World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
8953
World Ranking
5921
National Ranking
2672

Overview

Andrew H. Sung is affiliated with the University of Southern Mississippi in the United States. Their research spans several key areas within computer science and medicine, with a focus on advanced techniques in malware detection, network security, and artificial intelligence applications.

Their recent publications include the following:

  • Deepfake Detection: A Systematic Literature Review, 2022, IEEE Access
  • Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection, 2020, Vietnam Journal of Computer Science
  • Enhancing Machine Learning Performance with Continuous In-Session Ground Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity Prediction, 2023, arXiv (Cornell University)
  • Machine Unlearning using a Multi-GAN based Model, 2024, arXiv (Cornell University)
  • Machine unlearning using a Multi-GaN based model, 2024, AIP conference proceedings

The scientist's frequent collaborators include Md. Shohel Rana, Amartya Hatua, Trung T. Nguyen, Mohammad Nur Nobi, and Beddhu Murali.

The main fields of study for Andrew H. Sung are computer science and medicine. Within computer science, they have contributed notably to these subfields:

  • Artificial Intelligence
  • Signal Processing
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

The topics covered in their research encompass a range of technological domains and methodologies, including:

  • Advanced Malware Detection Techniques
  • Network Security and Intrusion Detection
  • Neural Networks and Applications
  • Digital Media Forensic Detection
  • Generative Adversarial Networks and Image Synthesis
  • Anomaly Detection Techniques and Applications
  • Software Testing and Debugging Techniques

Their work has appeared in multiple venues, indicating a focus on both theoretical and applied research. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Access
  • Vietnam Journal of Computer Science
  • AIP conference proceedings
  • Computers, Materials & Continua (Print)

Best Publications

  • Intrusion detection using neural networks and support vector machines

    S. Mukkamala;G. Janoski;A. Sung

  • Identifying important features for intrusion detection using support vector machines and neural networks

    A.H. Sung;S. Mukkamala

  • Intrusion detection using an ensemble of intelligent paradigms

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham

  • Static analyzer of vicious executables (SAVE)

    A.H. Sung;J. Xu;P. Chavez;S. Mukkamala

  • Detection of Phishing Attacks: A Machine Learning Approach

    Ram B. Basnet;Srinivas Mukkamala;Andrew H. Sung

  • Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligence Techniques.

    Srinivas Mukkamala;Andrew H. Sung

  • Ranking importance of input parameters of neural networks

    A.H. Sung

  • Feature Selection for Intrusion Detection with Neural Networks and Support Vector Machines

    Srinivas Mukkamala;Andrew H. Sung

  • Polymorphic malicious executable scanner by API sequence analysis

    J.-Y. Xu;A.H. Sung;P. Chavez;S. Mukkamala

  • Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

    Qingzhong Liu;Andrew H. Sung;Zhongxue Chen;Jianyun Xu

  • Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Steganalysis

    Qingzhong Liu;A.H. Sung;Mengyu Qiao

  • Predicting injection profiles using ANFIS

    Mingzhen Wei;Baojun Bai;Andrew H. Sung;Qingzhong Liu

  • Modeling intrusion detection systems using linear genetic programming approach

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham

  • Intrusion Detection Using Ensemble of Soft Computing Paradigms

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham

  • The feature selection and intrusion detection problems

    Andrew H. Sung;Srinivas Mukkamala

  • Image complexity and feature mining for steganalysis of least significant bit matching steganography

    Qingzhong Liu;Andrew H. Sung;Bernardete Ribeiro;Mingzhen Wei

  • A comparative study of techniques for intrusion detection

    S. Mukkamala;A.H. Sung

  • Feature Selection for Intrusion Detection using Neural Networks and Support Vector Machines

    Andrew H. Sung;Srinivas Mukkamala

  • Intrusion Detection Systems Using Adaptive Regression Spines

    Srinivas Mukkamala;Andrew H. Sung;Ajith Abraham;Vitorino Ramos

  • Computationally intelligent agents for distributed intrusion detection system and method of practicing same

    Andrew H. Sung;Srinivas Mukkamala;Jean-Louis Lassez

  • Gene selection and classification for cancer microarray data based on machine learning and similarity measures

    Qingzhong Liu;Andrew H Sung;Zhongxue Chen;Jianzhong Liu

  • Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data

    Qingzhong Liu;Andrew H. Sung;Zhongxue Chen;Jianzhong Liu

Frequent Co-Authors

Ajith Abraham
Ajith Abraham Sai University
Xudong Huang
Xudong Huang Harvard University
Xiao Qin
Xiao Qin Auburn University
Baojun Bai
Baojun Bai Missouri University of Science and Technology
Alexander P. Reiner
Alexander P. Reiner University of Washington
Gil Atzmon
Gil Atzmon Albert Einstein College of Medicine
Laurence T. Yang
Laurence T. Yang St. Francis Xavier University
Nicholas J. Schork
Nicholas J. Schork Translational Genomics Research Institute
Pui-Yan Kwok
Pui-Yan Kwok University of California, San Francisco
Brian D. Athey
Brian D. Athey University of Michigan–Ann Arbor

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