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1. LINKING EXPLOITS FROM THE DARK WEB TO KNOWN VULNERABILITIES FOR PROACTIVE CYBER THREAT INTELLIGENCE: AN ATTENTION-BASED DEEP STRUCTURED SEMANTIC MODEL1

   Samtani, S; Chai, Y; and Chen, H. (June 2022, 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 attention 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. 

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2. Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-based Deep Structured Semantic Model

   https://doi.org/10.25300/MISQ/2022/15392  

   Samtani, S; Chai, Y; and Chen, H (June 2022, 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 attention 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. 

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3. Interactive Demonstration of EVA

   https://doi.org/10.14778/3611540.3611626  

   Kakkar, Gaurav Tarlok; Rajoria, Aryan; Kalluraya, Myna Prasanna; Raju, Ashmita; Cao, Jiashen; Rong, Kexin; Arulraj, Joy (August 2023, Proceedings of the VLDB Endowment)

   In this demonstration, we will present EVA, an end-to-end AI-Relational database management system. We will demonstrate the capabilities and utility of EVA using three usage scenarios: (1) EVA serves as a backend for an exploratory video analytics interface developed using Streamlit and React, (2) EVA seamlessly integrates with the Python and Data Science ecosystems by allowing users to access EVA in a Python notebook alongside other popular libraries such as Pandas and Matplotlib, and (3) EVA facilitates bulk labeling with Label Studio, a widely-used labeling framework. By optimizing complex vision queries, we illustrate how EVA allows a wide range of application developers to harness the recent advances in computer vision. 

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4. EVA: An End-to-End Exploratory Video Analytics System

   https://doi.org/10.1145/3595360.3595858  

   Kakkar, Gaurav Tarlok; Cao, Jiashen; Chunduri, Pramod; Xu, Zhuangdi; Vyalla, Suryatej Reddy; Dintyala, Prashanth; Prabakaran, Anirudh; Bang, Jaeho; Sengupta, Aubhro; Ravichandran, Kaushik; et al (June 2023, ACM)

   In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analyt- ics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has de- veloped video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical appli- cation. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage RAY’s distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap. 

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5. Dynamics of Explosions in Cylindrical Vented Enclosures: Validation of a Computational Model by Experiments

   https://doi.org/10.3390/fire4010009  

   Ogunfuye, Samuel; Sezer, Hayri; Kodakoglu, Furkan; Farahani, Hamed Farmahini; Rangwala, Ali S.; Akkerman, V’yacheslav (March 2021, Fire)

   Recent explosions with devastating consequences have re-emphasized the relevance of fire safety and explosion research. From earlier works, the severity of the explosion has been said to depend on various factors such as the ignition location, type of a combustible mixture, enclosure configuration, and equivalence ratio. Explosion venting has been proposed as a safety measure in curbing explosion impact, and the design of safety vent requires a deep understanding of the explosion phenomenon. To address this, the Explosion Venting Analyzer (EVA)—a mathematical model predicting the maximum overpressure and characterizing the explosion in an enclosure—has been recently developed and coded (Process Saf. Environ. Prot. 99 (2016) 167). The present work is devoted to methane explosions because the natural gas—a common fossil fuel used for various domestic, commercial, and industrial purposes—has methane as its major constituent. Specifically, the dynamics of methane-air explosion in vented cylindrical enclosures is scrutinized, computationally and experimentally, such that the accuracy of the EVA predictions is validated by the experiments, with the Cantera package integrated into the EVA to identify the flame speeds. The EVA results for the rear-ignited vented methane-air explosion show good agreement with the experimental results. 

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