Integrating Safety Analyses and Component-Based Design 1st edition by Dominik Domis, Mario Trapp ISBN 3540876977 9783540876977

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Product details:

ISBN 10:       3540876977
ISBN 13: 978-3540876977
Author:         Dominik Domis, Mario Trapp

In recent years, awareness of how software impacts safety has increased rapidly. Instead of regarding software as a black box, more and more standards demand safety analyses of software architectures and software design. Due to the complexity of software-intensive embedded systems, safety analyses easily become very complex, time consuming, and error prone. To overcome these problems, safety analyses have to be integrated into the complete development process as tightly as possible. This paper introduces an approach to integrating safety analyses into a component-oriented, model-based software engineering approach. The reasons for this are twofold: First, component- and model-based development have already been proven in practical use to handle complexity and reduce effort. Second, they easily support the integration of functional and non-functional properties into design, which can be used to integrate safety analyses.

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

Introduction

• Overview of the Problem
• The Role of Intrusion Prevention Systems (IPS)
• Motivation for Finding Corrupted Computers
• Objectives and Structure of the Paper

Background and Related Work

• Overview of Intrusion Detection and Prevention Systems
• Imperfect Event Data in IPS
• Previous Research on Anomaly Detection and System Integrity
• Related Approaches for Identifying Compromised Systems

Intrusion Prevention Systems and Event Data

• How IPS Work: Detection and Prevention Mechanisms
• Types of Events Captured by IPS
• Challenges in Event Data Collection and Accuracy
• Limitations of IPS Event Data

Approaches to Detecting Corrupted Computers

• Defining Corruption in the Context of Intrusion Prevention
• Techniques for Anomaly Detection in Imperfect Data
• Statistical Methods for Detecting Corruption
• Machine Learning Approaches for Classification and Detection

Data Preprocessing and Feature Extraction

• Cleaning and Filtering Imperfect IPS Event Data
• Selecting Relevant Features for Corruption Detection
• Handling Missing or Inconsistent Data
• Normalization and Transformation of Event Data

Detection Algorithms and Models

• Supervised vs. Unsupervised Learning Approaches
• Algorithms for Anomaly Detection in Network Traffic
• Classification Models for Identifying Corrupted Systems
• Evaluation Metrics for Detection Performance

Experimental Setup and Evaluation

• Description of the Dataset Used for Evaluation
• Experimental Design and Testbed Setup
• Comparison of Detection Methods and Algorithms
• Performance Evaluation and Results Analysis

Challenges in Real-World Detection

• Dealing with Noise and False Positives
• Scalability Issues in Large-Scale Networks
• Real-Time Detection Constraints
• Handling Evolving Attack Techniques and System Changes

Applications and Case Studies

• Use of Corruption Detection in Enterprise Networks
• Case Study: Detecting Corrupted Systems in a Corporate Environment
• Applications in Security Incident Response and Forensics

Future Directions and Open Problems

• Improvements to Event Data Collection and Accuracy
• Enhancing Detection Algorithms for Evolving Threats
• Integrating Corruption Detection with Other Security Tools
• Potential Use of AI and Deep Learning for Advanced Detection

Conclusion

• Summary of Key Findings
• Implications for Security Practices
• Final Thoughts and Future Research Directions

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