C md C aliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research (2024)

Abstract: This research addresses command-line embedding in cybersecurity, a field obstructed by the lack of comprehensive datasets due to privacy and regulation concerns. We propose the first dataset of similar command lines, named CyPHER, for training and unbiased evaluation. The training set is generated using a set of large language models (LLMs) comprising 28,520 similar command-line pairs. Our testing dataset consists of 2,807 similar command-line pairs sourced from authentic command-line data.In addition, we propose a command-line embedding model named CmdCaliper, enabling the computation of semantic similarity with command lines. Performance evaluations demonstrate that the smallest version of CmdCaliper (30 million parameters) suppresses state-of-the-art (SOTA) sentence embedding models with ten times more parameters across various tasks (e.g., malicious command-line detection and similar command-line retrieval).Our study explores the feasibility of data generation using LLMs in the cybersecurity domain. Furthermore, we release our proposed command-line dataset, embedding models’ weights and all program codes to the public. This advancement paves the way for more effective command-line embedding for future researchers.

## introduction sentence embeddings, which map diverse sentences into a unified semantic feature space, are critical for various nlp applications such as classifier training, visualization (van der @xcite , and retrieval-augmented generation (rag) @xcite . in cybersecurity, command lines provide invaluable information for detecting malicious attacks by comparing them with known historical malicious command lines from a semantic perspective. however, the flexibility in command-line syntax and structure poses challenges for fully leveraging this information. for example, as shown in fig. 1 , one can still correlate the two command lines according to their outputs despite the different appearances. to achieve this, using a robust embedding model to calculate the semantic similarity of command lines is promising. however, the grammatical differences between command lines and natural language sentences hinder the direct application of sentence embedding models to command-line tasks. furthermore, one main challenge exacerbates the difficulty of research in command-line embedding: the scarcity of datasets specifically designed for command-line embedding tasks, both for training models and for fairly evaluating the performance of different methods. inspired by the successes of data synthesis by llms @xcite , the similar commandline pairs in our training set are automatically generated from a set of diverse command-line seeds initialized from multiple real-world sources by a total of six distinct llms trained on diverse datasets ( § 3.1). this facilitates a broader range of command-line generation. for the testing set of cypher, to prevent training data leakage, we directly employed a totally different data source instead of synthesizing command lines by llms, as done in the training set. ( § 3.2) to the end, our training set consists of 28,520 similar command-line pairs, totaling 55,909 unique command lines, and our testing set comprises 2,807 similar command-line pairs, totaling 5,576 unique command lines. our dataset analysis and human evaluation results ( § 5) demonstrate that our pipeline can generate highly diverse and highquality similar command-line pairs. based on our proposed dataset, cypher, we also developed the first embedding model specialized for command-line embeddings, called cmd-caliper. by encouraging semantically similar samples to come closer and simultaneously increasing the distance between semantically dissimilar samples in the embedding space, cmdcaliper can embed command lines into vectors from a semantic perspective. as demonstrated in fig. 1 , even when command lines differ in appearance, cmdcaliper can still position them closely in the embedding space based on their semantic meanings. our evaluation results ( §6) demonstrate that even the smallest version of cmdcaliper, with approximately 0.03 billion parameters, can surpass sota sentence embedding models with ten times more parameters (0.335 billion parameters) across various command-line specific tasks, such as malicious command-line detection, similar command-line retrieval, and command-line classification. our contribution is threefold. first, we propose the first dataset of similar command-line pairs named cypher, which allows for training and performance evaluation. through detailed validation of the dataset's effectiveness, we believe it is well-suited for further command-line research. secondly, we explore the potential of using llms to synthesize command-line data in the cybersecurity domain. our experiments demonstrate that llms can indeed generate high-quality and diverse data. lastly, we propose the first semantic command-line embedding model, cmdcaliper. our evaluations reveal that a command-line-specific embedding model significantly enhances performance across various downstream tasks compared to generic sentence embedding models. we open-source the entire dataset, model weights, and all program codes under bsd license at github repo foot_1 2 related work ## cmddataset: the first command-line similarity dataset despite the impressive performance of existing sentence embedding models @xcite @xcite , no embedding model has been designed specifically for command lines. we believe this is due to the unavailability of a large, diversified dataset with adequate annotations for effective training and unbiased evaluation. in this section, we primarily focus on introducing the first command-line similarity dataset, named cypher. this training set comprises 28,520 pairs of command lines automatically generated by a pool of llms. in contrast, the testing set contains 2,807 pairs of command lines collected from realworld attack scenarios. ## cmdcaliper: a semantic-aware command-line embedding model utilizing the proposed dataset cypher, a command-line embedding model can be trained with a contrastive objective, like sentence embedding methodologies, as seen in @xcite @xcite . given an embedding model, denoted as e, the procedure of embedding a command line c i into an embedding vector e i can be described as e @xmath0. in each training iteration, several similar pairs are randomly sampled from the training set to form a batch, which contains k similar command-line pairs {(x i , x + i )} k @xmath1, where (x i , x + i ) represents the i th similar command-line pair. within the batch, the similar command line x + i is regarded as a positive sample of sample x i , thereby encouraging the associated embedding vectors to be closer within the feature space. conversely, other samples {x + j , ∀@xmath2, 2, . . . , k} \ {i}} are treated as in-batch negatives @xcite @xcite , encouraging the embedding vectors to be farther. the infonce loss @xcite , denoted as l inf o , can be calculated as follows: ## conclusion in this work, we introduce cypher, a dataset of similar command-line pairs. the training set utilizes the impressive capabilities of an llm pool for automated generation, while the testing set consists of real-world malicious command lines for realistic evaluation. we also present cmdcaliper, the first dedicated command-line embedding model. our results show that cmdcaliper, specifically designed for command-line processing, outperforms existing sentence embedding methods in various commandline downstream tasks, such as command classification, malicious command line detection, and similar command-line retrieval. we open-source the dataset, model weights, and all program codes, hoping this study sheds light on future command-line embedding research.