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<title>蒋杰 | sec-jay | 个人主页</title>
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<body>
<div class="page">
<div class="topbar">
<div class="lang-switch" role="group" aria-label="Language switcher">
<button class="lang-btn" type="button" data-lang-target="zh" aria-pressed="true">中文</button>
<button class="lang-btn" type="button" data-lang-target="en" aria-pressed="false">EN</button>
</div>
</div>
<main class="shell">
<div class="content">
<section class="lang-panel" data-lang-panel="zh">
<section class="hero">
<div>
<div class="eyebrow">Malware Researcher · AI Security · MSc @ GZU</div>
<div class="hero-head">
<h1 class="title">蒋杰 / sec-jay</h1>
<div class="actions">
<a class="btn primary" href="mailto:2112433088@e.gzhu.edu.cn">邮件联系</a>
<a class="btn" href="https://github.com/sec-jay" target="_blank" rel="noreferrer">GitHub</a>
</div>
</div>
</div>
<div class="hero-stack">
<aside class="panel info-strip">
<h2>教育与联系</h2>
<div class="meta-list">
<div class="meta-item">
<span class="meta-label">当前身份</span>
广州大学网络空间安全学院人工智能硕士生，2025 - 2027（预计）
</div>
<div class="meta-item">
<span class="meta-label">本科背景</span>
海南大学工程管理本科，2017 - 2021
</div>
<div class="meta-item">
<span class="meta-label">联系</span>
2112433088@e.gzhu.edu.cn · GitHub / sec-jay
</div>
</div>
</aside>
<aside class="panel featured">
<div class="kicker">Featured Research</div>
<h2 class="paper-title">Unknown malware detection based on hyperspherical embedding and out-of-distribution sample detection</h2>
<ul class="list">
<li>在投 IEEE IoTJ。</li>
<li>方法覆盖 SupCon、vMF 建模与 Mahalanobis 置信度检测。</li>
<li>融合字符串、API、opcode 等多源静态特征。</li>
</ul>
</aside>
</div>
</section>
<section class="section">
<div class="section-head">
<h2>代表工作</h2>
<p>Featured Research Snapshot</p>
</div>
<article class="card">
<h3>Unknown malware detection based on hyperspherical embedding and out-of-distribution sample detection</h3>
<p>这篇工作聚焦未知恶意软件检测，核心思路是在超球面嵌入空间中引入更稳定的类间结构与置信度估计，面向零日检测场景提升鲁棒性和可解释性。</p>
<div class="metrics">
<div class="metric"><strong>IEEE</strong><span>Under Review</span></div>
<div class="metric"><strong>OOD</strong><span>Detection</span></div>
<div class="metric"><strong>vMF</strong><span>Modeling</span></div>
</div>
<ul class="list">
<li>基于 SupCon、vMF 建模和 Mahalanobis 置信度检测构建整体方案。</li>
<li>融合字符串、API、opcode 等多源静态特征。</li>
<li>目标是降低零日恶意软件检测中的误报并提升稳健性。</li>
</ul>
</article>
</section>
<section class="section">
<div class="section-head">
<h2>关于我</h2>
<p>一个持续扩展中的研究型个人主页</p>
</div>
<div class="grid two">
<article class="card">
<h3>研究取向</h3>
<p>我是广州大学网络空间安全学院人工智能方向硕士生，当前工作集中在恶意软件生成、对抗攻击与检测稳健性等问题，强调实验质量、方法可复用性和长期积累。</p>
</article>
<article class="card">
<h3>主页目标</h3>
<p>这个主页不只记录研究成果，也会逐步沉淀我的学习路径、项目轨迹和支持长期工作的生活方式。</p>
</article>
</div>
</section>
<section class="section">
<div class="section-head">
<h2>研究兴趣与方法</h2>
<p>Work / Professional Highlights</p>
</div>
<div class="grid two">
<article class="card">
<h3>研究兴趣</h3>
<ul class="list">
<li>恶意软件生成，关注可控性、真实性与高规避能力。</li>
<li>对抗样本生成与安全对抗，尤其是强化学习与生成方法的结合。</li>
<li>逆向工程学习路线，作为长期增强二进制分析能力的基础。</li>
</ul>
</article>
<article class="card">
<h3>方法与技能</h3>
<p>当前主要围绕恶意软件分析、对抗安全研究、生成模型、强化学习和 OOD 检测展开。</p>
<div class="tags">
<span class="tag">Malware Analysis</span>
<span class="tag">Adversarial Security</span>
<span class="tag">Generative Models</span>
<span class="tag">Reinforcement Learning</span>
<span class="tag">OOD Detection</span>
<span class="tag">Dataset Curation</span>
</div>
</article>
</div>
</section>
<section class="section">
<div class="section-head">
<h2>其他论文与在投工作</h2>
<p>Publications / Under Review</p>
</div>
<div class="grid two">
<article class="card">
<h3>An Evolutionary Strategy-Enhanced Conditional GAN for High-Evasion and Low-Trace Adversarial Malware Generation</h3>
<p>在投，期刊或会议待定。关注高规避、低痕迹、PE 合规的对抗恶意软件生成。</p>
<ul class="list">
<li>采用 CMA-ES + CGAN 进行离散字节空间优化。</li>
<li>结果包含 79.43% 峰值规避率、69.60% 平均 ASR、0.9% PBR。</li>
<li>扰动设计涵盖 DOS/header 修改、节空间填充与新节注入。</li>
</ul>
</article>
<article class="card">
<h3>基于对抗样本的恶意软件逃逸研究</h3>
<p>在投《软件学报》，系统梳理对抗恶意软件逃逸的方法、威胁模型与开放问题。</p>
<ul class="list">
<li>构建统一的扰动策略与评估指标分类框架。</li>
<li>覆盖攻击构造、评测方法和防御方向。</li>
</ul>
</article>
</div>
</section>
<section class="section">
<div class="section-head">
<h2>项目与当前重点</h2>
<p>Selected Work / Current Focus</p>
</div>
<div class="timeline">
<div class="timeline-item">
<strong>对抗恶意软件生成实验线</strong>
<div>持续建设可复用实验流程，已完成 CycleGAN 相关实验，并继续拆解 DQN 实现。</div>
</div>
<div class="timeline-item">
<strong>DataCon 数据集复现计划</strong>
<div>复现基线模型，稳定预处理和评估流程，形成后续研究可复用的数据工作流。</div>
</div>
<div class="timeline-item">
<strong>长期路线</strong>
<div>在完善论文与实验细节的同时，系统推进逆向学习路径，增强真实样本分析能力。</div>
</div>
</div>
</section>
</section>
<section class="lang-panel" data-lang-panel="en" hidden>
<section class="hero">
<div>
<div class="eyebrow">Malware Researcher · AI Security · MSc @ GZU</div>
<div class="hero-head">
<h1 class="title">Jay Dong / sec-jay</h1>
<div class="actions">
<a class="btn primary" href="mailto:2112433088@e.gzhu.edu.cn">Email</a>
<a class="btn" href="https://github.com/sec-jay" target="_blank" rel="noreferrer">GitHub</a>
</div>
</div>
</div>
<div class="hero-stack">
<aside class="panel info-strip">
<h2>Education and Contact</h2>
<div class="meta-list">
<div class="meta-item">
<span class="meta-label">Current Position</span>
MSc student in Artificial Intelligence, Guangzhou University, 2025 - 2027 (expected)
</div>
<div class="meta-item">
<span class="meta-label">Background</span>
BEng in Engineering Management, Hainan University, 2017 - 2021
</div>
<div class="meta-item">
<span class="meta-label">Contact</span>
2112433088@e.gzhu.edu.cn · GitHub / sec-jay
</div>
</div>
</aside>
<aside class="panel featured">
<div class="kicker">Featured Research</div>
<h2 class="paper-title">Unknown malware detection based on hyperspherical embedding and out-of-distribution sample detection</h2>
<p class="lead">This is the work I want to foreground first: unknown malware detection with hyperspherical embeddings and calibrated confidence, aimed at stronger zero-day detection with fewer false positives.</p>
<ul class="list">
<li>Under review at IEEE IoTJ.</li>
<li>Built around SupCon, vMF modeling, and Mahalanobis-confidence detection.</li>
<li>Combines strings, APIs, opcodes, and other static features.</li>
</ul>
</aside>
</div>
</section>
<section class="section">
<div class="section-head">
<h2>Featured Work</h2>
<p>Research Snapshot</p>
</div>
<article class="card">
<h3>Unknown malware detection based on hyperspherical embedding and out-of-distribution sample detection</h3>
<p>This work focuses on unknown malware detection by combining a hyperspherical embedding space with calibrated confidence estimation, targeting more robust and interpretable performance in zero-day settings.</p>
<div class="metrics">
<div class="metric"><strong>IEEE</strong><span>Under Review</span></div>
<div class="metric"><strong>OOD</strong><span>Detection</span></div>
<div class="metric"><strong>vMF</strong><span>Modeling</span></div>
</div>
<ul class="list">
<li>Uses SupCon, vMF modeling, and Mahalanobis-confidence detection.</li>
<li>Combines strings, APIs, opcodes, and other static features.</li>
<li>Aims to reduce false positives in zero-day malware detection.</li>
</ul>
</article>
</section>
<section class="section">
<div class="section-head">
<h2>About Me</h2>
<p>A bilingual personal homepage with separate English and Chinese views</p>
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<h3>Research Direction</h3>
<p>I am an MSc student in Artificial Intelligence at Guangzhou University, School of Cyberspace Security. My work focuses on malware generation, adversarial attacks, and robust detection under realistic settings.</p>
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<article class="card">
<h3>What This Site Is For</h3>
<p>This site is both a research profile and a longer-term personal homepage that records how I learn, build projects, and develop a sustainable way of working.</p>
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<h2>Research Interests and Methods</h2>
<p>Work / Professional Highlights</p>
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<h3>Research Interests</h3>
<ul class="list">
<li>Malware generation with emphasis on controllability, realism, and high-evasion behavior.</li>
<li>Adversarial sample generation and security confrontation, especially RL-enhanced generation.</li>
<li>Reverse-engineering as a long-term path to stronger binary analysis capability.</li>
</ul>
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<article class="card">
<h3>Methods and Skills</h3>
<p>My current toolkit centers on malware analysis, adversarial security research, generative models, reinforcement learning, and OOD detection.</p>
<div class="tags">
<span class="tag">Malware Analysis</span>
<span class="tag">Adversarial Security</span>
<span class="tag">Generative Models</span>
<span class="tag">Reinforcement Learning</span>
<span class="tag">OOD Detection</span>
<span class="tag">Dataset Curation</span>
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<h2>Other Publications and Work Under Review</h2>
<p>Selected Research</p>
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<h3>An Evolutionary Strategy-Enhanced Conditional GAN for High-Evasion and Low-Trace Adversarial Malware Generation</h3>
<p>Under review, venue TBD, focused on high-evasion and low-trace malware generation with PE-compliant perturbations.</p>
<ul class="list">
<li>Uses CMA-ES + CGAN for optimization in discrete byte space.</li>
<li>Reported 79.43% peak evasion, 69.60% mean ASR, and 0.9% PBR.</li>
<li>Perturbations include DOS/header edits, section padding, and new-section injection.</li>
</ul>
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<article class="card">
<h3>Research on adversarial-sample-based malware evasion</h3>
<p>Under review at Journal of Software, offering a systematic review of adversarial malware evasion methods and open challenges.</p>
<ul class="list">
<li>Builds a unified taxonomy for perturbation strategies and evaluation metrics.</li>
<li>Covers attack construction, evaluation practice, and defense directions.</li>
</ul>
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<section class="section">
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<h2>Projects and Current Focus</h2>
<p>Selected Work / Current Focus</p>
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<div class="timeline">
<div class="timeline-item">
<strong>Adversarial Malware Generation Track</strong>
<div>Building a reusable experiment line, including CycleGAN-related work and ongoing DQN implementation study.</div>
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<strong>DataCon Dataset Reproduction</strong>
<div>Reproducing baselines, stabilizing preprocessing and evaluation, and shaping a reusable malware-analysis workflow.</div>
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<div class="timeline-item">
<strong>Longer-Term Path</strong>
<div>Improving papers and experiment quality while systematically deepening reverse-engineering capability.</div>
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<div class="footer">Last updated: 2026-03-16 · Built with GitHub Pages</div>
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