Charles Smutz

Signal Processing · 2012–2016

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 21 citations into it; #667 of 7,572 by trust here, #2,457 by citations
• Computer Networks and Communications — 20 citations into it; #633 of 9,886 by trust here, #3,074 by citations
• Artificial Intelligence — 15 citations into it; #12,690 of 35,345 by trust here, #29,412 by citations
• Information Systems — 14 citations into it; #680 of 8,771 by trust here, #3,402 by citations
• Privacy & Security — 12 citations into it
• Safety, Robustness & Interpretability — 9 citations into it; #384 of 2,251 by trust here, #1,512 by citations

Whom Charles Smutz trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Chih-Jen Lin
2. Pavel Laskov
3. Nedim Srndic
4. Prahlad Fogla
5. Robin Sommer
6. Davide Maiorca
7. Marco Barreno
8. Mehran Sahami
9. Blaine Nelson
10. Angelos Stavrou

Who trusts Charles Smutz overall

1. Battista Biggio
2. Davide Maiorca
3. Arjun Nitin Bhagoji
4. Nedim Srndic
5. Huang Xiao
6. Joseph Gardiner
7. Fabio Roli
8. Fatima Salahdine
9. Giorgio Giacinto
10. Weilin Xu

Papers from Charles Smutz's perspective

Ordered by how strongly the researchers Charles Smutz trusts endorse each paper. Same corpus, a different order for every researcher.

1. Evading network anomaly detection systems: formal reasoning and practical techniques
2. Can machine learning be secure?
3. LIBSVM: A library for support vector machines
4. Detection and analysis of drive-by-download attacks and malicious JavaScript code
5. Exploiting Machine Learning to Subvert Your Spam Filter
6. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
7. Misleading worm signature generators using deliberate noise injection
8. Poisoning Attacks against Support Vector Machines
9. Scalable, Behavior-Based Malware Clustering
10. Abusing File Processing in Malware Detectors for Fun and Profit

Their papers

• When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors (2016) — 100 citations
• Malicious PDF detection using metadata and structural features (2012) — 307 citations