arXiv subjects

JongWon Kim

Publications and source records attributed to JongWon Kim.

4 recordsLinked to original sources

arXiv2026

AROMMA: Unifying Olfactory Embeddings for Single Molecules and Mixtures

Public olfaction datasets are small and fragmented across single molecules and mixtures, limiting learning of generalizable odor representations. Recent works either learn single-molecule embeddings or address mixtures via similarity or pairwise label prediction, leaving representations separate and unaligned. In this work, we propose AROMMA, a framework that learns a unified embedding space for single molecules and two-molecule mixtures. Each molecule is encoded by a chemical foundation model and the mixtures are composed by an attention-based aggregator, ensuring both permutation invariance and asymmetric molecular interactions. We further align odor descriptor sets using knowledge distillation and class-aware pseudo-labeling to enrich missing mixture annotations. AROMMA achieves state-of-the-art performance in both single-molecule and molecule-pair datasets, with up to 19.1% AUROC improvement, demonstrating a robust generalization in two domains.

cs.LG↗

arXiv2024

SmartX Intelligent Sec: A Security Framework Based on Machine Learning and eBPF/XDP

Information and Communication Technologies (ICT) infrastructures are becoming increasingly complex day by day, facing numerous challenges to support the latest networking paradigms. Security is undeniably a critical component for the effective functioning of these advanced ICT infrastructures. By considering the current network security challenges, we propose SmartX Intelligent Sec, an innovative intelligent security framework. SmartX Intelligent Sec leverages a combination of the lightweight extended Berkeley Packet Filter/eXpress Data Path (eBPF/XDP) for efficient network packet capturing and filtering malicious network traffic, and a Bidirectional Long Short-Term Memory (BiLSTM) classifier for network threat detection. Our real-time prototype demonstrates that SmartX Intelligent Sec offers comprehensive automation features, enabling continuous network packet capturing, effective network threat detection, and efficient filtering of malicious network traffic. This framework ensures enhanced security and operational efficiency for modern ICT infrastructures.

cs.CR↗

arXiv2024

Advancing Network Security: A Comprehensive Testbed and Dataset for Machine Learning-Based Intrusion Detection

This paper introduces a Testbed designed for generating network traffic, leveraging the capabilities of containers, Kubernetes, and eBPF/XDP technologies. Our Testbed serves as an advanced platform for producing network traffic for machine learning based network experiments. By utilizing this Testbed, we offer small malicious network traffic dataset publically that satisfy ground truth property completely.

cs.CR↗

arXiv2021

Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

The 6G vision is envisaged to enable agile network expansion and rapid deployment of new on-demand microservices (e.g., visibility services for data traffic management, mobile edge computing services) closer to the network's edge IoT devices. However, providing one of the critical features of network visibility services, i.e., data flow prediction in the network, is challenging at the edge devices within a dynamic cloud-native environment as the traffic flow characteristics are random and sporadic. To provide the AI-native services for the 6G vision, we propose a novel edge-native framework to provide an intelligent prognosis technique for data traffic management in this paper. The prognosis model uses long short-term memory (LSTM)-based encoder-decoder deep learning, which we train on real time-series multivariate data records collected from the edge $μ$-boxes of a selected testbed network. Our result accurately predicts the statistical characteristics of data traffic and verifies the trained model against the ground truth observations. Moreover, we validate our novel framework with two performance metrics for each feature of the multivariate data.

cs.IT↗