:books: Literature

• Scientific Research

  • Hunting for metamorphic engines
  • Longitudinal study of the prevalence of malware evasive techniques
  • 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
  • The MALICIA dataset: Identification and analysis of drive-by download operations
  • Measuring and defeating anti-instrumentation-equipped malware
  • Metamorphic malware detection based on support vector machine classification of malware sub-signatures
  • Metamorphic malware identification using engine-specific patterns based on co-opcode graphs
  • Mimicking anti-viruses with machine learning and entropy profiles - 05-21)
  • A novel framework for image-based malware detection with a deep neural network
  • On the (Im)possibility of obfuscating programs
  • On the (im)possibility of obfuscating programs (2)
  • On the feasibility of malware unpacking via hardware-assisted loop profiling
  • 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
  • Performance evaluation of filter-based feature selection techniques in classifying portable executable files
  • Program obfuscation by strong cryptography
  • RAMBO: Run-Time packer analysis with multiple branch observation
  • Replacement attacks against VM-protected applications
  • Research of software information hiding algorithm based on packing technology
  • Resurrecting anti-virtualization and anti-debugging: Unhooking your hooks
  • Semi-supervised learning for unknown malware detection
  • Singular value decomposition and metamorphic detection
  • SMASH: A malware detection method based on multi-feature ensemble learning
  • Software protection through anti-debugging
  • A static, packer-agnostic filter to detect similar malware samples
  • Structural analysis of binary executable headers for malware detection optimization
  • Structural entropy and metamorphic malware
  • A study of the packer problem and its solutions
  • A survey on adversarial attacks for malware analysis
  • A survey on automated dynamic malware-analysis techniques and tools
  • A survey on run-time packers and mitigation techniques
  • Symbolic deobfuscation: From virtualized code back to the original
  • A systematical and longitudinal study of evasive behaviors in windows malware
  • Unpacking malware in the real world: A step-by step guide
  • Unsupervised clustering machine learning on packed executable
  • VABox: A virtualization-based analysis framework of virtualization-obfuscated packed executables
  • Watermarking, tamper-proofing, and obfuscation - Tools for software protection
  • Wavelet decomposition of software entropy reveals symptoms of malicious code
  • Xunpack: Cross-Architecture unpacking for Linux IoT malware
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Analysis of machine learning approaches to packing detection
  • 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
  • Generic black-box end-to-end attack against state of the art API call based malware classifiers
  • Original entry point detection based on graph similarity
  • Packer classification based on association rule mining
  • 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
  • Sequential opcode embedding-based malware detection method
  • 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
  • Effective, efficient, and robust packing detection and classification
  • Efficient and automatic instrumentation for packed binaries
  • Evading packing detection: Breaking heuristic-based static detectors
  • Experimental toolkit for manipulating executable packing
  • Feature selection for packer classification based on association rule mining
  • 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
  • MaliCage: A packed malware family classification framework based on DNN and GAN
  • 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
  • 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
  • Adversarial learning on static detection techniques for executable packing
  • Adversarial tool for breaking static detection of executable packing
  • 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
  • Building a malware mutation tool
  • Building a smart and automated tool for packed malware detections using machine learning
  • 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
  • Experimental toolkit for studying executable packing - Analysis of the state-of-the-art packing detection techniques
  • 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
  • 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
  • Building high-quality datasets of packed executables - Enhancing static detection models via curated packed binary datasets
  • Denial-of-service attacks on host-based generic unpackers
  • Design and implementation of a modular executable packer - Experimenting with packing techniques and static detection
  • 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
  • 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