Integrating Safety Analyses and Component-Based Design 1st edition by Dominik Domis, Mario Trapp – Ebook PDF Instant Download/Delivery. 3540876977, 978-3540876977
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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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