Update README.md

README.md

@@ -5,7 +5,8 @@ library_name: peft
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This Model is built based on Mistral-7B which take attack scenario as input and it outputs techniques used by attacker
+
 
 
 
@@ -13,17 +14,17 @@ library_name: peft
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This Model is built based on Mistral-7B which take attack scenario as input and it outputs techniques used by attacker
 
 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Harish Santhanalakshmi Ganesan
+- **Funded by [optional]:** None
+- **Shared by [optional]:** None
+- **Model type:** LLM
+- **Language(s) (NLP):** English 
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2
 
 ### Model Sources [optional]
 
@@ -170,7 +171,16 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+@inproceedings{nguyen-srndic-neth-ttpm,
+    title = "Noise Contrastive Estimation-based Matching Framework for Low-resource Security Attack Pattern Recognition",
+    author = "Nguyen, Tu and Šrndić, Nedim and Neth, Alexander",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics",
+    month = mar,
+    year = "2024",
+    publisher = "Association for Computational Linguistics",
+    abstract = "Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain, described encyclopedically in textual knowledge bases. Identifying TTPs in cybersecurity writing, often called TTP mapping, is an important and challenging task. Conventional learning approaches often target the problem in the classical multi-class or multilabel classification setting. This setting hinders the learning ability of the model due to a large number of classes (i.e., TTPs), the inevitable skewness of the label distribution and the complex hierarchical structure of the label space. We formulate the problem in a different learning paradigm, where the assignment of a text to a TTP label is decided by the direct semantic similarity between the two, thus reducing the complexity of competing solely over the large labeling space. To that end, we propose a neural matching architecture with an effective sampling-based learn-to-compare mechanism, facilitating the learning process of the matching model despite constrained resources.",
+}
+
 
 **BibTeX:**