HeNet: A Deep Learning Approach on Intel® Processor Trace for Effective Exploit Detection

Keywords

Computer science, Malware, Executable, TRACE (psycholinguistics), Deep learning

References

• Control-Flow Bending: On the Effectiveness of Control-Flow IntegrityNicolas Carlini, Antonio Barresi, Mathias Payer — 2015
• Malware classification with recurrent networksRazvan Pascanu, Jack W. Stokes, H. Y. Y. Sanossian — 2015
• Very Deep Convolutional Networks for Large-Scale Image RecognitionKaren Simonyan, Andrew Zisserman — 2014
• Deep neural network based malware detection using two dimensional binary program featuresJoshua Saxe, Konstantin Berlin — 2015
• Large-scale malware classification using random projections and neural networksGeorge E. Dahl, Jack W. Stokes, Li Deng — 2013
• Malware images: visualization and automatic classificationLakshmanan Nataraj, S. Karthikeyan, Grégoire Jacob — 2011
• Refinement of a method for identifying probable archaeological sites from remotely sensed dataJames C. Tilton, Douglas C. Comer, Carey E. Priebe — 2012
• A comparative assessment of malware classification using binary texture analysis and dynamic analysisLakshmanan Nataraj, Vinod Yegneswaran, Phillip Porras — 2011
• Going deeper with convolutionsChristian Szegedy, Wei Liu, Yangqing Jia — 2014
• Gradient-based learning applied to document recognitionYann LeCun, Léon Bottou, Yoshua Bengio — 1998
• ROP is Still Dangerous: Breaking Modern DefensesNicholas Carlini, David Wagner — 2014
• A Survey on Transfer LearningSinno Jialin Pan, Qiang Yang — 2010
• Rethinking the Inception Architecture for Computer VisionChristian Szegedy, Vincent Vanhoucke, Sergey Ioffe — 2015
• Visualizing Data using t-SNELaurens van der Maaten, Geoffrey E. Hinton — 2008
• Inception-v4, Inception-ResNet and the Impact of Residual Connections on LearningChristian Szegedy, Sergey Ioffe, Vincent Vanhoucke — 2016
• Adversarial Perturbations Against Deep Neural Networks for Malware ClassificationKathrin Grosse, Nicolas Papernot, Praveen Manoharan — 2016
• Towards Evaluating the Robustness of Neural NetworksNicholas Carlini, David Wagner — 2016
• Universal Adversarial PerturbationsSeyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi — 2016
• LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection SystemsGyuwan Kim, Hayoon Yi, Jangho Lee — 2016
• Time series classification from scratch with deep neural networks: A strong baselineZhiguang Wang, Weizhong Yan, Tim Oates — 2016

Cited by

• When deep learning meets securityMajd Latah — 2018
• Program Anomaly Detection Against Data-Oriented AttacksCheng Long — 2018
• Deep Transfer Learning for Static Malware ClassificationLi Chen — 2018
• Towards resilient machine learning for ransomware detectionLi Chen, Chih-Yuan Yang, Anindya S. Paul — 2018
• Capturing the symptoms of malicious code in electronic documents by file's entropy signal combined with Machine learningLuping Liu, Xiaohai He, Liang Liu — 2019
• Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directionsLi Chen — 2019
• To believe or not to believe: Validating explanation fidelity for dynamic malware analysisLi Chen, Carter Yagemann, Evan Downing — 2019
• Real-Time Anomalous Branch Behavior Inference with a GPU-inspired Engine for Machine Learning ModelsHyunyoung Oh, Hayoon Yi, Hyeokjun Choe — 2019
• A Lightweight Assisted Vulnerability Discovery Method Using Deep Neural NetworksRunhao Li, Chao Feng, Xing Zhang — 2019
• Locating Vulnerability in Binaries Using Deep Neural NetworksRunhao Li, Chen Zhang, Chao Feng — 2019
• Symmetric-Key Cryptographic Routine Detection in Anti-Reverse Engineered Binaries Using Hardware TracingJuhyun Park, Yongsu Park — 2020
• Using deep learning to solve computer security challenges: a surveyYoon-Ho Choi, Peng Liu, Zitong Shang — 2019
• A Fuzzy Approach to User-level Intrusion DetectionWei Liu, Yu Mao, Linlin Ci — 2020
• Learning from Shader Program TracesYuting Yang, C. H. W. Barnes, Adam Finkelstein — 2021
• ARTINALI#: An Efficient Intrusion Detection Technique for Resource-Constrained Cyber-Physical SystemsMaryam Raiyat Aliabadi, Margo Seltzer, Mojtaba Vahidi Asl — 2021
• Real-time detection of hardware trojan attacks on General-Purpose Registers in a RISC-V processorShiwei Yuan, Lei Li, Ji Yang — 2021
• Identifying Symmetric-Key Algorithms Using CNN in Intel Processor TraceWooyeol Yang, Yongsu Park — 2021
• Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine LearningMuhammad Asam, Shaik Javeed Hussain, Mohammed Mohatram — 2021
• MDCD: A malware detection approach in cloud using deep learningDonghai Tian, Runze Zhao, Rui Ma — 2022
• The Role of Machine Learning in CybersecurityGiovanni Apruzzese, Pavel Laskov, Edgardo Montes de — 2022

Related papers

• A Dynamic Malware Detection Approach by Mining the Frequency of API CallsJin Rong Bai, Zhen An, Guo Zhong Zou — 2014
• A Dynamic Malware Detection Approach by Mining the Frequency of API CallsJin Rong Bai, Zhen An, Guo Zhong Zou — 2014
• Control Flow Analysis Based on Intermediate Representation of Executable CodeTian Jia — 2010
• Executable program structural representation tool based on flow analysisHaining Luo — 2007
• Extracting safe and precise control flow from binariesHenrik Theiling — 2000
• Decompiling High-level Control Structures with PropositionsJingbo Zhang, Rongcai Zhao, Jianmin Pang — 2009
• Generation executable test sequences based on Petri-net for combined control and data flow of communication protocolHua Li, Ming Ye — 1998
• Using Propositional Calculas to Accurately Structure Control-Flow from Binary ExecutablesJingbo Zhang, Fu Wen, Jianmin Pang — 2008