:books: Literature

• Scientific Research

  • BitBlaze: A new approach to computer security via binary analysis
  • Denial-of-service attacks on host-based generic unpackers
  • Detecting traditional packers, decisively
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Efficient and automatic instrumentation for packed binaries
  • Entropy analysis to classify unknown packing algorithms for malware detection
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • A fine-grained classification approach for the packed malicious code
  • Anti-emulation trends in modern packers: A survey on the evolution of anti-emulation techniques in UPA packers
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • Assessing static and dynamic features for packing detection
  • BitBlaze: A new approach to computer security via binary analysis
  • Denial-of-service attacks on host-based generic unpackers
  • Detecting traditional packers, decisively
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Efficient and automatic instrumentation for packed binaries
  • Entropy analysis to classify unknown packing algorithms for malware detection
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • A fine-grained classification approach for the packed malicious code
  • Anti-emulation trends in modern packers: A survey on the evolution of anti-emulation techniques in UPA packers
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • Assessing static and dynamic features for packing detection
  • BitBlaze: A new approach to computer security via binary analysis
  • Denial-of-service attacks on host-based generic unpackers
  • Detecting traditional packers, decisively
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Efficient and automatic instrumentation for packed binaries
  • Entropy analysis to classify unknown packing algorithms for malware detection
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • A fine-grained classification approach for the packed malicious code
  • Generating adversarial malware examples for black-box attacks based on GAN
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Generic unpacking method based on detecting original entry point
  • Hashing-based encryption and anti-debugger support for packing multiple files into single executable
  • A heuristics-based static analysis approach for detecting packed PE binaries
  • Highlighting the impact of packed executable alterations with unsupervised learning
  • Identifying malware packers through multilayer feature engineering in static analysis
  • Improving malware detection using multi-view ensemble learning
  • Incremental clustering of malware packers using features based on transformed CFG
  • A machine-learning-based framework for supporting malware detection and analysis
  • Mal-XT: Higher accuracy hidden-code extraction of packed binary executable
  • Mal-xtract: Hidden code extraction using memory analysis
  • Malware detection through opcode sequence analysis using machine learning
  • Malware family classification method based on static feature extraction
  • Malware images: Visualization and automatic classification
  • Malware makeover: Breaking ML-based static analysis by modifying executable bytes
  • Malware obfuscation through evolutionary packers
  • Metadata recovery from obfuscated programs using machine learning
  • MLxPack: Investigating the effects of packers on ML-based malware detection systems using static and dynamic traits
  • MSG: Missing-sequence generator for metamorphic malware detection
  • Novel feature extraction, selection and fusion for effective malware family classification
  • Obfuscation: Where are we in anti-DSE protections? (a first attempt)
  • On deceiving malware classification with section injection
  • On evaluating adversarial robustness
  • On the (Im)possibility of obfuscating programs
  • Opcode sequences as representation of executables for data-mining-based unknown malware detection
  • Opcodes as predictor for malware
  • OPEM: A static-dynamic approach for machine-learning-based malware detection
  • Original entry point detection based on graph similarity
  • Packed code detection using shannon entropy and homomorphic encrypted executables
  • Packer classifier based on PE header information
  • Packer identification based on metadata signature
  • Packer identification using byte plot and Markov plot
  • Packer identification using hidden Markov model
  • PackGenome: Automatically generating robust YARA rules for accurate malware packer detection
  • PackHero: A scalable graph-based approach for efficient packer identification
  • Pattern recognition techniques for the classification of malware packers
  • PE-Miner: Mining structural information to detect malicious executables in realtime
  • PEAL - Packed executable analysis
  • PEzoNG: Advanced packer for automated evasion on Windows
  • Pitfalls in machine learning for computer security
  • PolyPack: An automated online packing service for optimal antivirus evasion
  • Potent and stealthy control flow obfuscation by stack based self-modifying code
  • Preprocessing of binary executable files towards retargetable decompilation
  • RAMBO: Run-Time packer analysis with multiple branch observation
  • REFORM: A framework for malware packer analysis using information theory and statistical methods
  • Renovo: A hidden code extractor for packed executables
  • Research of software information hiding algorithm based on packing technology
  • SE-PAC: A self-evolving packer classifier against rapid packers evolution
  • Semi-supervised learning for unknown malware detection
  • Sequential opcode embedding-based malware detection method
  • SoK: Automatic deobfuscation of virtualization-protected applications
  • Source-free binary mutation for offense and defense
  • SPADE: Signature based packer detection
  • Static analysis method on portable executable files for REMNUX based malware identification
  • Static analysis of executables to detect malicious patterns
  • Static features exploration for executable packing with unsupervised learning
  • A static, packer-agnostic filter to detect similar malware samples
  • A study of the packer problem and its solutions
  • A survey on malware analysis techniques: Static, dynamic, hybrid and memory analysis
  • A survey on run-time packers and mitigation techniques
  • Symbolic deobfuscation: From virtualized code back to the original
  • Technical report on the cleverhans v2.1.0 adversarial examples library
  • Thwarting real-time dynamic unpacking
  • Towards paving the way for large-scale Windows malware analysis: Generic binary unpacking with orders-of-magnitude performance boost
  • Transcending transcend: Revisiting malware classification in the presence of concept drift
  • Unknown malcode detection using OPCODE representation
  • Unpacking malware in the real world: A step-by step guide
  • Unpacking virtualization obfuscators
  • VABox: A virtualization-based analysis framework of virtualization-obfuscated packed executables
  • VMAttack: Deobfuscating virtualization-based packed binaries
  • VMHunt: A verifiable approach to partially-virtualized binary code simplification
  • WYSINWYX: What you see is not what you execute
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Anti-emulation trends in modern packers: A survey on the evolution of anti-emulation techniques in UPA packers
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • Assessing static and dynamic features for packing detection
  • BitBlaze: A new approach to computer security via binary analysis
  • Denial-of-service attacks on host-based generic unpackers
  • Detecting traditional packers, decisively
  • Detecting unknown malicious code by applying classification techniques on opcode patterns
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Efficient and automatic instrumentation for packed binaries
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • A fine-grained classification approach for the packed malicious code
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Generic unpacking method based on detecting original entry point
  • Highlighting the impact of packed executable alterations with unsupervised learning
  • A machine-learning-based framework for supporting malware detection and analysis
  • Mal-XT: Higher accuracy hidden-code extraction of packed binary executable
  • Mal-xtract: Hidden code extraction using memory analysis
  • On the (Im)possibility of obfuscating programs
  • OPEM: A static-dynamic approach for machine-learning-based malware detection
  • Original entry point detection based on graph similarity
  • Packer identification using hidden Markov model
  • Pattern recognition techniques for the classification of malware packers
  • PE-Miner: Mining structural information to detect malicious executables in realtime
  • PEAL - Packed executable analysis
  • RAMBO: Run-Time packer analysis with multiple branch observation
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Research of software information hiding algorithm based on packing technology
  • Semi-supervised learning for unknown malware detection
  • A static, packer-agnostic filter to detect similar malware samples
  • A study of the packer problem and its solutions
  • A survey on run-time packers and mitigation techniques
  • Symbolic deobfuscation: From virtualized code back to the original
  • Unpacking malware in the real world: A step-by step guide
  • VABox: A virtualization-based analysis framework of virtualization-obfuscated packed executables
  • Anti-emulation trends in modern packers: A survey on the evolution of anti-emulation techniques in UPA packers
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • Assessing static and dynamic features for packing detection
  • BitBlaze: A new approach to computer security via binary analysis
  • Classification of packed executables for accurate computer virus detection
  • Control flow-based opcode behavior analysis for malware detection
  • Denial-of-service attacks on host-based generic unpackers
  • Detecting traditional packers, decisively
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Efficient and automatic instrumentation for packed binaries
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • A fine-grained classification approach for the packed malicious code
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Generic unpacking method based on detecting original entry point
  • Highlighting the impact of packed executable alterations with unsupervised learning
  • A machine-learning-based framework for supporting malware detection and analysis
  • Mal-flux: Rendering hidden code of packed binary executable
  • Mal-XT: Higher accuracy hidden-code extraction of packed binary executable
  • Mal-xtract: Hidden code extraction using memory analysis
  • MSG: Missing-sequence generator for metamorphic malware detection
  • On the (Im)possibility of obfuscating programs
  • OPEM: A static-dynamic approach for machine-learning-based malware detection
  • Original entry point detection based on graph similarity
  • Packer identification using hidden Markov model
  • Pattern recognition techniques for the classification of malware packers
  • PE-Miner: Mining structural information to detect malicious executables in realtime
  • PEAL - Packed executable analysis
  • RAMBO: Run-Time packer analysis with multiple branch observation
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Research of software information hiding algorithm based on packing technology
  • Semi-supervised learning for unknown malware detection
  • A static, packer-agnostic filter to detect similar malware samples
  • A study of the packer problem and its solutions
  • A survey on run-time packers and mitigation techniques
  • Symbolic deobfuscation: From virtualized code back to the original
  • Unpacking malware in the real world: A step-by step guide
  • VABox: A virtualization-based analysis framework of virtualization-obfuscated packed executables
  • Anti-emulation trends in modern packers: A survey on the evolution of anti-emulation techniques in UPA packers
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • Assessing static and dynamic features for packing detection
  • BitBlaze: A new approach to computer security via binary analysis
  • Denial-of-service attacks on host-based generic unpackers
  • Detecting traditional packers, decisively
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Efficient and automatic instrumentation for packed binaries
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • A fine-grained classification approach for the packed malicious code
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Generic unpacking method based on detecting original entry point
  • Highlighting the impact of packed executable alterations with unsupervised learning
  • A machine-learning-based framework for supporting malware detection and analysis
  • Mal-XT: Higher accuracy hidden-code extraction of packed binary executable
  • Mal-xtract: Hidden code extraction using memory analysis
  • On the (Im)possibility of obfuscating programs
  • OPEM: A static-dynamic approach for machine-learning-based malware detection
  • Original entry point detection based on graph similarity