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Computer Science

D-Index
47
Citations
14104
World Ranking
6331
National Ranking
100

Overview

Asaf Shabtai is affiliated with Ben-Gurion University of the Negev in Israel, focusing primarily on computer science research. Their work encompasses a range of subfields, including artificial intelligence, computer networks and communications, information systems, signal processing, and computer vision and pattern recognition.

Their research extensively covers various topics such as network security and intrusion detection, adversarial robustness in machine learning, advanced malware detection techniques, anomaly detection techniques and applications, information and cyber security, internet traffic analysis and secure e-voting, and smart grid security and resilience.

Shabtai has contributed to numerous publications, with recent papers including:

  • Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA, 2021, IEEE Transactions on Dependable and Secure Computing
  • A novel approach for detecting vulnerable IoT devices connected behind a home NAT, 2020, Computers & Security
  • The Security of IP-Based Video Surveillance Systems, 2020, MDPI (MDPI AG)
  • Android malware detection via an app similarity graph, 2021, Computers & Security
  • TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack, 2022, IEEE Transactions on Information Forensics and Security

Frequent co-authors collaborating with Shabtai include:

  • Yuval Elovici (120 joint publications)
  • Ron Bitton (26 joint publications)
  • Dudu Mimran (21 joint publications)
  • Oleg Brodt (19 joint publications)
  • Rami Puzis (15 joint publications)

Their research outputs are often published in venues such as:

  • arXiv (Cornell University) with 79 publications
  • Computers & Security with 14 publications
  • Zenodo (CERN European Organization for Nuclear Research) with 9 publications
  • SSRN Electronic Journal with 7 publications
  • Sensors with 5 publications

Shabtai's academic focus demonstrates a sustained interest in cybersecurity challenges, particularly in detecting and mitigating threats in networked systems and developing resilient machine learning models against adversarial attacks. Their interdisciplinary approach integrates methods from artificial intelligence and signal processing to strengthen information security frameworks.

Best Publications

  • N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders

    Yair Meidan;Michael Bohadana;Yael Mathov;Yisroel Mirsky

  • Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection.

    Yisroel Mirsky;Tomer Doitshman;Yuval Elovici;Asaf Shabtai

  • N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

    Yair Meidan;Michael Bohadana;Yael Mathov;Yisroel Mirsky

  • Andromaly: a behavioral malware detection framework for android devices

    Asaf Shabtai;Uri Kanonov;Yuval Elovici;Chanan Glezer

  • Google Android: A Comprehensive Security Assessment

    A. Shabtai;Y. Fledel;U. Kanonov;Y. Elovici

  • ProfilIoT: a machine learning approach for IoT device identification based on network traffic analysis

    Yair Meidan;Michael Bohadana;Asaf Shabtai;Juan David Guarnizo

  • Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

    Asaf Shabtai;Robert Moskovitch;Yuval Elovici;Chanan Glezer

  • Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

    Moshe Kravchik;Asaf Shabtai

  • Detecting unknown malicious code by applying classification techniques on OpCode patterns

    Asaf Shabtai;Robert Moskovitch;Clint Feher;Shlomi Dolev

  • A Survey of Data Leakage Detection and Prevention Solutions

    Asaf Shabtai;Yuval Elovici;Lior Rokach

  • Securing Android-Powered Mobile Devices Using SELinux

    Asaf Shabtai;Yuval Fledel;Yuval Elovici

  • Automated Static Code Analysis for Classifying Android Applications Using Machine Learning

    Asaf Shabtai;Yuval Fledel;Yuval Elovici

  • Mobile malware detection through analysis of deviations in application network behavior

    Asaf Shabtai;Lena Tenenboim-Chekina;Dudu Mimran;Lior Rokach

  • Detection of Unauthorized IoT Devices Using Machine Learning Techniques

    Yair Meidan;Michael Bohadana;Asaf Shabtai;Martín Ochoa

  • Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

    Ishai Rosenberg;Asaf Shabtai;Yuval Elovici;Lior Rokach

  • Efficient Cyber Attacks Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

    Moshe Kravchik;Asaf Shabtai

  • Security Testbed for Internet-of-Things Devices

    Shachar Siboni;Vinay Sachidananda;Yair Meidan;Michael Bohadana

  • Improving malware detection by applying multi-inducer ensemble

    Eitan Menahem;Asaf Shabtai;Lior Rokach;Yuval Elovici

  • Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

    Ishai Rosenberg;Asaf Shabtai;Lior Rokach;Yuval Elovici

  • Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method

    Asaf Shabtai;Uri Kanonov;Yuval Elovici

  • Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

    Moshe Kravchik;Asaf Shabtai

Frequent Co-Authors

Yuval Elovici
Yuval Elovici Ben-Gurion University of the Negev
Lior Rokach
Lior Rokach Ben-Gurion University of the Negev
Bracha Shapira
Bracha Shapira Ben-Gurion University of the Negev
Robert Moskovitch
Robert Moskovitch Columbia University
Yuval Shahar
Yuval Shahar Ben-Gurion University of the Negev
Battista Biggio
Battista Biggio University of Cagliari
Shlomi Dolev
Shlomi Dolev Ben-Gurion University of the Negev
Antonio Puliafito
Antonio Puliafito University of Messina
Francesco Longo
Francesco Longo University of Messina
Nils Ole Tippenhauer
Nils Ole Tippenhauer Saarland University

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