L1NNA Research Laboratory

We research AI for security and security for AI.

Recruitment

At L1NNA, we believe that AI techniques will drive the next-generation security solutions and transform our cybersecurity landscape in a dramatic way. Keeping this in mind, we advance AI to empower human, and secure AI for reliability and explainability. Our research portfolio bridges the area of machine learning, data mining, artificial intelligence and cybersecurity. We mainly focus on the following four dimensions

Protection

Securing the future with AI. Protect your organization against intrusion, malware, spams, zero-day vulnerabilities, and other cyber attacks. Adapt your defense to the constantly changing attack behaviors.

Investigation

Empowering cyber-crime investigators, malware analysts, and reverse engineers with the latest possibility of AI, to understand the provenance and the inner-working of a specific piece of artifact.

Reliability

Promoting the reliability of AI against perturbation, poisoning and adversarial samples by studying the evasive and defensive techniques. Explore the possibility of AI specification and verification.

Explainability

Explaining the unknown. Visualizing the AI's decision making process by post-hoc explanation and build-in transparency in AI. Facilitate the interaction between human and AI systems in the future.

L1NNA research laboratory is located within the School of Computing, at Queen's University in Kingston, Ontario, Canada. Queen’s University is a community, 175 years of tradition, academic excellence, research, and beautiful waterfront campus made of limestone buildings and modern facilities.

At L1NNA, We design novel data mining and machine learning models and develop them as practical software applications that address critical issues in cybersecurity. Our research has attracted industrial interests and has an ongoing close collaboration DRDC. We focus on both the research and the development aspects of these systems.

Research

Understanding a problem's practical challenges, and connect them with the theoretical research gaps.

Development

Putting our research outcome into practice. Develop scalable ready-to-use tools and systems for various problems.

Training

Connecting industrial needs to our research and teaching programs. Preparing HQP for the evolving job market.

News

  • 2019-Mar, Laboratory established
  • 2019-Mar, Received start-up grant from SOC

Current Team Members

Meghna

Steven H. H. Ding

Assistant Professor, Lab Director/Primary Contact

PhD, McGill University

Machine Learning, Data Mining, Cybersecurity

Meghna

Miles Li

Research Member

PhD candidate, McGill University

Machine Learning, Data Mining, Malware Analysis

Meghna

Haohan Bo

Research Member

MSc, McGill University

Machine Learning, Reinforcement Learning, Authorship Analysis

Meghna

Jun Hong Peng

Master's Student @ Queen's

BSc, University of California Santa Barbara

Cryptography, Reinforcement Learning

Meghna

Litao Li

Master's Student @ Queen's [Fast-track]

MSc, University of Toronto

Deep Learning, Artificial Intelligence, Cybersecurity

Meghna

Weihan Ou

Research Member

MSc, University College London (UCL)

Machine Learning, Natural Language Processing, Authorship Analysis

2017-2018 Sarah Alkhodair Concordia University - Doctoral Student Assessing Trust and Veracity of Big Data in Social Media Publication: IPM18, ICML19 Assistant Professor, King Saud University, Saudi Arabia
2017-2019 Arnaud Massenet McGill University - Undergraduate Student Stylometric Analysis for Authorship Analysis Software: StyloTensor - Automated Benchmark of Machine Learning Models for Authorship Analysis Graduate student at Oxford University

Sum Ergo Computo; I am therefore I compute.

Recruitment: PhD/MSc Students in Machine Learning and Cybersecurity

We are looking for self-motivated graduate students with passion in machine learning or cybersecurity. The ideal condidate will possess good writing skills, proficiency in programming, and be familiar with Java/C#/Python/TypeScript. Knowledge of TensorFlow/Spark/NoSQL/React+Django/Docker/Kubernetes will be a big plus!

All research-based graduate students will be fully funded.

Minimum English requirement [for international students]: TOEFL: 88 or IELTS: 7

Start date: flexible

[Chinese version of this posting]
Interested? Shoot your CV over here!

L1NNA
人工智能与安全实验室

招收博士生,硕士生

加拿大皇后大学 Queen's University 的L1NNA人工智能与安全实验室招博士、硕士若干名,欢迎朋友们转发, 或向我推荐优秀学生。

我们的研究方向包括人工智能、深度学习和数据挖掘在计算机安全领域的发展,还涵盖了人工智能安全性、可靠性、可解释度以及对抗机器学习(AML)方向的理论研究。安全是人工智能过去十年以来及未来最有盈利能力的方向,其也是大众对人工智能、自动驾驶可接受程度重要考量。

目前的研究课题涉及恶意软件分解(Malware Analysis),人工智能辅助逆向工程(AI-assisted reverse engineering),海量汇编数据的克隆索引(assembly clone search),写作风格(Writeprint Analysis)的智能分析, 计算机安全物源分析(provenance analysis),透明和可解释的人工智能(transparent and explainable AI)以及对抗机器学习 (AML)。未来我们还将探索人工智能和机器学习的可靠性在规格说明(specification)以及形式化验证(formal verification)的理论研究。

实验室目前处于上升阶段,研究基金宽裕,对于录取的学生提供全额奖学金,涵盖学杂费以及生活费用,折合约13-15万人民币/年起。详情可参见计算机学院网页。

work-image
  • 入学时间: 2019年秋, 2020年冬
  • 负责人: 丁教授 (硕士生导师,博士生导师)
  • 联系方式: ding@cs.queensu.ca
  • 实验室网站:l1nna.com
  • 英语要求 TOEFL: 88 或 IELTS: 7
  • 专业不限,要求一定数学和计算机基础

秉承学术研究开放独立自由的理念,所有研究成果均公开透明,研究兴趣和方向不受任何个体或组织所影响。

L1NNA实验室位于加拿大皇后大学计算机学院。皇后大学是加拿大最古老的大学之一,175年历史,以英国维多利亚女王命名,2015年物理学一教授获得了诺贝尔奖。国际学生仅占不到10%,加拿大排名常年前五,学生满意度排名常年前三。据统计,98%的学生在毕业后六个月内被应聘,大部分计算机系的学生毕业前就已收到至少一个录用通知,研究生和博士生更受欢迎。

Current Projects

Assembly Code Data Mining

Assembly clone search greatly reduces the manual effort of reverse engineering since it can identify the cloned parts that have been previously analyzed. By closely collaborating with reverse engineers, I studied the challenges, designed and implemented an award-winning clone search engine called Kam1n0. It also includes specialized techniques that can mitigate the variance introduced by different processor families, different compilers, optimization techniques, and binary protection techniques. Kam1n0 has been presented at the Smart Cybersecurity Network Canada (SERENE-RISC), SOPHOS, ESET, Above Security, and Google.

Read more

Authorship Analysis

The internet provides an ideal anonymous channel for concealing computer-mediated malicious activities, as the network-based origins of critical electronic textual evidence (e.g., emails, blogs, forum posts, chat logs, etc.) can be easily repudiated. Given the anonymous documents, authorship analysis is the study of identifying the actual author and his/her socio-linguistic characteristics. Many linguistic stylometric features and computational techniques have been extensively studied for this purpose. However, most of them emphasize promoting the authorship attribution accuracy, and few works have been done for the purpose of constructing and visualizing the evidential traits. I opt for an interpretable and explainable approach by which writing styles can be visualized, compared, and interpreted by an investigator like fingerprints. I also propose to integrate differential privacy and reinforcement learning to paraphrase text where writing style is sanitized.

Read more

Binary Provenance Analysis

Binary provenance denotes the characteristics of a program that derives from its path from source code to executable form. Binary provenance is important in the domain of binary forensic and performance analysis. It provides important evidential trial for cybersecurity investigators to track down the hackers behind the security accidence. For example, the Lazarus group is linked to the Wannacry incidence by code similarity. I mainly focus on two critical aspects: toolchain recovery and authorship analysis.

Read more

Neural Malware Analysis

Malware behavioral indicators denote those potentially high-risk malicious behaviors exhibited, such as unintended network communications, file encryption, keystroke logging, sandbox evasion, and camera manipulation. Generally, they are generated using sandboxes or simulators. However, the complexity of modern malware has been considerably increased. Malware is becoming sandbox-aware by incorporating modern evasive techniques. To address these issues, I propose a new neural network-based static scanner that can characterize the malicious behaviors of a given executable, without running it in a sandbox. It can be used as an additional binary analytic layer to mitigate the issues of polymorphism, metamorphism, and evasive techniques.

Read more

Highlight: KAM1N0

Open-source binary analysis and assembly code management platform.

Publications and Talks

Browse our latest publications spanning a wide range of research problems.

Journal Articles (3 + 2 in progress)

  • S. H. H. Ding, B. C. M. Fung, F. Iqbal, and W. K. Cheung. Learning stylometric representations for authorship analysis. IEEE Transactions on Cybernetics (CYB18), ():14 pages, 2018, in press.
    [ JCR impact factor: 8.803, 5-year: 8.775 | Abstract | BibTex | Code | PDF | URL ]
  • M. H. Altakrori, F. Iqbal, B. C. M. Fung, S. H. H. Ding, and A. Tubaishat. Arabic authorship attribution: an extensive study on twitter posts. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 18(1):5.1-5.51, November 2018.
    [ Abstract | BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and M. Debbabi. A visualizable evidence-driven approach for authorship attribution. ACM Transactions on Information and System Security (TISSEC15), 17(3):30 pages, 2015.
    [ JCR impact factor: 0.759, 5-year: 1.610 | SJR impact factor: 1.772 | top journal in information security | Abstract | BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and P. Charland. Sym1n0: large-scale symbolic expression retrieval for cross-architecture assembly clone search. IEEE Transactions on Software Engineering (TSE), (14 pages):, under revision for re-submission.
    [ BibTex | PDF | URL ]
  • S. A. Alkhodair, S. H. H. Ding, B. C. M. Fung, and J. Liu. Detecting breaking news rumors of emerging topics in social media. Elsevier Information Processing and Management (IPM), ():20 pages, under minor revision.
    [ BibTex | PDF | URL ]

Conference Proceedings (2 + 1 in progress)

  • S. H. H. Ding, B. C. M. Fung, and P. Charland. Kam1n0: mapreduce-based assembly clone search for reverse engineering. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD16), 10 pages. San Francisco, CA, August 2016. ACM Press.
    [ acceptance ratio: 142784 = 18\% | top conference in data mining | Abstract | BibTex | Code | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and P. Charland. Asm2vec: boosting static representation robustness for binary clone search against code obfuscation and compiler optimization. In Proceedings of the 40th International Symposium on Security and Privacy (S&P19a), 18 pages. San Francisco, CA, May 2019. IEEE Computer Society.
    [ up-to-date acceptance ratio: 13225 = 5.7\% (submission opens until Feb.) | top conference in security | Abstract | BibTex | Code | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, S. McIntosh, S. M, and P. Charland. Raven: neural malware behavior characterization without sandbox. In Proceedings of the 40th International Symposium on Security and Privacy (S&P19b), 18 pages. 2019, under review. IEEE Computer Society.
    [ BibTex | PDF | URL ]

Invited Talks (6)

  • S. H. H. Ding. Kam1n0: data mining and machine learning for reverse engineering. Hong Kong University of Science and Technology, Hong Kong, China, Oct. 2018.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding. Kam1n0: mapreduce-based assembly clone search for reverse engineering. Google Montreal, Montreal, Canada, Nov. 2016.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding. Kam1n0: mapreduce-based assembly clone search for reverse engineering. EBTIC Research Centre established by British Telecommunications (BT), Abu Dhabi, UAE, Sept. 2016.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding. Kam1n0: mapreduce-based assembly clone search for reverse engineering. Above Security Montreal, Montreal, Canada, Jul. 2016.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding. Kam1n0: mapreduce-based assembly clone search for reverse engineering. ESET Montreal, Montreal, Canada, Jun. 2016.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding. Kam1n0: mapreduce-based assembly clone search for reverse engineering. Sophos Vancouver, Vancouver, Canada, Apr. 2016.
    [ BibTex | PDF | URL ]

Conference Presentations (3)

  • S. H. H. Ding, B. C. M. Fung, and P. Charland. Asm2vec: boosting static representation robustness for binary clone search against code obfuscation and compiler optimization. The 40th International Symposium on Security and Privacy (S&P19), Jun. 2019.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding. Kam1n0: mapreduce-based assembly clone search for reverse engineering. The Smart Cybersecurity Network (SERENE-RISC) Research Showcase, rapid-fire presentation and poster presentation, best poster award, Nov. 2016.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, S. M, and P. Charland. Kam1n0: mapreduce-based assembly clone search for reverse engineering. Jun. 2016.
    [ span class=‘cit’>The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD16), poster presentatio | BibTex | PDF | URL ]

Technical Reports (8)

  • S. H. H. Ding, M. Q. Li, and B. C. M. Fung. Assembly code data mining: design and prototyping (part 7). Technical Report W7701-155902/001/QCL (TA07), Defence Research and Development Canada (DRDC), Mar. 2018.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, M. Q. Li, and M. R. Farhadi. Assembly code data mining: design and prototyping (part 6). Technical Report W7701-155902/001/QCL (TA06), Defence Research and Development Canada (DRDC), Jan. 2018.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and M. R. Farhadi. Assembly code data mining: design and prototyping (part 5). Technical Report W7701-155902/001/QCL (TA05), Defence Research and Development Canada (DRDC), Feb. 2017.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and M. R. Farhadi. Assembly code data mining: design and prototyping (part 4). Technical Report W7701-155902/001/QCL (TA04), Defence Research and Development Canada (DRDC), Jan. 2017.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and K. Zhao. Assembly code data mining: design and prototyping (part 3). Technical Report W7701-155902/001/QCL (TA02), Defence Research and Development Canada (DRDC), Mar. 2016.
    [ BibTex | PDF | URL ]
  • S. H. H. Ding, B. C. M. Fung, and K. Zhao. Assembly code data mining: porting to other platforms. Technical Report W7701-155902/001/QCL (TA03), Defence Research and Development Canada (DRDC), Feb. 2016.
    [ BibTex | PDF | URL ]
  • B. C. M. Fung, S. H. H. Ding, and K. Zhao. Assembly code data mining: design and prototyping (part 2). Technical Report W7701-155902/001/QCL (TA01), Defence Research and Development Canada (DRDC), Jan. 2016.
    [ BibTex | PDF | URL ]
  • B. C. M. Fung, S. H. H. Ding, M. R. Farhadi, and K. Zhao. Assembly code data mining: design and prototyping (part 1). Technical Report W7701-155902/001/QCL (Firm Portion), Defence Research and Development Canada (DRDC), Mar. 2015.
    [ BibTex | PDF | URL ]

Our Courses

CISC 327 Software Quality Assurance

2019-fall

Queen's University

CISC 372 Advanced Data Analytics

2019-winter

Queen's University

GLIS 630 Data Mining

2018-winter

McGill University

Recent Blogs

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R-ggplot Notes

ggplot quick reference notes

Sklean+Xgboost Cross Validation with Grid Search Tuning

Xgboost with Sklean with randomized parameter search

IDAAPI Notes

IDAAPI quick reference notes

IEEE Xplore PDF Check - Missing Fonts

Embedding fonts using preflight.

Get In Touch

Schedule - Make an appointment HERE

Contact Details

531 Goodwin Hall, Queen's University
     Kingston, Ontario, Canada K7L 2N8
531 Goodwin Hall, Queen's University
     Kingston, Ontario, Canada K7L 2N8
Fax: available upon request