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Detection of Intrusions and Malware, and Vulnerability Assessment

Overview of attention for book
Cover of 'Detection of Intrusions and Malware, and Vulnerability Assessment'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Subverting Operating System Properties Through Evolutionary DKOM Attacks
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    Chapter 2 DeepFuzz: Triggering Vulnerabilities Deeply Hidden in Binaries
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    Chapter 3 AutoRand: Automatic Keyword Randomization to Prevent Injection Attacks
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    Chapter 4 AVRAND: A Software-Based Defense Against Code Reuse Attacks for AVR Embedded Devices
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    Chapter 5 Towards Vulnerability Discovery Using Staged Program Analysis
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    Chapter 6 Comprehensive Analysis and Detection of Flash-Based Malware
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    Chapter 7 Reviewer Integration and Performance Measurement for Malware Detection
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    Chapter 8 On the Lack of Consensus in Anti-Virus Decisions: Metrics and Insights on Building Ground Truths of Android Malware
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    Chapter 9 Probfuscation: An Obfuscation Approach Using Probabilistic Control Flows
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    Chapter 10 RAMBO: Run-Time Packer Analysis with Multiple Branch Observation
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    Chapter 11 Detecting Hardware-Assisted Virtualization
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    Chapter 12 Financial Lower Bounds of Online Advertising Abuse
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    Chapter 13 Google Dorks: Analysis, Creation, and New Defenses
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    Chapter 14 Flush+Flush: A Fast and Stealthy Cache Attack
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    Chapter 15 Rowhammer.js: A Remote Software-Induced Fault Attack in JavaScript
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    Chapter 16 Detile: Fine-Grained Information Leak Detection in Script Engines
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    Chapter 17 Understanding the Privacy Implications of ECS
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    Chapter 18 Analysing the Security of Google’s Implementation of OpenID Connect
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    Chapter 19 Leveraging Sensor Fingerprinting for Mobile Device Authentication
  21. Altmetric Badge
    Chapter 20 MtNet: A Multi-Task Neural Network for Dynamic Malware Classification
  22. Altmetric Badge
    Chapter 21 Adaptive Semantics-Aware Malware Classification
Overall attention for this book and its chapters
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
3 tweeters

Readers on

mendeley
18 Mendeley
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Title
Detection of Intrusions and Malware, and Vulnerability Assessment
Published by
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-40667-1
ISBNs
978-3-31-940666-4, 978-3-31-940667-1
Editors

Juan Caballero, Urko Zurutuza, Ricardo J. Rodríguez

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 33%
Student > Master 3 17%
Lecturer 1 6%
Professor > Associate Professor 1 6%
Unknown 7 39%
Readers by discipline Count As %
Computer Science 9 50%
Engineering 1 6%
Unknown 8 44%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 April 2018.
All research outputs
#7,125,228
of 12,846,518 outputs
Outputs from Lecture notes in computer science
#3,471
of 7,332 outputs
Outputs of similar age
#111,321
of 261,122 outputs
Outputs of similar age from Lecture notes in computer science
#44
of 103 outputs
Altmetric has tracked 12,846,518 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,332 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 261,122 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.