (2011). CANTINA+: A Feature-rich Machine Learning Framework for Detecting Phishing Web Sites; [Ya et al., 2019] Ya, J., Liu, T., Zhang, P., Shi, J., Guo, L., and Gu, Z. (2019). NeuralAS: Deep Word-Based Spoofed URLs Detection Against Strong Similar Samples. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–7; [Yan et al., 2020] Yan, X., Xu, Y., Xing, X., Cui, B., Guo, Z., and Guo, T. (2020). Trust- worthy Network Anomaly Detection Based on an Adaptive Learning Rate and Momentum in IIoT. IEEE Transactions on Industrial Informatics, page 1; [Yang et al., 2019] Yang, P., Zhao, G., and Zeng, P. (2019). Phishing Website Detection Based on Multidimensional Features Driven by Deep Learning. IEEE Access, 7:15196– 15209.; [Yao et al., 2018] Yao, W., Ding, Y., and Li, X. (2018). LogoPhish: A New Two-Dimensional Code Phishing Attack Detection Method. In 2018 IEEE Intl Conf on Parallel & Distribu- ted Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communi- cations (ISPA/IUCC/BDCloud/SocialCom/SustainCom), pages 231–236; [Yazhmozhi and Janet, 2019] Yazhmozhi, V. M. and Janet, B. (2019). Natural language processing and Machine learning based phishing website detection system. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I- SMAC), pages 336–340.; [Yuan et al., 2018] Yuan, H., Chen, X., Li, Y., Yang, Z., and Liu, W. (2018). Detecting Phishing Websites and Targets Based on URLs and Webpage Links. In 2018 24th Inter- national Conference on Pattern Recognition (ICPR), pages 3669–3674.; [Zhu et al., 2018] Zhu, E., Ye, C., Liu, D., Liu, F., Wang, F., and Li, X. (2018). An Effective Neural Network Phishing Detection Model Based on Optimal Featu- re Selection. In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Compu- ting, Social Computing & Networking, Sustainable Computing & Communications (IS- PA/IUCC/BDCloud/SocialCom/SustainCom), pages 781–787.; [Zuraiq and Alkasassbeh, 2019] Zuraiq, A. A. and Alkasassbeh, M. (2019). Review: Phishing Detection Approaches. In 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), pages 1–6.; https://repositorio.unal.edu.co/handle/unal/84259; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

  3. 3
    Book

    المصدر: Communications in Computer and Information Science ; Applied Informatics ; page 173-188 ; ISSN 1865-0929 1865-0937 ; ISBN 9783031196461 9783031196478