Artificial Intelligence (AI) and cybersecurity intersect in ways that offer significant opportunities and lead to considerable threats. Recognizing the critical importance of this convergence, major entities like the National Science Foundation (NSF), the National Science Technology Council (NSTC), and the National Academies of Sciences (NAS) have prioritized enhancing AI capabilities for cybersecurity. This minitrack focuses on AI and cybersecurity within broad domains, including collaborative inter-organizational environments and shared technologies. The five papers in this minitrack can potentially contribute to advancing the development of AI for cybersecurity, addressing critical cybersecurity challenges for AI, and proposing innovative solutions.

The accelerated growth of computing technologies has provided interdisciplinary teams a platform for producing innovative research at an unprecedented speed. Advanced scientific cyberinfrastructures, in particular, provide data storage, applications, software, and other resources to facilitate the development of critical scientific discoveries. Users of these environments often rely on custom developed virtual machine (VM) images that are comprised of a diverse array of open source applications. These can include vulnerabilities undetectable by conventional vulnerability scanners. This research aims to identify the installed applications, their vulnerabilities, and how they vary across images in scientific cyberinfrastructure. We propose a novel unsupervised graph embedding framework that captures relationships between applications, as well as vulnerabilities identified on corresponding GitHub repositories. This embedding is used to cluster images with similar applications and vulnerabilities. We evaluate cluster quality using Silhouette, Calinski-Harabasz, and Davies-Bouldin indices, and application vulnerabilities through inspection of selected clusters. Results reveal that images pertaining to genomics research in our research testbed are at greater risk of high-severity shell spawning and data validation vulnerabilities.

The intentional and non-intentional use of social media platforms resulting in digital wildfires of misinformation has increased significantly over the last few years. However, the factors that influence this rapid spread in the online space remain largely unknown. We study how believability and intention to share information are influenced by multiple factors, in addition to confirmation bias. We conducted an experiment where a mix of true and false articles was evaluated by study participants. Using hierarchical linear modelling to analyze our data, we found that, in addition to confirmation bias, believability is influenced by source endorser credibility and argument quality, both of which are moderated by the type of information – true or false. Source likeability had a positive main effect on believability. After controlling for belief and confirmation bias, intention to share information was affected by source endorser credibility and information source likeability.

The scientific cyberinfrastructure community heavily relies on public internet-based systems (e.g., GitHub) to share resources and collaborate. GitHub is one of the most powerful and popular systems for open source collaboration that allows users to share and work on projects in a public space for accelerated development and deployment. Monitoring GitHub for exposed vulnerabilities can save financial cost and prevent misuse and attacks of cyberinfrastructure. Vulnerability scanners that can interface with GitHub directly can be leveraged to conduct such monitoring. This research aims to proactively identify vulnerable communities within scientific cyberinfrastructure. We use social network analysis to construct graphs representing the relationships amongst users and repositories. We leverage prevailing unsupervised graph embedding algorithms to generate graph embeddings that capture the network attributes and nodal features of our repository and user graphs. This enables the clustering of public cyberinfrastructure repositories and users that have similar network attributes and vulnerabilities. Results of this research find that major scientific cyberinfrastructures have vulnerabilities pertaining to secret leakage and insecure coding practices for high-impact genomics research. These results can help organizations address their vulnerable repositories and users in a targeted manner.

The intentional and non-intentional use of social media platforms resulting in digital wildfires of misinformation has increased significantly over the last few years. However, the factors that influence this rapid spread in the online space remain largely unknown. We study how believability and intention to share information are influenced by multiple factors in addition to confirmation bias. We conducted an experiment where a mix of true and false articles were evaluated by study participants. Using hierarchical linear modelling to analyze our data, we found that in addition to confirmation bias, believability is influenced by source endorser credibility and argument quality, both of which are moderated by the type of information – true or false. Source likeability also had a positive main effect on believability. After controlling for belief and confirmation bias, intention to share information was affected by source endorser credibility and information source likeability.

The frequency and costs of cyber-attacks are increasing each year. By the end of 2019, the total cost of data breaches is expected to reach $2.1 trillion through the ever-growing online presence of enterprises and their consumers. The tools to perform these attacks and the breached data can often be purchased within the Dark-net. Many of the threat actors within this realm use its various platforms to broker, discuss, and strategize these cyber-threat assets. To combat these attacks, researchers are developing Cyber-Threat Intelligence (CTI) tools to proactively monitor the ever-growing online hacker community. This paper will detail the creation and use of a CTI tool that leverages a social network to identify cyber-threats across major Dark-net data sources. Through this network, emerging threats can be quickly identified so proactive or reactive security measures can be implemented.

With the rapid development of blockchain platforms, it is important that different implementations are tested and analyzed for comparative purposes. One such implementation is Hyperledger Sawtooth, a new member of the Hyperledger family. Sawtooth blockchain is a permissioned implementation developed in part by Intel. While research has been done on Hyperledger Fabric, research on Sawtooth is not well documented. Using the Hyperledger Caliper benchmarking tool, we aim to test the performance of the blockchain and identify potential issues.

Cyber-Physical Systems (CPSs) are engineered systems seamlessly integrating computational algorithms and physical components. CPS advances offer numerous benefits to domains such as health, transportation, smart homes and manufacturing. Despite these advances, the overall cybersecurity posture of CPS devices remains unclear. In this paper, we provide knowledge on how to improve CPS resiliency by evaluating and comparing the accuracy, and scalability of two popular vulnerability assessment tools, Nessus and OpenVAS. Accuracy and suitability are evaluated with a diverse sample of pre-defined vulnerabilities in Industrial Control Systems (ICS), smart cars, smart home devices, and a smart water system. Scalability is evaluated using a large-scale vulnerability assessment of 1,000 Internet accessible CPS devices found on Shodan, the search engine for the Internet of Things (IoT). Assessment results indicate several CPS devices from major vendors suffer from critical vulnerabilities such as unsupported operating systems, OpenSSH vulnerabilities allowing unauthorized information disclosure, and PHP vulnerabilities susceptible to denial of service attacks.

Scientific advances of higher education institutions make them attractive targets for malicious cyber-attacks. Modern scanners such as Nessus and Burp can pinpoint an organization's vulnerabilities for subsequent mitigation. However, the remediation reports generated from the tools often cause significant information overload while failing to provide actionable solutions. Consequently, higher education institutions lack the appropriate knowledge to improve their cybersecurity posture. In this study, we conduct a large-scale vulnerability assessment of 272 higher education institutions. From the results, we identified vulnerabilities that fail to provide comprehensive remediation strategies. Selected flaws are recreated and remediated in a virtual environment to develop enhanced, automated reporting mechanisms that provide succinct reports to enable the efficient vulnerability remediation. Our enhanced reports address 27.80% of vulnerabilities found in scanned higher education institutions.

Cyber threats have emerged as a key societal concern. To counter the growing threat of cyber-attacks, organizations, in recent years, have begun investing heavily in developing Cyber Threat Intelligence (CTI). Fundamentally a data driven process, many organizations have traditionally collected and analyzed data from internal log files, resulting in reactive CTI. The online hacker community can offer significant proactive CTI value by alerting organizations to threats they were not previously aware of. Amongst various platforms, forums provide the richest metadata, data permanence, and tens of thousands of freely available Tools, Techniques, and Procedures (TTP). However, forums often employ anti-crawling measures such as authentication, throttling, and obfuscation. Such limitations have restricted many researchers to batch collections. This exploratory study aims to (1) design a novel web crawler augmented with numerous anti-crawling countermeasures to collect hacker exploits on an ongoing basis, (2) employ a state-of-the-art deep learning approach, Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), to automatically classify exploits into pre-defined categories on-the-fly, and (3) develop interactive visualizations enabling CTI practitioners and researchers to explore collected exploits for proactive, timely CTI. The results of this study indicate, among other findings, that system and network exploits are shared significantly more than other exploit types.

Supervisory Control and Data Acquisition (SCADA) systems allow operators to control critical infrastructure. Vendors are increasingly integrating Internet technology into these devices, making them more susceptible to cyberattacks. Identifying and assessing vulnerabilities of SCADA devices using Shodan, a search engine that contains records about publicly available Internet-connected devices, can help mitigate cyberattacks. The authors present a principled approach to systematically identify all SCADA devices on Shodan and then assess the vulnerabilities of the devices with a state-of-the-art tool.

Internet enabled medical devices offer patients with a level of convenience. In recent years, the healthcare industry has seen a surge in the number of cyber-attacks. Given the potentially fatal impact of a compromised medical device, this study aims to identify vulnerabilities of medical devices. Our approach uses Shodan to obtain a large collection of IP addresses that will be passed through Nessus to verify if any vulnerabilities exist. We determined some devices manufactured by primary vendors such as Omron Corporation, FORA, Roche, and Bionet contain serious vulnerabilities such as Dropbear SSH Server and MS17-010. These allow remote execution of code and authentication bypassing potentially giving attackers control of their systems.

The Internet of Things becomes more defined year after year. Companies are looking for novel ways to implement various smart capabilities into their products that increase interaction between users and other network devices. While many smart devices offer greater convenience and value, they also present new security vulnerabilities that can have a detrimental effect on consumer privacy. Given the societal impact of IoT device vulnerabilities, this study aims to perform a large-scale vulnerability assessment of consumer IoT devices exposed on the Internet. Specifically, Shodan is used to collect a large testbed of consumer IoT devices which are then passed through Nessus to determine whether potential vulnerabilities exist. Results of this study indicate that a significant number of consumer IoT devices are vulnerable to exploits that can compromise user information and privacy.

Current estimates place the global economic cost of cybercrime at $450 billion annually. Since 2003, the international Intelligence and Security Informatics (ISI) community (1,500+ members) has been recognized as leading experts in computationally collection and analysis of security datasets for high-impact and emerging topics such as Cyber Threat Intelligence, terrorism informatics, and Big Data cybersecurity. Despite ISI’s numerous successes, no repository of ISI datasets and analytic tools currently exists. This paper presents the AZSecure Data system, an effort which strives to promote research data sharing and reproducibility in cybersecurity. AZSecure Data is a multi-year, multi-institution project generously funded by the National Science Foundation (NSF) Data Infrastructure Building Blocks (DIBBs) program. Designed around library science principles of access and discovery, the AZSecure Data system has 22 collections across seven data types: forums and chat logs, tweets, malware, network traffic phishing, newspapers, and other websites. AZSecure Data has seen international usage, with several datasets already used for scholarly publication. Keywords— cybersecurity, data repository, cybersecurity big data analytics, cyber threat intelligence, data sharing

Cybersecurity is a critical concern in society today. One common avenue of attack for malicious hackers is exploiting vulnerable websites. It is estimated that there are over one million websites that are attacked daily. Two emerging targets of such attacks are Supervisory Control and Data Acquisition (SCADA) devices and scientific instruments. Vulnerability assessment tools can help provide owners of these devices with the knowledge on how to protect their infrastructure. However, owners face difficulties in identifying which tools are ideal for their assessments. This research aims to benchmark two state-of-the-art vulnerability assessment tools, Nessus and Burp Suite, in the context of SCADA devices and scientific instruments. We specifically focus on identifying the accuracy, scalability, and vulnerability results of the scans. Results of our study indicate that both tools together can provide a comprehensive assessment of the vulnerabilities in SCADA devices and scientific instruments.

Cyber-attacks are constantly increasing and can prove difficult to mitigate, even with proper cybersecurity controls. Currently, cyber threat intelligence (CTI) efforts focus on internal threat feeds such as antivirus and system logs. While this approach is valuable, it is reactive in nature as it relies on activity which has already occurred. CTI experts have argued that an actionable CTI program should also provide external, open information relevant to the organization. By finding information about malicious hackers prior to an attack, organizations can provide enhanced CTI and better protect their infrastructure. Hacker forums can provide a rich data source in this regard. This research aims to proactively identify mobile malware and associated key authors. Specifically, we use a state-of-the-art neural network architecture, recurrent neural networks, to identify mobile malware attachments followed by social network analysis techniques to determine key hackers disseminating the mobile malware. Results of this study indicate that many identified attachments are zipped Android apps made by threat actors holding administrative positions in hacker forums. Our identified mobile malware attachments are consistent with some of the emerging mobile malware concerns as highlighted by industry leaders.

Performing port scans through Tor is a way to hide the source's IP address from the target. Researchers hoping to source their own scans benefit from a means of scanning that helps them to anonymize themselves from targets that may potentially retaliate as the result of being scanned. Though effective in providing anonymization during scanning, it is not scalable to the point of scanning the entire IPv4 Address space on multiple ports, as scans take considerably longer to execute through Tor. This paper specifically explores using a third-party data source to target specific areas of interest in the IPv4 range and then scanning those areas anonymously with parallelized scanners as an effective way to anonymously collect internet scan data. The results demonstrate the feasibility of this approach.

The anonymizing network Tor is examined as one method of anonymizing port scanning tools and avoiding identification and retaliation. Performing anonymized port scans through Tor is possible using Nmap, but parallelization of the scanning processes is required to accelerate the scan rate.

The purpose of this paper is to discuss how using Gephi to visualize the open ports at IP addresses in Shodan may provide a means of identifying SCADA devices. Visualizations were created using both IP addresses and open ports as nodes. Modularity, centralities, and layout were used to enhance the visualizations. From these visualizations we hope to gather a better understanding of what devices are on the network.