Enhancing Cyber Security through Predictive Analytics: Real-Time Threat Detection and Response (2407.10864v1)

Abstract: This research paper aims to examine the applicability of predictive analytics to improve the real-time identification and response to cyber-attacks. Today, threats in cyberspace have evolved to a level where conventional methods of defense are usually inadequate. This paper highlights the significance of predictive analytics and demonstrates its potential in enhancing cyber security frameworks. This research integrates literature on using big data analytics for predictive analytics in cyber security, noting that such systems could outperform conventional methods in identifying advanced cyber threats. This review can be used as a framework for future research on predictive models and the possibilities of implementing them into the cyber security frameworks. The study uses quantitative research, using a dataset from Kaggle with 2000 instances of network traffic and security events. Logistic regression and cluster analysis were used to analyze the data, with statistical tests conducted using SPSS. The findings show that predictive analytics enhance the vigilance of threats and response time. This paper advocates for predictive analytics as an essential component for developing preventative cyber security strategies, improving threat identification, and aiding decision-making processes. The practical implications and potential real-world applications of the findings are also discussed.

• The paper establishes that predictive analytics shifts cyber security from reactive to proactive, enhancing real-time threat detection.
• It employs quantitative methods on 2,000 network data points using models like logistic regression, cluster analysis, and neural networks.
• The study reveals that while individual predictors show limited variance, integrating AI-driven insights significantly improves security operations decision-making.

This paper, "Enhancing Cyber Security through Predictive Analytics: Real-Time Threat Detection and Response" (Danish, 15 Jul 2024 ), investigates the application of predictive analytics to improve cyber security systems, particularly in real-time threat detection and response. It highlights the limitations of conventional, signature-based, and reactive security methods which struggle with evolving threats, zero-day attacks, high false positive rates, and scalability issues.

The research aims to demonstrate how predictive analytics can shift cyber security from a reactive posture to a proactive and preventive one. Key research questions explored include the effectiveness of predictive analytics in real-time threat detection and response, the types of patterns and anomalies predictive models can identify that traditional methods miss, and how predictive analytics can enhance decision-making in security operations centers.

The paper reviews the evolution of predictive analytics in cyber security, emphasizing its reliance on large volumes of data from various sources like network traffic and security logs. It discusses the role of AI and Machine Learning (ML), including both supervised (trained on labeled data to identify known threats) and unsupervised (identifying anomalies in unlabeled data to detect unknown threats) learning models. The literature review underscores the practical gap between theoretical predictive models and their real-world implementation, noting challenges related to data quality, model complexity, integration into existing infrastructure, and the need for continuous updates to counter new threats.

The methodology employed is quantitative, using a dataset of 2000 instances of network traffic and security events obtained from Kaggle. This dataset includes features such as source/destination IP/port, packet length, traffic type, anomaly scores, and threat indicators. The data underwent preparation steps including cleaning, handling missing values, removing duplicates, converting categorical data, and normalization. The dataset was partitioned into training, validation, and test sets (70/15/15 split). Predictive analysis techniques mentioned include logistic regression and cluster analysis, among others like decision trees and neural networks. Statistical analysis was primarily conducted using SPSS, applying descriptive analysis, correlation analysis, regression analysis, chi-squared tests, and t-tests to evaluate the data and model relationships. The choice of a 2000-row sample size was justified based on achieving sufficient statistical power, representing data diversity, balancing overfitting/underfitting risks, and remaining computationally manageable.

The results section presents the statistical analysis findings:

• Descriptive Statistics: Showed significant variability in key features like anomaly scores, user information, and device information, deemed essential for training robust models.
• Correlation Analysis: Revealed some statistically significant, albeit weak in some cases, correlations between variables (e.g., negative correlation between Source Port and Protocol, positive correlation between Traffic Type and Packet Type, negative correlation between Geo-location Data and Device Information, and a significant negative correlation between Attack Type and Attack Signature).
• Regression Analysis: A logistic regression model predicting 'Action Taken' using Attack Type, Packet Length, and Anomaly Scores showed a statistically significant model overall (p < 0.01) but explained a very small portion of the variance (R² = 0.007). Attack Type was the only statistically significant predictor (p < 0.01) in this specific model, while Packet Length and Anomaly Scores were not significant predictors.
• Chi-squared Tests: Indicated no significant association between the tested categorical variables and the dependent variable 'Action Taken'.
• T-Test Analysis: Showed no significant difference in Packet Length between the groups being compared.

The discussion section interprets these results, confirming that predictive analytics can significantly enhance the ability to identify and respond to cyber-attacks in real-time and detect subtle patterns often missed by traditional methods, thus supporting the research questions. The use of models like cluster analysis and anomaly detection was highlighted as effective in identifying complex threat features. Furthermore, the paper suggests that integrating predictive analytics outputs (e.g., visualized on dashboards) improves decision-making in security operations centers by providing actionable, predictive insights. While the regression results for predicting 'Action Taken' showed limited predictive power for the selected variables, the overall discussion emphasizes the broader potential observed through the application of predictive modeling and pattern recognition techniques to cyber security data.

In conclusion, the paper asserts that predictive analytics provides a significant advantage over conventional reactive methods in cyber security by enhancing real-time threat identification, reducing response times, and improving overall security management. It acknowledges challenges related to data integration, continuous model updates, and integration into existing systems. Future research directions include focusing on real-time data integration techniques and developing more adaptive learning algorithms to improve the accuracy and timeliness of threat detection in dynamic cyber environments.