Scorecard: Machine Learning To Identify Probable Cyber Threats

Core Insights - The article discusses the development of Scorecard, a machine learning-based toolkit designed to identify potential cyber threats from Advanced Persistent Threats (APTs) targeting organizations [4][21][28] Group 1: APT Overview - APTs are sophisticated hacking groups often state-sponsored, with over 40 classified groups posing significant cybersecurity threats to governments and businesses [2][6] - APTs have specific targets and exhibit discernible patterns in their attack strategies, which can be leveraged to predict future threats [12][21] Group 2: Scorecard Functionality - Scorecard provides tailored insights into which APTs are most likely to target specific organizations, allowing for improved cybersecurity strategies [4][5] - The tool uses publicly available information to generate a risk score for companies, indicating their likelihood of being targeted by various APTs [5][23] Group 3: Data Collection and Model Training - The development of Scorecard faced challenges in data acquisition, as information on APTs and their victims is often scarce and not standardized [7][29] - A total of 27 APTs and 170 publicly known company victims were analyzed to train the model, utilizing a combination of automatic and manual data collection methods [10][29] Group 4: Model Performance - The model achieved accuracies of 50%, 85%, and 94% for predicting the top 1, 5, and 10 APTs, respectively, indicating its effectiveness in identifying patterns [21] - Real-world testing of Scorecard on six companies revealed that while high-risk companies scored higher, the differences were not significantly large, suggesting room for model refinement [25][23] Group 5: Future Directions - Future improvements to Scorecard may include enhanced data acquisition methods and the ability to assess companies' public-facing infrastructure against APT tactics [27][30] - The development of Scorecard highlights the potential of machine learning in cybersecurity, with aspirations for it to actively counter APT strategies in the future [28][27]