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김휘강 교수

Huy Kang Kim

고려대학교 정보보호대학원 · 컴퓨터과학

연구실 소개

김휘강 교수의 연구실은 사이버 보안 분야에서 핵심적인 과제인 악성코드 탐지 및 공격 대응 기술을 연구하고 있습니다. 특히 모바일 악성코드의 생성자 특성 분석, 동적 분석 기반의 효율적 행동 분류, 그리고 산업 제어 시스템의 보안 취약점 진단에 초점을 맞추고 있습니다. 최근에는 스마트 기기와 IOT 환경에서 발생하는 보안 위협에 대응하기 위한 하이브리드 분석 기법과 네트워크 과학 기반 사용자 행동 분석도 함께 진행하고 있습니다. 기술적 혁신과 실질적 보안 적용을 융합한 연구를 지속하고 있습니다.

악성코드 탐지모바일 보안동적 분석산업 제어 시스템행동 기반 분류

연구 현황

논문 수
334
총 인용 수
6,093
최근 5년 논문
81
주요 분야
컴퓨터과학

연구 성과 추이

표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수
81총합
2022
2023
2024
2025
2026
5개년 연도별 피인용 수
337총합
20222023202420252026

주요 논문

15
1
논문|인용수 551·2019
In-vehicle network intrusion detection using deep convolutional neural network
Hyun Min Song, Jiyoung Woo, Huy Kang Kim
SJR Q1Vehicular Communications
Computer Networks and CommunicationsComputer Science
2
논문|인용수 378·2021
Cybersecurity for autonomous vehicles: Review of attacks and defense
Kyounggon Kim, Jun Seok Kim, Seong Hoon Jeong, Jo-Hee Park, Huy Kang Kim
SJR Q1Computers & Security
Electrical and Electronic EngineeringEngineering
3
논문|인용수 262·2015
A Novel Approach to Detect Malware Based on API Call Sequence Analysis
Youngjoon Ki, Eun‐jin Kim, Huy Kang Kim
SJR Q2International Journal of Distributed Sensor NetworksOA

In the era of ubiquitous sensors and smart devices, detecting malware is becoming an endless battle between ever-evolving malware and antivirus programs that need to process ever-increasing security related data. For malware detection, various approaches have been proposed. Among them, dynamic analysis is known to be effective in terms of providing behavioral information. As malware authors increasingly use obfuscation techniques, it becomes more important to monitor how malware behaves for its

Signal ProcessingComputer Science
4
논문|인용수 175·2018
Anomaly intrusion detection method for vehicular networks based on survival analysis
Mee Lan Han, Byung Il Kwak, Huy Kang Kim
SJR Q1Vehicular Communications
Electrical and Electronic EngineeringEngineering
5
논문|인용수 152·2015
Detecting and Classifying Android Malware Using Static Analysis along with Creator Information
Hyunjae Kang, Jae-wook Jang, Aziz Mohaisen, Huy Kang Kim
SJR Q2International Journal of Distributed Sensor NetworksOA

Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional underground actors; however previous studies overlooked such information as a feature in detecting and classifying malware and in attributing malware to creators. Guided by this insight, we propose a method to improve the performance of Android malware detection by incorporating the creator'

Signal ProcessingComputer Science
6
논문|인용수 94·2012
Online game bot detection based on party-play log analysis
Ah Reum Kang, Jiyoung Woo, Juyong Park, Huy Kang Kim
SJR Q1Computers & Mathematics with Applications
Signal ProcessingComputer Science
7
논문|인용수 74·2021
Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks
Seong Hoon Jeong, Boo-Sun Jeon, Boheung Chung, Huy Kang Kim
SJR Q1Vehicular Communications
Electrical and Electronic EngineeringEngineering
8
논문|인용수 61·2021
AutoVAS: An automated vulnerability analysis system with a deep learning approach
Sanghoon Jeon, Huy Kang Kim
SJR Q1Computers & Security
Information SystemsComputer Science
9
논문|인용수 57·2021
Unsupervised Fault Detection on Unmanned Aerial Vehicles: Encoding and Thresholding Approach
Kyung Ho Park, Eunji Park, Huy Kang Kim
SJR Q1SensorsOA

Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle's control component. Prior studies proposed various fault detection approaches leveraging heuristics-based rules and supervised learning-based models, but there were several drawbacks. The rule-based approaches required an engineer to update the rules on every type of fault, and the supervised learning-based approaches necessitated the acquisition of a

Artificial IntelligenceComputer Science
10
논문|인용수 56·2020
Cosine similarity based anomaly detection methodology for the CAN bus
Byung Il Kwak, Mee Lan Han, Huy Kang Kim
SJR Q1Expert Systems with Applications
Electrical and Electronic EngineeringEngineering
11
논문|인용수 54·2016
Andro-Dumpsys: Anti-malware system based on the similarity of malware creator and malware centric information
Jae-wook Jang, Hyunjae Kang, Jiyoung Woo, Aziz Mohaisen, Huy Kang Kim
SJR Q1Computers & Security
Signal ProcessingComputer Science
12
논문|인용수 48·2015
Andro-AutoPsy: Anti-malware system based on similarity matching of malware and malware creator-centric information
Jae-wook Jang, Hyunjae Kang, Jiyoung Woo, Aziz Mohaisen, Huy Kang Kim
Digital Investigation
Signal ProcessingComputer Science
13
논문|인용수 47·2009
DSS for computer security incident response applying CBR and collaborative response
Huy Kang Kim, Kwang Hyuk Im, Sang Chan Park
SJR Q1Expert Systems with Applications
Computer Networks and CommunicationsComputer Science
14
논문|인용수 41·2012
Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game
Seokshin Son, Ah Reum Kang, Hyun-chul Kim, Hyun-chul Kim, Taekyoung Kwon, Juyong Park, Huy Kang Kim, Huy Kang Kim
SJR Q1PLoS ONEOA

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMO

Statistical and Nonlinear PhysicsPhysics and Astronomy
15
논문|인용수 40·2022
STRIDE‐based threat modeling and DREAD evaluation for the distributed control system in the oil refinery
Kyoung Ho Kim, Kyounggon Kim, Huy Kang Kim
SJR Q2ETRI JournalOA

Industrial control systems (ICSs) used to be operated in closed networks, that is, separated physically from the Internet and corporate networks, and independent protocols were used for each manufacturer. Thus, their operation was relatively safe from cyberattacks. However, with advances in recent technologies, such as big data and internet of things, companies have been trying to use data generated from the ICS environment to improve production yield and minimize process downtime. Thus, ICSs ar

Information SystemsComputer Science

대표 연구 분야

Signal ProcessingComputer Networks and CommunicationsInformation SystemsArtificial IntelligenceElectrical and Electronic EngineeringSociology and Political Science

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