Most cited papers in Advanced Malware Detection Techniques
What are the most cited papers in Advanced Malware Detection Techniques?
The most cited paper in Advanced Malware Detection Techniques is "A detailed analysis of the KDD CUP 99 data set" (Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu et al., 2009), with 5,024 citations. Next come "The Sybil Attack" (4,436) and "UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)" (3,934).
Showing 50 of 100 papers. Every paper with more than 973 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | A detailed analysis of the KDD CUP 99 data set Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu et al. | Jul 2009 | 5,024 |
| 2 | The Sybil Attack John R. Douceur · Lecture notes in computer science | 2002 | 4,436 |
| 3 | UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) Nour Moustafa, Jill Slay | Nov 2015 | 3,934 |
| 4 | Practical Black-Box Attacks against Machine Learning Nicolas Papernot, Patrick Drew McDaniel, Ian Goodfellow et al. | 31 Mar 2017 | 3,549 |
| 5 | An Intrusion-Detection Model Dorothy E. Denning · IEEE Transactions on Software Engineering | Feb 1987 | 3,414 |
| 6 | A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection Anna L. Buczak, Erhan Guven · IEEE Communications Surveys & Tutorials | 26 Oct 2015 | 3,296 |
| 7 | Program Slicing Mark Weiser · IEEE Transactions on Software Engineering | Jul 1984 | 3,217 |
| 8 | IoT security: Review, blockchain solutions, and open challenges Minhaj Ahmad Khan, Khaled Salah · Future Generation Computer Systems | 26 Nov 2017 | 2,497 |
| 9 | Drebin: Effective and Explainable Detection of Android Malware in Your Pocket Daniel J. Arp, Michael Spreitzenbarth, Malte Hübner et al. | 2014 | 2,347 |
| 10 | TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones William Enck, Peter J. Gilbert, Byung-Gon Chun et al. | 4 Oct 2010 | 2,272 |
| 11 | Security, privacy and trust in Internet of Things: The road ahead Sabrina Sicari, Alessandra Rizzardi, Luigi Alfredo Grieco et al. · Computer Networks | 18 Nov 2014 | 2,189 |
| 12 | Dissecting Android Malware: Characterization and Evolution Yajin Zhou, Xuxian Jiang | May 2012 | 2,164 |
| 13 | Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Hassan Akhtar, Ajmal Saeed Mian · IEEE Access | 2018 | 2,089 |
| 14 | Hey, you, get off of my cloud Thomas Ristenpart, Eran Tromer, Hovav Shacham et al. | 9 Nov 2009 | 2,055 |
| 15 | A taxonomy of DDoS attack and DDoS defense mechanisms Jelena Mirković, Peter L. Reiher · ACM SIGCOMM Computer Communication Review | Apr 2004 | 2,025 |
| 16 | Survey of intrusion detection systems: techniques, datasets and challenges Ansam Khraisat, Iqbal Gondal, Peter W. Vamplew et al. · Cybersecurity | 17 Jul 2019 | 1,972 |
| 17 | Modern Operating Systems Andrew S. Tanenbaum | 1992 | 1,916 |
| 18 | Deep Learning Approach for Intelligent Intrusion Detection System R. Vinayakumar, Mamoun Alazab, K. P. Soman et al. · IEEE Access | 2019 | 1,915 |
| 19 | Language-based information-flow security Andrei Sabelfeld, Andrew C. Myers · IEEE Journal on Selected Areas in Communications | 2003 | 1,909 |
| 20 | A lattice model of secure information flow Dorothy E. Denning · Communications of the ACM | May 1976 | 1,909 |
| 21 | DDoS in the IoT: Mirai and Other Botnets Constantinos Kolias, Georgios Kambourakis, Angelos Stavrou et al. · Computer | 2017 | 1,905 |
| 22 | Experimental Security Analysis of a Modern Automobile Karl Koscher, Alexei Czeskis, Franziska Roesner et al. | 2010 | 1,872 |
| 23 | Adversarial Examples in the Physical World Alexey Kurakin, Ian Goodfellow, Samy Bengio | 27 Jul 2018 | 1,839 |
| 24 | A Deep Learning Approach to Network Intrusion Detection Nathan Shone, Trần Nguyên Ngọc, Vu Dinh Phai et al. · IEEE Transactions on Emerging Topics in Computational Intelligence | 19 Jan 2018 | 1,821 |
| 25 | A survey of network anomaly detection techniques Mohiuddin Ahmed, Abdun Naser Mahmood, Jiankun Hu · Journal of Network and Computer Applications | 16 Dec 2015 | 1,750 |
| 26 | An Analysis and Survey of the Development of Mutation Testing Yue Jia, Mark Harman · IEEE Transactions on Software Engineering | 23 Jun 2010 | 1,746 |
| 27 | CCFinder: a multilinguistic token-based code clone detection system for large scale source code T. Kamiya, Shinya Kusumoto, Koji Inoue · IEEE Transactions on Software Engineering | Jul 2002 | 1,737 |
| 28 | Evasion attacks against machine learning at test time Battista Biggio, Igino Corona, Davide Maiorca et al. | 2013 | 1,640 |
| 29 | Intrusion detection system: A comprehensive review Hung-Jen Liao, Chun‐Hung Richard Lin, Ying-Chih Lin et al. · Journal of Network and Computer Applications | 23 Sep 2012 | 1,631 |
| 30 | Accessorize to a Crime Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer et al. | 24 Oct 2016 | 1,599 |
| 31 | An overview of anomaly detection techniques: Existing solutions and latest technological trends Animesh Patcha, Jung-Min Jerry Park · Computer Networks | 17 Feb 2007 | 1,568 |
| 32 | DART Patrice Godefroid, Nils Klarlund, Koushik Sen | 12 Jun 2005 | 1,559 |
| 33 | N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders Yair Meidan, Michael Bohadana, Yael Mathov et al. · IEEE Pervasive Computing | Jul 2018 | 1,465 |
| 34 | Adversarial Examples Are Not Easily Detected Nicholas Carlini, David Wagner | 3 Nov 2017 | 1,434 |
| 35 | Fast Software Encryption Mitsuru Matsui · Lecture notes in computer science | 2002 | 1,423 |
| 36 | Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks Bolun Wang, Yuanshun Yao, Shawn Shan et al. | May 2019 | 1,417 |
| 37 | FlowDroid Steven Arzt, Siegfried Rasthofer, Christian Fritz et al. | 13 May 2014 | 1,402 |
| 38 | A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks Saman Taghavi Zargar, James B D Joshi, David Tipper · IEEE Communications Surveys & Tutorials | 2013 | 1,397 |
| 39 | CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang, Weishi Wang, Shafiq Joty et al. · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing | 2021 | 1,396 |
| 40 | Intrusion detection using sequences of system calls Steven A. Hofmeyr, Stephanie Forrest, Anil Somayaji · Journal of Computer Security | Jul 1998 | 1,319 |
| 41 | A Virtual Machine Introspection Based Architecture for Intrusion Detection. Tal Garfinkel, Mendel Rosenblum | 2003 | 1,319 |
| 42 | Internet of Things security: A survey Fadele Ayotunde Alaba, Mazliza Othman, Mohamed Hashem et al. · Journal of Network and Computer Applications | 7 Apr 2017 | 1,306 |
| 43 | Android permissions demystified Adrienne Porter Felt, Erika Chin, Steve Hanna et al. | 17 Oct 2011 | 1,298 |
| 44 | A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Khalid Al-Ali et al. · Qatar University QSpace (Qatar University) | 2020 | 1,297 |
| 45 | The geometry of innocent flesh on the bone Hovav Shacham | 28 Oct 2007 | 1,291 |
| 46 | Enforceable security policies Fred B. Schneider · ACM Transactions on Information and System Security | Feb 2000 | 1,289 |
| 47 | A Survey on Metaverse: Fundamentals, Security, and Privacy Yuntao Wang, Zhou Su, Ning Zhang et al. · IEEE Communications Surveys & Tutorials | 7 Sep 2022 | 1,287 |
| 48 | Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software James Newsome, Dawn Xiaodong Song · Figshare | 29 Jun 2018 | 1,287 |
| 49 | Clone detection using abstract syntax trees Ira D. Baxter, A. Yahin, Leonardo de Moura et al. | 27 Nov 2002 | 1,284 |
| 50 | An empirical study of the reliability of UNIX utilities Barton P. Miller, Lars Fredriksen, Bryan So · Communications of the ACM | Dec 1990 | 1,283 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.