Finding Corrupted Computers Using Imperfect Intrusion Prevention System Event Data 1st edition by Danielle Chrun, Michel Cukier, Gerry Sneeringer ISBN 3540876977 9783540876977

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ISBN 10:       3540876977
ISBN 13: 978-3540876977
Author:     Danielle Chrun, Michel Cukier, Gerry Sneeringer    

With the increase of attacks on the Internet, a primary concern for organizations is how to protect their network. The objectives of a security team are 1) to prevent external attackers from launching successful attacks against organization computers that could become compromised, 2) to ensure that organization computers are not vulnerable (e.g., fully patched) so that in either case the organization computers do not start launching attacks. The security team can monitor and block malicious activity by using devices such as intrusion prevention systems. However, in large organizations, such monitoring devices could record a high number of events. The contributions of this paper are 1) to introduce a method that ranks potentially corrupted computers based on imperfect intrusion prevention system event data, and 2) to evaluate the method based on empirical data collected at a large organization of about 40,000 computers. The evaluation is based on the judgment of a security expert of which computers were indeed corrupted. On the one hand, we studied how many computers classified as of high concern or of concern were indeed corrupted (i.e., true positives). On the other hand, we analyzed how many computers classified as of lower concern were in fact corrupted (i.e., false negatives).

Finding Corrupted Computers Using Imperfect Intrusion Prevention System Event Data 1st Table of contents:

Introduction

• Overview of Intrusion Prevention Systems (IPS)
• Importance of Detecting Corrupted Computers in Network Security
• Challenges of Using Imperfect IPS Event Data
• Motivation for the Study and Research Goals
• Structure of the Paper

Background and Related Work

• Intrusion Prevention Systems: Principles and Technologies
• Limitations and Imperfections in IPS Event Data
• Existing Approaches for Detecting Corrupted Computers
• Machine Learning and Statistical Methods in Intrusion Detection
• Review of Related Work on Event Data and System Integrity Detection

Problem Definition

• Formal Definition of the Problem: Identifying Corrupted Computers from IPS Data
• Challenges in Using Imperfect Data for Detection
• Characteristics of Corrupted Systems and their Signatures
• Impact of False Positives and False Negatives on System Performance
• The Need for Accurate and Robust Detection Techniques

Data Collection and Event Data Analysis

• Sources of IPS Event Data and Types of Events Collected
• Data Preprocessing Techniques for Imperfect Event Data
• Noise and Anomalies in IPS Data
• Identifying Key Features and Patterns in IPS Events
• Statistical Analysis of IPS Event Data

Detection Methods for Identifying Corrupted Computers

• Signature-Based Detection Techniques
• Anomaly Detection Models for Identifying System Corruption
• Machine Learning Approaches for Corrupted Computer Detection
• Hybrid Methods: Combining Multiple Detection Approaches
• Statistical Methods for Quantifying Uncertainty in IPS Data

Evaluation of Detection Techniques

• Performance Metrics for Detection Methods (Accuracy, Precision, Recall)
• Comparison of Various Detection Algorithms
• Handling Imperfect Event Data in Evaluation
• Experimental Setup and Data Sets Used for Evaluation
• Case Studies and Examples of Detection Results

Challenges in Corrupted Computer Detection

• Dealing with Imperfect, Incomplete, or Missing Data
• Effectiveness of Detection Methods in Real-World Scenarios
• Addressing Scalability Issues in Large-Scale Networks
• Minimizing False Alerts and Overcoming Data Bias
• Impact of Evolving Threats on Detection Models

Improving Detection Accuracy

• Techniques for Reducing Data Imperfections and Noise
• Enhancing Detection Models with Feature Engineering
• Use of Ensemble Methods for Robust Detection
• Adaptive Models that Improve with Continuous Data Feedback
• Incorporating Contextual Information for Better Detection

Practical Applications and Case Studies

• Application of Detection Methods in Real Network Environments
• Case Study: Detecting Corrupted Computers in an Enterprise Network
• Case Study: Applying Detection Methods in Critical Infrastructure
• Real-World Performance and Challenges Encountered
• Implementing a Real-Time Detection System Based on IPS Data

Future Directions and Open Problems

• Enhancing IPS Systems for Better Data Quality
• Leveraging Advanced Machine Learning Techniques for Improved Detection
• Integrating IPS Event Data with Other Security Tools for Comprehensive Detection
• Exploring New Approaches for Detecting Emerging Threats
• Open Research Problems in the Field of Corrupted Computer Detection

Conclusion

• Summary of Key Findings
• Contributions of the Research to Intrusion Detection Systems
• Final Thoughts on Detecting Corrupted Computers Using Imperfect IPS Data
• Outlook on the Future of IPS and Corruption Detection

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