Malicious code detection is one of the most essential tasks in safeguarding against security breaches, data compromise, and related threats. While machine learning has emerged as a predominant method for pattern detection, the training process is intricate due to the severe scarcity of malicious code samples. Consequently, machine learning detectors often encounter malicious patterns in limited and isolated scenarios, hindering their ability to generalize effectively across diverse threat landscapes. In this paper, we introduce MalCoder, a novel method for synthesizing malicious code samples. MalCoder enlarges the quantity and diversity of malicious instances by transplanting a set of malicious prototypes into a vast pool of benign code, thereby crafting a diverse array of malicious instances tailored to various application scenarios. For each malware prototype, MalCoder treats it as an incomplete code fragment and crafts its preceding and subsequent contexts through right-to-left and left-to-right code completion respectively. By leveraging GPTs with various sampling strategies, we can instantiate a large number of code samples bearing the malware prototype. Subsequently, MalCoder masks the original prototypes within the transplanted samples and fine-tunes an LLM code generator to reconstruct the original prototype. This process enables the model to seamlessly transplant malicious code fragments into benign code. During inference, MalCoder can automatically insert malicious fragments into benign samples at random positions, transforming benign code into malicious code. We apply MalCoder to a large pool of benign code in CodeSearchNet and craft over 50,000 malicious samples stemming from 39 malicious prototypes. Both qualitative and quantitative analyses show that the generated samples maintain key characteristics of malicious code while blending seamlessly with benign code, which helps in creating realistic and varied training data. Additionally, by using the generated samples as augmented training data, we witness a remarkable surge in malicious code detection capabilities. Specifically, the F1-score experiences a significant increase compared to utilizing only the original prototype samples.

Data augmentation is a critical technique in deep learning.Traditional methods like Backtranslation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics.While large language models (LLMs) have enhanced text augmentation by their "knowledge emergence" capability, controlling the style and structure of these outputs remains challenging and requires meticulous prompt engineering.In this paper, we propose LMTransplant, a novel text augmentation paradigm leveraging LLMs.The core idea of LMTransplant is transplant-thenregenerate: incorporating seed text into a context expanded by LLM, and asking the LLM to regenerate a variant based on the expanded context.This strategy allows the model to create more diverse and creative content-level variants by fully leveraging the knowledge embedded in LLMs, while preserving the core attributes of the original text.We evaluate LMTransplant across various text-related tasks, demonstrating its superior performance over existing text augmentation methods.Moreover, LMTransplant demonstrates exceptional scalability as the size of augmented data grows.

Graph data analysis, particularly local triangle counting, plays a pivotal role in deciphering complex relationships within graph data. This method is invaluable across diverse fields such as social networks, transportation, and cybersecurity. However, this process often involves handling sensitive information, necessitating that the relationship between any two nodes is considered private. Differential privacy (DP) is a formal model to address privacy concerns and can be categorized into two types: the central DP (CDP) model, which achieves better result accuracy, and the local DP (LDP) model, which does not assume a trusted server. To bridge the gap between the two models, we propose Sectric, a server-aided crypto-assisted local triangle counting protocol, in this paper. It can achieve the same result accuracy with the same privacy budget as the CDP model without assuming a trusted server. Sectric also explores a new approach in crypto-assisted graph data analysis algorithms that represents a node's neighbors using a set instead of an adjacency vector, and successfully achieves higher efficiency compared to other crypto-assisted solutions. We also conduct theoretical and empirical evaluations to demonstrate that Sectric achieves the design principles.

The fundamental prerequisite for embodied agents to make intelligent decisions lies in autonomous cognition. Typically, agents optimize decision-making by leveraging extensive spatiotemporal information from episodic memory. Concurrently, they utilize long-term experience for task reasoning and foster conscious behavioral tendencies. However, due to the significant disparities in the heterogeneous modalities of these two cognitive abilities, existing literature falls short in designing effective coupling mechanisms, thus failing to endow robots with comprehensive intelligence. This article introduces a navigation framework, the hierarchical topology-semantic cognitive navigation (HTSCN), which seamlessly integrates both memory and reasoning abilities within a singular end-to-end system. Specifically, we represent memory and reasoning abilities with a topological map and a semantic relation graph, respectively, within a unified dual-layer graph structure. Additionally, we incorporate a neural-based cognition extraction process to capture cross-modal relationships between hierarchical graphs. HTSCN forges a link between two different cognitive modalities, thus further enhancing decision-making performance and the overall level of intelligence. Experimental results demonstrate that in comparison to existing cognitive structures, HTSCN significantly enhances the performance and path efficiency of image-goal navigation. Visualization and interpretability experiments further corroborate the promoting role of memory, reasoning, as well as their online learned relationships, on intelligent behavioral patterns. Furthermore, we deploy HTSCN in real-world scenarios to further verify its feasibility and adaptability.