Tracking concept drift in malware families

No abstract is available for this paper.

• Publication year

  2012

• Venue

  Security and Artificial Intelligence

• Publication date

  2012-10-19

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/2381896.2381910
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• On Challenges in Evaluating Malware Clustering

  2010cited by this paper
• A Survey of Binary Similarity and Distance Measures

  2010cited by this paper
• Extracting compiler provenance from program binaries

  2010cited by this paper
• Learning under Concept Drift: an Overview

  2010cited by this paper
• Evaluation of malware phylogeny modelling systems using automated variant generation

  2009influential reference
• ANTIDOTE: understanding and defending against poisoning of anomaly detectors

  2009cited by this paper
• Surreptitious Software: Obfuscation, Watermarking, and Tamperproofing for Software Protection

  2009cited by this paper
• Adaptive concept drift detection

  2009influential reference
• Scalable, Behavior-Based Malware Clustering

  2009cited by this paper
• Cloud-based malware detection for evolving data streams

  2008cited by this paper
• Unknown Malcode Detection Using OPCODE Representation

  2008cited by this paper
• A Study of the Packer Problem and Its Solutions

  2008cited by this paper
• A Hybrid Model to Detect Malicious Executables

  2007cited by this paper
• Automated Classification and Analysis of Internet Malware

  2007cited by this paper
• Applying lazy learning algorithms to tackle concept drift in spam filtering

  2007cited by this paper
• ECUE: A Spam Filter that Uses Machine Learning to Track Concept Drift

  2006cited by this paper
• Learning to Detect and Classify Malicious Executables in the Wild

  2006cited by this paper
• A Comparison of Ensemble and Case-Base Maintenance Techniques for Handling Concept Drift in Spam Filtering

  2006cited by this paper
• Measuring Similarity of Large Software Systems Based on Source Code Correspondence

  2005cited by this paper
• Malware phylogeny generation using permutations of code

  2005cited by this paper
• Introduction to Data Mining, (First Edition)

  2005cited by this paper
• Discovering Knowledge in Data: An Introduction to Data Mining

  2005cited by this paper
• N-gram-based detection of new malicious code

  2004cited by this paper
• Learning with Drift Detection

  2004cited by this paper
• Tracking drifting concepts by minimizing disagreements

  2004cited by this paper
• The problem of concept drift: definitions and related work

  2004cited by this paper
• A case-based technique for tracking concept drift in spam filtering

  2004cited by this paper
• Learning drifting concepts: Example selection vs. example weighting

  2004cited by this paper
• The Elements of Statistical Learning

  2003cited by this paper
• Permutation tests for equality of distributions in high‐dimensional settings

  2002cited by this paper
• Data mining methods for detection of new malicious executables

  2001cited by this paper
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction

  2001cited by this paper
• Machine learning in automated text categorization

  2001cited by this paper
• The impact of changing populations on classifier performance

  1999influential reference
• Adaptive Information Filtering : Learning Drifting Concepts

  1998cited by this paper
• Computer virus-antivirus coevolution

  1997cited by this paper
• Laws of Software Evolution Revisited

  1996cited by this paper
• Learning in the Presence of Concept Drift and Hidden Contexts

  1996cited by this paper
• Learning Time-Varying Concepts

  1990cited by this paper
• Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests

  1979cited by this paper

• FARM: Few-shot Adaptive Malware Family Classification under Concept Drift

  2026cites this paper
• Detecting and Explaining Malware Family Evolution Using Rule-Based Drift Analysis

  2026cites this paper
• BIDO: An Out-Of-Distribution Resistant Image-based Malware Detector

  2025cites this paper
• Density Boosts Everything: A One-stop Strategy for Improving Performance, Robustness, and Sustainability of Malware Detectors

  2025cites this paper
• Cluster analysis and concept drift detection in malware

  2025cites this paper
• Addressing malware family concept drift with triplet autoencoder

  2025cites this paper
• Temporal Dimension of Concept Drift in Malware Behavior Classification

  2025cites this paper
• MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective Search

  2024cites this paper
• EAGLE: Evasion Attacks Guided by Local Explanations Against Android Malware Classification

  2024cites this paper
• ActDroid: An active learning framework for Android malware detection

  2024cites this paper
• Assessing Concept Drift in Malware: A Comprehensive Review and Analysis

  2024cites this paper
• Adaptive Real-Time Malware Detection for IoT Traffic Streams: A Comparative Study of Concept Drift Detection Techniques

  2024cites this paper
• Continuously Learning Bug Locations

  2024cites this paper
• Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection

  2024cites this paper
• When Adversarial Perturbations meet Concept Drift: An Exploratory Analysis on ML-NIDS

  2024cites this paper
• Position: The Explainability Paradox - Challenges for XAI in Malware Detection and Analysis

  2024cites this paper
• Temporal Analysis of Distribution Shifts in Malware Classification for Digital Forensics

  2023cites this paper
• Temporal Constraints in Online Dating Fraud Classification

  2023cites this paper
• FeSA: Feature selection architecture for ransomware detection under concept drift

  2022cites this paper
• Improving the prediction of continuous integration build failures using deep learning

  2022cites this paper
• Concept Drift and Malware Detection

  2022cites this paper
• Robust Machine Learning for Malware Detection over Time

  2022cites this paper
• A Global Survey of Android Dual-Use Applications Used in Intimate Partner Surveillance

  2022cites this paper
• The infinite race between steganography and steganalysis in images

  2022cites this paper
• Firenze: Model Evaluation Using Weak Signals

  2022cites this paper
• Learn to Adapt: Robust Drift Detection in Security Domain

  2022cites this paper
• Fast & Furious: Modelling Malware Detection as Evolving Data Streams

  2022cites this paper
• Improving the prediction of continuous integration build failures using deep learning

  2022cites this paper
• A Survey on Adversarial Attacks for Malware Analysis

  2021cites this paper
• An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model Using Concept Drift Detection and Sequential Deep Learning

  2021cites this paper
• Investigating Labelless Drift Adaptation for Malware Detection

  2021cites this paper
• Birds of a Feature: Intrafamily Clustering for Version Identification of Packed Malware

  2020cites this paper
• A Survey of Machine Learning Methods and Challenges for Windows Malware Classification

  2020cites this paper
• Machine Learning (In) Security: A Stream of Problems

  2020cites this paper
• Program analysis and machine learning techniques for mobile security

  2019cites this paper
• A scalable and extensible framework for android malware detection and family attribution

  2019cites this paper
• Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework

  2019cites this paper
• Adapting to Concept Drift in Malware Detection

  2019cites this paper
• A dynamic‐adversarial mining approach to the security of machine learning

  2018cites this paper
• Learning State Machines from data streams and an application in network-based threat detection

  2018cites this paper
• Malware Triage for Early Identification of Advanced Persistent Threat Activities

  2018cites this paper
• Phylogenetic Analysis for Ransomware Detection and Classification into Families

  2018cites this paper
• Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus

  2018cites this paper
• Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory

  2018cites this paper
• Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps

  2017cites this paper
• A multi-view context-aware approach to Android malware detection and malicious code localization

  2017cites this paper
• Context-Aware, Adaptive, and Scalable Android Malware Detection Through Online Learning

  2017cites this paper
• A Better Understanding of Machine Learning Malware Misclassifcation

  2017cites this paper
• Understanding and study of intrusion detection systems for various networks and domains

  2017cites this paper
• Lightweight behavioral malware detection for windows platforms

  2017cites this paper
• Dynamic Analysis of Executables to Detect and Characterize Malware

  2017cites this paper
• The Effect of Code Obfuscation on Authorship Attribution of Binary Computer Files

  2017cites this paper
• Dynamic adversarial mining - effectively applying machine learning in adversarial non-stationary environments.

  2017cites this paper
• Improved Detection for Advanced Polymorphic Malware

  2017cites this paper
• Adaptive and scalable Android malware detection through online learning

  2016cites this paper
• SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods

  2016cites this paper
• Prescience: Probabilistic Guidance on the Retraining Conundrum for Malware Detection

  2016cites this paper
• Be Sensitive to Your Errors: Chaining Neyman-Pearson Criteria for Automated Malware Classification

  2015cites this paper
• Malware Dynamic Behavior Classification : SVM-HMM applied to Malware API Sequencing

  2014cites this paper
• Approaches to adversarial drift

  2013cites this paper
• DEPSCOR FY 09) Obfuscation and Deobfuscation of Intent of Computer Programs

  2012cites this paper
• DroidHunter: A Robust Vision-Based Detection Against Hidden Android Malware

  year unknowncites this paper