Roberto Perdisci

Computer Networks and Communications · 2006–2017

A researcher in the Science corpus: 14 papers; trusts 48 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 90 citations into it; #802 of 9,886 by trust here, #1,147 by citations
• Artificial Intelligence — 83 citations into it; #5,566 of 35,345 by trust here, #8,619 by citations
• Signal Processing — 80 citations into it; #1,856 of 7,572 by trust here, #1,820 by citations
• Information Systems — 48 citations into it; #6,309 of 8,771 by trust here, #7,705 by citations
• Safety, Robustness & Interpretability — 22 citations into it
• Privacy & Security — 16 citations into it; #261 of 709 by trust here, #225 by citations
• Natural Language Processing — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it

Whom Roberto Perdisci trusts overall

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

1. Anil K. Jain
2. Ke Wang
3. Thorsten Joachims
4. Fabrizio Sebastiani
5. Wenke Lee
6. Christopher Krügel
7. Ulrich Bayer
8. John McHugh
9. Matthew V. Mahoney
10. Thomas G. Dietterich

Who trusts Roberto Perdisci overall

1. Battista Biggio
2. Fabio Roli
3. Giorgio Fumera
4. Joseph Gardiner
5. Igino Corona
6. Pavel Laskov
7. Giorgio Giacinto
8. Marcos Sebastián
9. Nedim Srndic
10. Monowar Hussain Bhuyan

Papers from Roberto Perdisci's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Misleading worm signature generators using deliberate noise injection (2006) — 207 citations
• Practical Attacks Against Graph-based Clustering (2017) — 90 citations
• PeerRush: Mining for unwanted P2P traffic (2013) — 107 citations
• Scalable fine-grained behavioral clustering of HTTP-based malware (2013) — 71 citations
• Early Detection of Malicious Flux Networks via Large-Scale Passive DNS Traffic Analysis (2012) — 126 citations
• From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware (2012) — 569 citations
• VAMO: towards a fully automated malware clustering validity analysis (2012) — 68 citations
• Detecting Malware Domains at the Upper DNS Hierarchy (2011) — 329 citations
• Behavioral Clustering of HTTP-Based Malware and Signature Generation Using Malicious Network Traces (2010) — 405 citations
• Building a Dynamic Reputation System for DNS (2010) — 520 citations
• McPAD: A multiple classifier system for accurate payload-based anomaly detection (2009) — 293 citations
• BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection (2008) — 1,251 citations
• Polymorphic Blending Attacks (2006) — 292 citations
• Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems (2006) — 249 citations