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Detection of Intrusions and Malware, and Vulnerability Assessment

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Cover of 'Detection of Intrusions and Malware, and Vulnerability Assessment'

Table of Contents

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    Book Overview
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    Chapter 1 Hybrid Pruning: Towards Precise Pointer and Taint Analysis
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    Chapter 2 Establishing the Contaminating Effect of Metadata Feature Inclusion in Machine-Learned Network Intrusion Detection Models
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    Chapter 3 Extended Abstract: Effective Call Graph Fingerprinting for the Analysis and Classification of Windows Malware
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    Chapter 4 COBRA-GCN: Contrastive Learning to Optimize Binary Representation Analysis with Graph Convolutional Networks
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    Chapter 5 Prioritizing Antivirus Alerts on Internal Enterprise Machines
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    Chapter 6 VANDALIR: Vulnerability Analyses Based on Datalog and LLVM-IR
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    Chapter 7 Branch Different - Spectre Attacks on Apple Silicon
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    Chapter 8 MPKAlloc: Efficient Heap Meta-data Integrity Through Hardware Memory Protection Keys
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    Chapter 9 A Human in Every APE: Delineating and Evaluating the Human Analysis Systems of Anti-Phishing Entities
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    Chapter 10 Amplification Chamber: Dissecting the Attack Infrastructure of Memcached DRDoS Attacks
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    Chapter 11 Consistency is All I Ask: Attacks and Countermeasures on the Network Context of Distributed Honeypots
Attention for Chapter 6: VANDALIR: Vulnerability Analyses Based on Datalog and LLVM-IR
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Chapter title
VANDALIR: Vulnerability Analyses Based on Datalog and LLVM-IR
Chapter number 6
Book title
Detection of Intrusions and Malware, and Vulnerability Assessment
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-09484-2_6
Book ISBNs
978-3-03-109483-5, 978-3-03-109484-2
Authors

Schilling, Joschua, Müller, Tilo

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