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Creators/Authors contains: "Chen, H."

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  1. ; ; ; ; ; ; ; ; ( , Monthly notices of the Royal Astronomical Society)
    Free, publicly-accessible full text available October 1, 2023
  2. ; ; ( , MIS quarterly)
    Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)-based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attentionmore »mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-the-art non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVA-DSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors.« less
    Free, publicly-accessible full text available June 1, 2023
  3. ; ; ( , Journal of machine learning research)
    Marc Peter Deisenroth (Ed.)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ; ; ( , ArXivorg)
    Semiconductors with O(meV) band gaps have been shown to be promising targets to search for sub-MeV mass dark matter (DM). In this paper we focus on a class of materials where such narrow band gaps arise naturally as a consequence of spin-orbit coupling (SOC). Specifically, we are interested in computing DM-electron scattering and absorption rates in these materials using state- of-the-art density functional theory (DFT) techniques. To do this, we extend the DM interaction rate calculation to include SOC effects which necessitates a generalization to spin-dependent wave functions. We apply our new formalism to calculate limits for several DM benchmarkmore »models using an example ZrTe5 target and show that the inclusion of SOC can substantially alter projected constraints.« less
    Free, publicly-accessible full text available February 1, 2023
  5. ; ; ; ; ( , IEEE ICDM)
    Free, publicly-accessible full text available December 1, 2022
  6. ; ; ; ; ; ; ; ( , Journal of Mathematical Analysis)
    Free, publicly-accessible full text available January 1, 2023
  7. ; ; ; ; ( , 2021 IEEE Intelligence and Security Informatics (ISI))
    Cybercrime was estimated to cost the global economy $945 billion in 2020. Increasingly, law enforcement agencies are using social network analysis (SNA) to identify key hackers from Dark Web hacker forums for targeted investigations. However, past approaches have primarily focused on analyzing key hackers at a single point in time and use a hacker’s structural features only. In this study, we propose a novel Hacker Evolution Identification Framework to identify how hackers evolve within hacker forums. The proposed framework has two novelties in its design. First, the framework captures features such as user statistics, node-level metrics, lexical measures, and postmore »style, when representing each hacker with unsupervised graph embedding methods. Second, the framework incorporates mechanisms to align embedding spaces across multiple time-spells of data to facilitate analysis of how hackers evolve over time. Two experiments were conducted to assess the performance of prevailing graph embedding algorithms and nodal feature variations in the task of graph reconstruction in five timespells. Results of our experiments indicate that Text- Associated Deep-Walk (TADW) with all of the proposed nodal features outperforms methods without nodal features in terms of Mean Average Precision in each time-spell. We illustrate the potential practical utility of the proposed framework with a case study on an English forum with 51,612 posts. The results produced by the framework in this case study identified key hackers posting piracy assets.« less
    Free, publicly-accessible full text available November 1, 2022
  8. ; ; ; ; ; ; ; ( , ACM CIKM)
    Free, publicly-accessible full text available November 1, 2022
  9. ; ; ( , ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021)
    Despite significant contributions to various aspects of cybersecurity, cyber-attacks remain on the unfortunate rise. Increasingly, internationally recognized entities such as the National Science Foundation and National Science & Technology Council have noted Artificial Intelligence can help analyze billions of log files, Dark Web data, malware, and other data sources to help execute fundamental cybersecurity tasks. Our objective for the 1st Workshop on Artificial Intelligence-enabled Cybersecurity Analytics (half-day; co-located with ACM KDD) was to gather academic and practitioners to contribute recent work pertaining to AI-enabled cybersecurity analytics. We composed an outstanding, inter-disciplinary Program Committee with significant expertise in various aspects ofmore »AI-enabled Cybersecurity Analytics to evaluate the submitted work. Significant contributions to the half-day workshop were made in the areas of CTI, vulnerability assessment, and malware analysis.« less
    Accepted Manuscript Full Text Available
  10. ; ; ( , SIGKDD explorations)
    Full Text Available