Design of a Cyber Threat Information Collection System for Cyber Attack Correlation

Kim, Nakhyun; Lee, Seulgi; Cho, Hyeisun; Kim, Byun-Ik; Jun, Moon-Seog

doi:10.1109/platcon.2018.8472775

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…This research uses an innovative AI-driven RPA methodology to acquire, manage, and store global cyber threat statistics. The presented methodology of data aggregation is fully automated and much more robust in comparison with existing systems [48][49][50][51][52]. The comprehensive cyber data deriving from this study have been made available to researchers and academics for the first time in order to facilitate open-source cyber research.…”

Section: Discussionmentioning

confidence: 99%

A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting

Sufi

2024

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In the face of escalating cyber threats that have contributed significantly to global economic losses, this study presents a comprehensive dataset capturing the multifaceted nature of cyber-attacks across 225 countries over a 14-month period from October 2022 to December 2023. The dataset, comprising 77,623 rows and 18 fields, provides a detailed chronology of cyber-attacks, categorized into eight critical dimensions: spam, ransomware, local infection, exploit, malicious mail, network attack, on-demand scan, and web threat. The dataset also includes ranking data, offering a comparative view of countries’ susceptibility to different cyber threats. The results reveal significant variations in the frequency and intensity of cyber-attacks across different countries and attack types. The data were meticulously compiled using modern AI-based data acquisition techniques, ensuring a high degree of accuracy and comprehensiveness. Correlation tests against the eight types of cyber-attacks resulted in the determination that on-demand scan and local infection are highly correlated, with a correlation coefficient of 0.93. Lastly, neural-network-based forecasting of these highly correlated factors (i.e., on-demand scan and local infection) reveals a similar pattern of prediction, with an MSE and an MAPE of 1.616 and 80.13, respectively. The study’s conclusions provide critical insights into the global landscape of cyber threats, highlighting the urgent need for robust cybersecurity measures.

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Section: Discussionmentioning

confidence: 99%

A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting

Sufi

2024

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“…The same authors (Deliu et al, 2018) (Kim et al, 2018). The system was used to collect and extract Indicators of Compromise (IOC) from Open-Source Intelligence (OSINT) feeds and correlate them.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Cyber Analytics Data Collection Framework

Maosa

Ouazzane

Sowinski-Mydlarz

2022

International Journal of Information Security and Privacy

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In cyber security, it is critical that event data is collected in as near real time as possible to enable early detection and response to threats. Performing analytics from event logs stored in databases slows down the response time due to the time cost of database insertion and retrieval operations. The authors present a data collection framework that minimizes the need for long-term storage. Events are buffered in memory, up to a configurable threshold, before being streamed in real time using live streaming technologies. The framework deploys virtualized data collecting agents that ingest data from multiple sources including threat intelligence. The framework enables the correlation of events from various sources, improving detection precision. The authors have tested the framework in a real time, machine-learning-based threat detection system. The results show a time gain of 300 milliseconds in transmission time from event capture to analytics system, compared with storage-based collection frameworks. Threat detection was measured at 95%, which is comparable to the benchmark snort IDS.

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“…• Correlation: Detection of relationships between people, events or pieces of data in general [54]. Strong related features are specially valuable to reveal those non-explicit associations existing in the dataset.…”

Section: Osint Knowledge Extractionmentioning

confidence: 99%

The Not Yet Exploited Goldmine of OSINT: Opportunities, Open Challenges and Future Trends

et al. 2020

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The amount of data generated by the current interconnected world is immeasurable, and a large part of such data is publicly available, which means that it is accessible by any user, at any time, from anywhere in the Internet. In this respect, Open Source Intelligence (OSINT) is a type of intelligence that actually benefits from that open natureby collecting, processing and correlating points of the whole cyberspace to generate knowledge. In fact, recent advances in technology are causing OSINT to currently evolve at a dizzying rate, providing innovative data-driven and AI-powered applications for politics, economy or society, but also offering new lines of action against cyberthreats and cybercrime. The paper at hand describes the current state of OSINT and makes a comprehensive review of the paradigm, focusing on the services and techniques enhancing the cybersecurity field. On the one hand, we analyze the strong points of this methodology and propose numerous ways to apply it to cybersecurity. On the other hand, we cover the limitations when adopting it. Considering there is a lot left to explore in this ample field, we also enumerate some open challenges to be addressed in the future. Additionally, we study the role of OSINT in the public sphere of governments, which constitute an ideal landscape to exploit open data.

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Design of a Cyber Threat Information Collection System for Cyber Attack Correlation

Cited by 6 publications

References 2 publications

A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting

A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting

Real-Time Cyber Analytics Data Collection Framework

The Not Yet Exploited Goldmine of OSINT: Opportunities, Open Challenges and Future Trends

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