:books: Literature

• Scientific Research

  • Classifying packed malware represented as control flow graphs using deep graph convolutional neural network
  • Classifying packed programs as malicious software detected
  • A comparative analysis of classifiers in the recognition of packed executables
  • A comparative analysis of software protection schemes
  • Comparing malware samples for unpacking: A feasibility study
  • Computational-intelligence techniques for malware generation
  • A consistently-executing graph-based approach for malware packer identification
  • A control flow graph-based signature for packer identification
  • Countering entropy measure attacks on packed software detection
  • Dealing with virtualization packers
  • Denial-of-service attacks on host-based generic unpackers
  • Deobfuscation of packed and virtualization-obfuscation protected binaries
  • Design and development of a new scanning core engine for malware detection
  • Design and performance evaluation of binary code packing for protecting embedded software against reverse engineering
  • Detecting packed executable file: Supervised or anomaly detection method?
  • Detecting packed executables based on raw binary data
  • Detecting packed executables using steganalysis
  • Detecting packed PE files: Executable file analysis for the Windows operating system
  • Detecting traditional packers, decisively
  • Detection of metamorphic malware packers using multilayered LSTM networks
  • Detection of packed executables using support vector machines
  • DexHunter: Toward extracting hidden code from packed Android applications
  • Disabling anti-debugging techniques for unpacking system in user-level debugger
  • Dynamic binary instrumentation for deobfuscation and unpacking
  • Dynamic classification of packing algorithms for inspecting executables using entropy analysis
  • Effective, efficient, and robust packing detection and classification
  • Efficient and automatic instrumentation for packed binaries
  • Efficient automatic original entry point detection
  • An efficient block-discriminant identification of packed malware
  • Efficient malware packer identification using support vector machines with spectrum kernel
  • Efficient SVM based packer identification with binary diffing measures
  • EMBER: An open dataset for training static PE malware machine learning models
  • An empirical evaluation of an unpacking method implemented with dynamic binary instrumentation
  • Encoded executable file detection technique via executable file header analysis
  • Entropy analysis to classify unknown packing algorithms for malware detection
  • Eureka: A framework for enabling static malware analysis
  • Experimental comparison of machine learning models in malware packing detection
  • A fast flowgraph based classification system for packed and polymorphic malware on the endhost
  • A fast randomness test that preserves local detail
  • Feature set reduction for the detection of packed executables
  • A fine-grained classification approach for the packed malicious code
  • A generic approach to automatic deobfuscation of executable code
  • Generic packing detection using several complexity analysis for accurate malware detection
  • Generic unpacker of executable files
  • Generic unpacking method based on detecting original entry point
  • Generic unpacking of self-modifying, aggressive, packed binary programs
  • Generic unpacking techniques
  • Generic unpacking using entropy analysis
  • Hashing-based encryption and anti-debugger support for packing multiple files into single executable
  • A heuristic approach for detection of obfuscated malware
  • A heuristics-based static analysis approach for detecting packed PE binaries
  • An implementation of a generic unpacking method on Bochs Emulator
  • Instructions-based detection of sophisticated obfuscation and packing
  • A learning model to detect maliciousness of portable executable using integrated feature set
  • Limits of static analysis for malware detection
  • Maitland: Analysis of packed and encrypted malware via paravirtualization extensions
  • Mal-EVE: Static detection model for evasive malware
  • 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
  • Malware analysis using multiple API sequence mining control flow graph
  • Malware analysis using visualized images and entropy graphs
  • Malware makeover: Breaking ML-based static analysis by modifying executable bytes
  • Malware obfuscation techniques: A brief survey
  • Malwise - An effective and efficient classification system for packed and polymorphic malware
  • McBoost: Boosting scalability in malware collection and analysis using statistical classification of executables
  • Memory behavior-based automatic malware unpacking in stealth debugging environment
  • Modern Linux malware exposed
  • MutantX-S: Scalable malware clustering based on static features
  • The new signature generation method based on an unpacking algorithm and procedure for a packer detection
  • Obfuscation-resilient executable payload extraction from packed malware
  • Obfuscation: The hidden malware
  • OmniUnpack: Fast, generic, and safe unpacking of malware
  • On deceiving malware classification with section injection
  • On the (Im)possibility of obfuscating programs
  • On the adoption of anomaly detection for packed executable filtering
  • OPEM: A static-dynamic approach for machine-learning-based malware detection
  • An original entry point detection method with candidate-sorting for more effective generic unpacking
  • Packed malware detection using entropy related analysis: A survey
  • Packed malware variants detection using deep belief networks
  • Packed PE file detection for malware forensics
  • Packer analysis report debugging and unpacking the NsPack 3.4 and 3.7 packer
  • Packer detection for multi-layer executables using entropy analysis
  • Packer identification method based on byte sequences
  • Packer identification using byte plot and Markov plot
  • Packer identification using hidden Markov model
  • Packer-complexity analysis in PANDA
  • Packing detection and classification relying on machine learning to stop malware propagation
  • Pandora's Bochs: Automatic unpacking of malware
  • Pattern recognition techniques for the classification of malware packers
  • PE file features in detection of packed executables
  • PE file header analysis-based packed PE file detection technique (PHAD)
  • PE-Miner: Mining structural information to detect malicious executables in realtime
  • PE-Probe: Leveraging packer detection and structural information to detect malicious portable executables
  • PEAL - Packed executable analysis
  • PEzoNG: Advanced packer for automated evasion on Windows
  • PolyUnpack: Automating the hidden-code extraction of unpack-executing malware
  • Preprocessing of binary executable files towards retargetable decompilation
  • Prevalence and impact of low-entropy packing schemes in the malware ecosystem
  • Qualitative and quantitative evaluation of software packers
  • RAMBO: Run-Time packer analysis with multiple branch observation
  • REFORM: A framework for malware packer analysis using information theory and statistical methods
  • RePEconstruct: Reconstructing binaries with self-modifying code and import address table destruction
  • Research and implementation of compression shell unpacking technology for PE file
  • Research and implementation of packing technology for PE files
  • Research of software information hiding algorithm based on packing technology
  • Revealing packed malware
  • Reverse engineering self-modifying code: Unpacker extraction
  • Robust static analysis of portable executable malware
  • Runtime packers testing experiences
  • SCORE: Source code optimization & reconstruction
  • Secure and advanced unpacking using computer emulation
  • Semi-supervised learning for packed executable detection
  • Semi-supervised learning for unknown malware detection
  • Sensitive system calls based packed malware variants detection using principal component initialized multilayers neural networks
  • SoK: Deep packer inspection: A longitudinal study of the complexity of run-time packers
  • Standards and policies on packer use
  • Static malware detection & subterfuge: Quantifying the robustness of machine learning and current anti-virus
  • A static, packer-agnostic filter to detect similar malware samples
  • Structural feature based anomaly detection for packed executable identification
  • The study of evasion of packed PE from static detection
  • A study of the packer problem and its solutions
  • Survey on malware evasion techniques: State of the art and challenges
  • Syntia: Synthesizing the semantics of obfuscated code
  • Things you may not know about Android (Un) packers: A systematic study based on whole-system emulation.
  • A token strengthened encryption packer to prevent reverse engineering PE files
  • Toward generic unpacking techniques for malware analysis with quantification of code revelation
  • Tutorial: An overview of malware detection and evasion techniques
  • Two techniques for detecting packed portable executable files
  • Unconditional self-modifying code elimination with dynamic compiler optimizations
  • Understanding Linux malware
  • A unpacking and reconstruction system-agunpacker
  • Unpacking framework for packed malicious executables
  • Unpacking techniques and tools in malware analysis
  • UnThemida: Commercial obfuscation technique analysis with a fully obfuscated program
  • Using entropy analysis to find encrypted and packed malware
  • VMRe: A reverse framework of virtual machine protection packed binaries
  • WaveAtlas: Surfing through the landscape of current malware packers
  • When malware is packin' heat; limits of machine learning classifiers based on static analysis features
  • x64Unpack: Hybrid emulation unpacker for 64-bit Windows Environments and detailed analysis results on VMProtect 3.4
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • 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
  • A fine-grained classification approach for the packed malicious code
  • Generic unpacking method based on detecting original entry point
  • 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
  • Packer identification using byte plot and Markov plot
  • 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
  • REFORM: A framework for malware packer analysis using information theory and statistical methods
  • 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
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • 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
  • A fine-grained classification approach for the packed malicious code
  • Generic unpacking method based on detecting original entry point
  • 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
  • Packer identification using byte plot and Markov plot
  • 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
  • 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
  • AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
  • 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
  • A fine-grained classification approach for the packed malicious code
  • Generic unpacking method based on detecting original entry point
  • 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