Large Language Models (LLMs) have been used in cybersecurity in many ways, including their recent use as intelligent agent systems for autonomous security analysis. Capture the Flag (CTF) challenges serve as benchmarks for assessing the automated task-planning abilities of LLM agents across various cybersecurity skill sets. Early attempts to apply LLMs for solving CTF challenges relied on single-agent systems, where feedback was restricted to a single reasoning-action loop. This approach proved inadequate for handling complex CTF tasks. Drawing inspiration from real-world CTF competitions, where teams of experts collaborate, we introduce the D-CIPHER multi-agent LLM framework for collaborative CTF challenge solving. D-CIPHER integrates agents with distinct roles, enabling dynamic feedback loops to enhance reasoning on CTF challenges. It introduces the Planner-Executor agent system, consisting of a Planner agent for overall problem-solving along with multiple heterogeneous Executor agents for individual tasks, facilitating efficient allocation of responsibilities among the LLMs. Additionally, D-CIPHER incorporates an Auto-prompter agent, which improves problem-solving by exploring the challenge environment and generating a highly relevant initial prompt. We evaluate D-CIPHER on CTF benchmarks using multiple LLM models and conduct comprehensive studies to highlight the impact of our enhancements. Our results demonstrate that the multi-agent D-CIPHER system achieves a significant improvement in challenges solved, setting a state-of-the-art performance on three benchmarks: 22.0% on NYU CTF Bench, 22.5% on Cybench, and 44.0% on HackTheBox. D-CIPHER is available at https://github.com/NYU-LLM-CTF/nyuctf_agents as the nyuctf_multiagent package.

The rise in cybercrime and the complexity of multilingual and code-mixed complaints present significant challenges for law enforcement and cybersecurity agencies. These organizations need automated, scalable methods to identify crime types, enabling efficient processing and prioritization of large complaint volumes. Manual triaging is inefficient, and traditional machine learning methods fail to capture the semantic and contextual nuances of textual cybercrime complaints. Moreover, the lack of publicly available datasets and privacy concerns hinder the research to present robust solutions. To address these challenges, we propose a framework for automated cybercrime complaint classification. The framework leverages Hinglish-adapted transformers, such as HingBERT and HingRoBERTa, to handle code-mixed inputs effectively. We employ the real-world dataset provided by Indian Cybercrime Coordination Centre (I4C) during CyberGuard AI Hackathon 2024. We employ GenAI open source model-based data augmentation method to address class imbalance. We also employ privacy-aware preprocessing to ensure compliance with ethical standards while maintaining data integrity. Our solution achieves significant performance improvements, with HingRoBERTa attaining an accuracy of 74.41% and an F1-score of 71.49%. We also develop ready-to-use tool by integrating Django REST backend with a modern frontend. The developed tool is scalable and ready for real-world deployment in platforms like the National Cyber Crime Reporting Portal. This work bridges critical gaps in cybercrime complaint management, offering a scalable, privacy-conscious, and adaptable solution for modern cybersecurity challenges.

The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for short-term forecasting $\{1-24\}$ hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the state-of-the-art techniques.