Rse Adversarial Defense
An implementation for "Defense of Word-level Adversarial Attacks via Random Substitution Encoding"
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Firecrack | 560 | 8 months ago | 39 | |||||||
| :fire: Firecrack pentest tools: Facebook hacking random attack, deface, admin finder, bing dorking: | ||||||||||
| Damnvulnerablecryptoapp | 81 | a year ago | 20 | mit | TypeScript | |||||
| An app with really insecure crypto. To be used to see/test/exploit weak cryptographic implementations as well as to learn a little bit more about crypto, without the need to dive deep into the math behind it | ||||||||||
| Manzana Attack | 45 | 5 years ago | 3 | gpl-3.0 | Rust | |||||
| A game about throwing apples at random people. Based on a true story. | ||||||||||
| Petya_green | 19 | 7 years ago | 2 | C++ | ||||||
| Application for random attack on Green Petya's key | ||||||||||
| Skiddiemonkeys | 10 | 9 years ago | gpl-3.0 | Python | ||||||
| Rse Adversarial Defense | 7 | 3 years ago | Python | |||||||
| An implementation for "Defense of Word-level Adversarial Attacks via Random Substitution Encoding" | ||||||||||
| Xkcdpass | 6 | 7 years ago | mit | Ruby | ||||||
| A passphrase generator | ||||||||||
| Attack And Anomaly Detection In Iot Sensors In Iot Sites Using Machine Learning Approaches | 6 | 5 years ago | mit | Jupyter Notebook | ||||||
| Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better. | ||||||||||
| Unbound Bloomfilter | 5 | 8 years ago | 2 | bsd-3-clause | Groff | |||||
| Random subdomain attack mitigation using bloom filter for Unbound | ||||||||||
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