Cyber threats are becoming so complicated and rampant in today's highly connected digital world that there is a tremendous need for intelligent, adaptive IDS. Detecting and responding against network intrusions in real-time is the aim of the present work, which proposes a real-time Network Intrusion Detection and Automated Response Framework developed by Multi-Agent Deep Q-Network (MADQN). The proposed design uses the UNSW-NB15 network traffic set and discriminates and classifies attacks by using supervised machine learning models including Logistic Regression model, Decision Tree model, Random Forest model and the Neural Network classifiers. To augment the automation of the response, a reinforcement learning layer of three cooperative agents: Detection Agent, Blocking Agent and Isolation Agent is introduced based on Deep Q-Networks (DQNs). They learn the optimal mitigations actions they should take on a network state represented in 221 dimensions, and through the experience replay and epsilon-greedy exploration with the reinforcement learning, the agents learn. The results of the experiments demonstrate excellent response detection and 99.38% classification accuracy for the Random Forest model; for the intelligent response selection, the MADQN framework has a good neutralization rate of 93.94%. The architecture designed in the Flask can be fully integrated to the user authentication, attack prediction, attack response management and historical monitoring architecture. Overall, the proposed framework presents a powerful cybersecurity solution that integrates ML and MARL, enabling more secure and resilient networks.
S. Banu, D. Manogna, Farooq Sunar Mahammad et al.· 2026 International Conferenc...· 0 citations
This project proposes a privacy-preserving framework for location-based services, incorporating federated analytics, secure computation and mobility intelligence for customized next-location prediction and recommendation of points-of-interest. The proposed system is based on the Foursquare NYC check-in data set of user-wise location traces, venue categories, geographic coordinates and timestamps. The whole dataset is split by user identity to simulate federated clients and all user check-in logs are not stored at a central server. Clients train an LSTM-based sequence model to learn temporal mobility behavior from their check-in patterns locally. Gaussian noise is applied using a differential privacy layer prior to the sharing of model updates, to minimize the likelihood of revealing information about the location of the user. The noisy local updates are then aggregated at the Flask server using the FedAvg algorithm, which leads to an aggregated mobility model without noise. In addition to next-location prediction, the system uses matrix factorization to produce top 5 recommendations and DBSCAN clustering to detect spatial hotspots based on spatial patterns. The frontend is created with HTML, CSS, JavaScript and Leaflet.js and presents recommended venues, predicted latitude/longitude, hotspot heatmap and an epsilon-based privacy budget meter. The novelty of the work is that the location prediction, POI recommendation, hotspot detection, federated aggregation, and differential privacy are all integrated into a unified location-based service architecture. This not only facilitates beneficial mobility analytics but also minimises the central exposure of sensitive GPS traces and enhances the privacy awareness of location-based recommendation systems.
Mr. M. Amareswara Kumar, M. Bhavana, Pg Student et al.· 2026 6th International Confe...· 0 citations
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