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Prudent Practices for Designing Malware Experiments: Status Quo and Outlook
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-68768-1_1openalex-snapshot
Botnet Detection Based on Network Behavior
inferredcites →https://doi.org/10.1007/978-0-387-68768-1_3openalex-snapshot
Characterizing Bots’ Remote Control Behavior
inferredcites →https://doi.org/10.1007/978-3-540-70542-0_11openalex-snapshot
Traffic Aggregation for Malware Detection
inferredcites →https://doi.org/10.1007/978-3-540-70542-0_6openalex-snapshot
Learning and Classification of Malware Behavior
inferredcites →https://doi.org/10.1007/978-3-540-73614-1_6openalex-snapshot
Characterizing Bots’ Remote Control Behavior
inferredcites →https://doi.org/10.1007/978-3-540-74320-0_10openalex-snapshot
Automated Classification and Analysis of Internet Malware
inferredcites →https://doi.org/10.1007/978-3-540-85886-7_7openalex-snapshot
BotTracer: Execution-Based Bot-Like Malware Detection
inferredcites →https://doi.org/10.1007/978-3-642-04444-1_15openalex-snapshot
Automatically Generating Models for Botnet Detection
inferredcites →https://doi.org/10.1007/978-3-642-05284-2_11openalex-snapshot
Using Failure Information Analysis to Detect Enterprise Zombies
inferredcites →https://doi.org/10.1007/978-3-642-15512-3_13openalex-snapshot
On Challenges in Evaluating Malware Clustering
inferredcites →https://doi.org/10.1007/978-3-642-15512-3_7openalex-snapshot
BotSwindler: Tamper Resistant Injection of Believable Decoys in VM-Based Hosts for Crimeware Detection
inferredcites →https://doi.org/10.1007/978-3-642-16161-2_2openalex-snapshot
Analyzing and Exploiting Network Behaviors of Malware
inferredcites →https://doi.org/10.1007/978-3-642-23644-0_18openalex-snapshot
Detecting Environment-Sensitive Malware
inferredcites →https://doi.org/10.1109/dsn.2008.4630086openalex-snapshot
Towards an understanding of anti-virtualization and anti-debugging behavior in modern malware
inferredcites →https://doi.org/10.1109/ec2nd.2009.10openalex-snapshot
Walowdac - Analysis of a Peer-to-Peer Botnet
inferredcites →https://doi.org/10.1109/lcn.2006.322210openalex-snapshot
Usilng Machine Learning Technliques to Identify Botnet Traffic
inferredcites →https://doi.org/10.1109/msp.2007.45openalex-snapshot
Toward Automated Dynamic Malware Analysis Using CWSandbox
inferredcites →https://doi.org/10.1109/sp.2009.14openalex-snapshot
Prospex: Protocol Specification Extraction
inferredcites →https://doi.org/10.1109/sp.2010.10openalex-snapshot
Inspector Gadget: Automated Extraction of Proprietary Gadgets from Malware Binaries
inferredcites →https://doi.org/10.1109/sp.2010.25openalex-snapshot
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
inferredcites →https://doi.org/10.1145/1453175.1453186openalex-snapshot
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inferredcites →https://doi.org/10.1145/1774088.1774484openalex-snapshot
Improving the efficiency of dynamic malware analysis
inferredcites →https://doi.org/10.1145/1966913.1966930openalex-snapshot
Boosting the scalability of botnet detection using adaptive traffic sampling
inferredcites →https://doi.org/10.3233/jcs-2010-0410openalex-snapshot
Automatic analysis of malware behavior using machine learning
inferredcites →https://doi.org/10.4304/jnw.5.5.568-576openalex-snapshot
Online Botnet Detection Based on Incremental Discrete Fourier Transform
inferredcites →https://doi.org/10.5555/1855711.1855737openalex-snapshot
Behavioral clustering of HTTP-based malware and signature generation using malicious network traces
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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A Deep Dive Inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores
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Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
inferred← citeshttps://doi.org/10.1145/3607505.3607523openalex-snapshot
Analyzing Cyber Security Research Practices through a Meta-Research Framework
inferred← citeshttps://doi.org/10.1145/3618257.3624800openalex-snapshot
Re-measuring the Label Dynamics of Online Anti-Malware Engines from Millions of Samples
inferred← citeshttps://doi.org/10.1145/3678890.3678913openalex-snapshot
Down to earth! Guidelines for DGA-based Malware Detection
inferred← citeshttps://doi.org/10.1145/3729228openalex-snapshot
Behavior Nets: Context-Aware Behavior Modeling for Code Injection-Based Windows Malware
inferred← citeshttps://doi.org/10.1145/3777555.3777573openalex-snapshot
Fingerprinting Malware Behavior: A Deep Learning Approach
inferred← citeshttps://doi.org/10.1145/3779208.3805971openalex-snapshot
SoK: Systematization, Detection, and Hunting of Windows Malware Persistence Techniques
inferred← citeshttps://doi.org/10.1177/1548512917721755openalex-snapshot
Malware modeling and experimentation through parameterized behavior
inferred← citeshttps://doi.org/10.1177/1548512917737635openalex-snapshot
The protoscience of cybersecurity
inferred← citeshttps://doi.org/10.1184/r1/6469490openalex-snapshot
Towards Automatic Software Lineage Inference
inferred← citeshttps://doi.org/10.13016/m2fx4i-b4zpopenalex-snapshot
MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels
inferred← citeshttps://doi.org/10.13016/m2qtkw-vl04openalex-snapshot
Automatic Yara Rule Generation Using Biclustering
inferred← citeshttps://doi.org/10.13016/m2rt7w-bkokopenalex-snapshot
Malware Detection by Eating a Whole EXE
inferred← citeshttps://doi.org/10.13052/jcsm2245-1439.321openalex-snapshot
Characterizing Evaluation Practices of Intrusion Detection Methods for Smartphones
inferred← citeshttps://doi.org/10.13130/fattori-aristide_phd2014-03-18openalex-snapshot
HARDWARE-ASSISTED VIRTUALIZATION AND ITS APPLICATIONS TO SYSTEMS SECURITY
inferred← citeshttps://doi.org/10.14257/ijsia.2015.9.3.23openalex-snapshot
On the Comparison of Malware Detection Methods Using Data Mining with Two Feature Sets
inferred← citeshttps://doi.org/10.14569/ijacsa.2021.0121016openalex-snapshot
LightGBM-based Ransomware Detection using API Call Sequences
inferred← citeshttps://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
inferred← citeshttps://doi.org/10.14722/ndss.2020.24310openalex-snapshot
When Malware is Packin' Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features
inferred← citeshttps://doi.org/10.14722/ndss.2021.24076openalex-snapshot
Understanding Worldwide Private Information Collection on Android
inferred← citeshttps://doi.org/10.15622/ia.23.3.2openalex-snapshot
Methodology for Collecting Data on the Activity of Malware for Windows OS Based on MITRE ATT&CK
inferred← citeshttps://doi.org/10.17760/d20289366openalex-snapshot
Techniques and solutions for addressing ransomware attacks
inferred← citeshttps://doi.org/10.18293/seke2019-086openalex-snapshot
Prudent Practices for Designing Virtual Desktop Experiments
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3246819/v1openalex-snapshot
Scalable Simulation Algorithm to Generate Large-Scale Botnet Dataset using Role-Mining and Markov Chain
inferred← citeshttps://doi.org/10.22214/ijraset.2021.35426openalex-snapshot
Malware Detection using Deep Learning
inferred← citeshttps://doi.org/10.22214/ijraset.2024.59911openalex-snapshot
Robust Intelligent Malware Detection Using Deep Learning
inferred← citeshttps://doi.org/10.23919/eusipco.2018.8553214openalex-snapshot
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
inferred← citeshttps://doi.org/10.25046/aj050566openalex-snapshot
Malware classification using XGboost-Gradient Boosted Decision Tree
inferred← citeshttps://doi.org/10.3233/ao-160163openalex-snapshot
An ontology of suspicious software behavior
inferred← citeshttps://doi.org/10.3233/jcs-191346openalex-snapshot
Multilayer ransomware detection using grouped registry key operations, file entropy and file signature monitoring
inferred← citeshttps://doi.org/10.3233/jifs-190888openalex-snapshot
Combining multi-features with a neural joint model for Android malware detection
inferred← citeshttps://doi.org/10.33260/zictjournal.v1i1.19openalex-snapshot
Demystifying Ransomware Attacks: Reverse Engineering and Dynamic Malware Analysis of WannaCry for Network and Information Security
inferred← citeshttps://doi.org/10.3390/app112110244openalex-snapshot
Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
inferred← citeshttps://doi.org/10.3390/e24101503openalex-snapshot
Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification
inferred← citeshttps://doi.org/10.3390/s23094355openalex-snapshot
Temporal Data Correlation Providing Enhanced Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation
inferred← citeshttps://doi.org/10.3837/tiis.2020.05.021openalex-snapshot
Automated Analysis Approach for the Detection of High Survivable Ransomware
inferred← citeshttps://doi.org/10.4018/978-1-4666-7381-6.ch019openalex-snapshot
Detecting Botnet Traffic from a Single Host
inferred← citeshttps://doi.org/10.4018/978-1-7998-8545-0.ch004openalex-snapshot
Identification of Cryptographic Vulnerability and Malware Detection in Android
inferred← citeshttps://doi.org/10.4018/ijisp.2017070102openalex-snapshot
Identification of Cryptographic Vulnerability and Malware Detection in Android
inferred← citeshttps://doi.org/10.4108/eai.13-7-2018.156002openalex-snapshot
Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering
inferred← citeshttps://doi.org/10.48175/ijarsct-19519openalex-snapshot
Scalable and Robust Fraud Detection in Distributed Systems
inferred← citeshttps://doi.org/10.48550/arxiv.1311.5612openalex-snapshot
Tracking and Characterizing Botnets Using Automatically Generated Domains
inferred← citeshttps://doi.org/10.48550/arxiv.1609.03020openalex-snapshot
Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection
inferred← citeshttps://doi.org/10.48550/arxiv.1611.03186openalex-snapshot
SoK: Applying Machine Learning in Security - A Survey
inferred← citeshttps://doi.org/10.48550/arxiv.1702.00552openalex-snapshot
Beyond Free Riding: Quality of Indicators for Assessing Participation in Information Sharing for Threat Intelligence
inferred← citeshttps://doi.org/10.48550/arxiv.1710.01367openalex-snapshot
Validating Computer Security Methods: Meta-methodology for an Adversarial Science
inferred← citeshttps://doi.org/10.48550/arxiv.1801.02381openalex-snapshot
Benchmarking Crimes: An Emerging Threat in Systems Security
inferred← citeshttps://doi.org/10.48550/arxiv.1804.08672openalex-snapshot
Prediction in Cyber Security: Complications and Consolations
inferred← citeshttps://doi.org/10.48550/arxiv.1807.07838openalex-snapshot
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
inferred← citeshttps://doi.org/10.48550/arxiv.1905.07059openalex-snapshot
Using Camouflaged Cyber Simulations as a Model to Ensure Validity in Cybersecurity Experimentation
inferred← citeshttps://doi.org/10.48550/arxiv.1910.11376openalex-snapshot
Neurlux: Dynamic Malware Analysis Without Feature Engineering
inferred← citeshttps://doi.org/10.48550/arxiv.2006.09271openalex-snapshot
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification
inferred← citeshttps://doi.org/10.48550/arxiv.2010.09470openalex-snapshot
Dos and Don'ts of Machine Learning in Computer Security
inferred← citeshttps://doi.org/10.53730/ijhs.v6ns6.9818openalex-snapshot
intelligent robust malware detection by implementing deep learning
inferred← citeshttps://doi.org/10.55041/ijsrem16162openalex-snapshot
MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES
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