awesome-executable-packing
A curated list of awesome resources related to executable packing
https://github.com/packing-box/awesome-executable-packing
Last synced: 10 days ago
JSON representation
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:books: Literature
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Scientific Research
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Maitland: Analysis of packed and encrypted malware via paravirtualization extensions
- 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
- 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
- An experimental study on identifying obfuscation techniques in packer
- 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
- 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
- Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art
- Adversarially robust assembly language model for packed executables detection
- Anti-emulation trends in modern packers: A survey on the evolution of anti-emulation techniques in UPA packers
- API-MalDetect: Automated malware detection framework for windows based on API calls and deep learning techniques
- AppSpear: Bytecode decrypting and DEX reassembling for packed Android malware
- Assessing static and dynamic features for packing detection
- Assessing the impact of packing on machine learning-based malware detection and classification systems
- Auditing static machine learning anti-Malware tools against metamorphic attacks
- Benchmark for filter methods for feature selection in high-dimensional classification data
- Beyond the sandbox: Leveraging symbolic execution for evasive malware classification
- BitBlaze: A new approach to computer security via binary analysis
- BODMAS: An open dataset for learning based temporal analysis of PE malware
- Building high-quality datasets of packed executables - Enhancing static detection models via curated packed binary datasets
- Bypassing heaven’s gate technique using black-box testing
- BYTEWEIGHT: Learning to recognize functions in binary code
- Classification of malware by using structural entropy on convolutional neural networks
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