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class:gradsec2026 [2026/03/16 01:14] mhshin [Agenda] |
class:gradsec2026 [2026/03/30 13:59] (current) jhj2004 [Agenda] |
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| | 3/4 | Minho | AI-Introduction | {{ :class:ai-intro.pdf |AI-Intro}} | | | | 3/4 | Minho | AI-Introduction | {{ :class:ai-intro.pdf |AI-Intro}} | | | ||
| | 3/11 | Minho | | | | | | 3/11 | Minho | | | | | ||
| - | | ::: | Cho | [[https://www.usenix.org/system/files/sec21-schuster.pdf|You autocomplete me: Poisoning vulnerabilities in neural code completion]] | | | | + | | ::: | Cho | [[https://www.usenix.org/system/files/sec21-schuster.pdf|You autocomplete me: Poisoning vulnerabilities in neural code completion]] | [[https://1drv.ms/p/c/005794ae9195628e/IQB4fo_zfZeySKirBSMijjfiAVbNdg_9N1hiWS702-MyQpk?e=SsGxwB|You autocomplete me: Poisoning vulnerabilities in neural code completion]] | | |
| | 3/18 | Minho | | | | | | 3/18 | Minho | | | | | ||
| | ::: | Han | [[https://arxiv.org/pdf/2102.07995.pdf|D2a: A dataset built for ai-based vulnerability detection methods using differential analysis]] | | | | | | ::: | Han | [[https://arxiv.org/pdf/2102.07995.pdf|D2a: A dataset built for ai-based vulnerability detection methods using differential analysis]] | | | | | ||
| - | | 3/25 | Minho | | | | | + | | 3/27 | Minho | | | | |
| - | | ::: | Kwak| | | | | + | | ::: | Kwak| [[https://www.mdpi.com/1424-8220/23/9/4403/pdf|A Deep Learning-Based Innovative Technique for Phishing Detection with URLs]] | | | |
| - | | 4/1 | Cho | | | | | + | | 4/1 | No Class | | | | |
| - | | 4/8 | No Class | | | | | + | | 4/10 | Cho | [[https://arxiv.org/pdf/1803.04173|Adversarial Malware Binaries: Evading Deep |
| + | Learning for Malware Detection in Executables]] | | | | ||
| | 4/15 | Han | | | | | | 4/15 | Han | | | | | ||
| - | | 4/22 | No Class | | | | | + | | 4/24 | Kwak| | | | |
| - | | 4/29 | Kwak | | | | | + | | 4/29 | Cho | | | | |
| - | | 5/6 | Cho | | | | | + | | 5/6 | Han | | | | |
| - | | 5/13 | Han | | | | | + | | 5/13 | Kwak | | | | |
| - | | 5/20 | Kwak | | | | | + | | 5/20 | Cho | | | | |
| - | | 5/27 | Cho | | | | | + | | 5/27 | Han | | | | |
| - | | 6/3 | Han | | | | | + | | 6/3 | Kwak | | | | |
| - | | 6/10 | Kwak | | | | | + | | 6/10 | Cho | | | | |
| - | | 6/17 | Cho | | | | | + | | 6/17 | Han | | | | |
| - | | 6/24 | Han | | | | | + | | 6/24 | Kwak | | | | |
| ====== Class Information ====== | ====== Class Information ====== | ||
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| * Keywords: Email phishing, deep learning, BERT, CNN-LSTM, natural language processing | * Keywords: Email phishing, deep learning, BERT, CNN-LSTM, natural language processing | ||
| * URL: https://www.mdpi.com/2079-9292/12/20/4261/pdf | * URL: https://www.mdpi.com/2079-9292/12/20/4261/pdf | ||
| - | - **A Deep Learning-Based Innovative Technique for Phishing Detection with URLs** | + | - <fc red>(kwak)</fc>**A Deep Learning-Based Innovative Technique for Phishing Detection with URLs** |
| * Saleh N. Almuayqil et al., Sensors 2023 | Pages: 20 | Difficulty: 2/5 | * Saleh N. Almuayqil et al., Sensors 2023 | Pages: 20 | Difficulty: 2/5 | ||
| * Abstract: Proposes CNN-based model for phishing website detection using character embedding approach on URLs. Evaluates performance on PhishTank dataset achieving high accuracy in distinguishing legitimate from phishing websites. Introduces novel 1D CNN architecture specifically designed for URL-based detection without requiring HTML content analysis. | * Abstract: Proposes CNN-based model for phishing website detection using character embedding approach on URLs. Evaluates performance on PhishTank dataset achieving high accuracy in distinguishing legitimate from phishing websites. Introduces novel 1D CNN architecture specifically designed for URL-based detection without requiring HTML content analysis. | ||
class/gradsec2026.1773598442.txt.gz · Last modified: 2026/03/16 01:14 by mhshin · [Old revisions]
