ROPNN: Detection of ROP Payloads Using Deep Neural Networks
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Summary
ROPNN, which innovatively combines address space layout guided disassembly and deep neural networks, to detect ROP payloads in HTTP requests, PDF files, and images, etc, and successfully detects all of the 80 exploits.
- Type
- preprint
- Published
- 2018-07-29
- Cited by
- 12
- References
- 74
- Access
- Open access
- OpenAlex
- https://openalex.org/W2883008388
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:51871168
Keywords
Exploit, Computer science, Gadget, Overhead (engineering), Code (set theory)
References
- Transparent ROP Exploit Mitigation Using Indirect Branch Tracing
- How to Generate a Good Word Embedding
- Control-Flow Bending: On the Effectiveness of Control-Flow Integrity
- Size Does Matter: Why Using Gadget-Chain Length to Prevent Code-Reuse Attacks is Hard
- Describing Videos by Exploiting Temporal Structure
- Readactor: Practical Code Randomization Resilient to Memory Disclosure
- Stitching the Gadgets: On the Ineffectiveness of Coarse-Grained Control-Flow Integrity Protection
- Oxymoron: Making Fine-Grained Memory Randomization Practical by Allowing Code Sharing
- Enforcing Forward-Edge Control-Flow Integrity in GCC & LLVM
- Control Flow Integrity for COTS Binaries
- Convolutional Neural Networks for Sentence Classification
- Just-In-Time Code Reuse: On the Effectiveness of Fine-Grained Address Space Layout Randomization
- ROPecker: A Generic and Practical Approach For Defending Against ROP Attacks
- Jump-oriented programming: a new class of code-reuse attack
- Marlin: Mitigating Code Reuse Attacks Using Code Randomization
- On the momentum term in gradient descent learning algorithms
- Breaking the memory secrecy assumption
- Return-oriented programming without returns
- You Can Run but You Can't Read: Preventing Disclosure Exploits in Executable Code
- Out of Control: Overcoming Control-Flow Integrity
Cited by
- Static Analysis of ROP Code
- DeepCheck: A Non-intrusive Control-flow Integrity Checking based on Deep Learning
- Using deep learning to solve computer security challenges: a survey
- A survey on the application of deep learning for code injection detection
- A traffic detection method of ROP attack based on image representation
- Horus: An Effective and Reliable Framework for Code-Reuse Exploits Detection in Data Stream
- Use of Ensemble Learning to Detect Buffer Overflow Exploitation
- Program Characterization for Software Exploitation Detection
- Memory Integrity Techniques for Memory-Unsafe Languages: A Survey
- Classification of return-oriented programming gadgets: a machine learning approach
- A technique to locate control-flow hijacking point for vulnerability exploitation based on jump-oriented features
- Horus: An Effective and Reliable Framework for Code-Reuse Exploits Detection in Data Stream
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