What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
Giorgio Giacinto spends much of his time researching Artificial intelligence, Machine learning, Pattern recognition, Computer security and Malware.
Quadratic classifier, Classifier, False positive rate, Support vector machine and False positive paradox are the core of his Artificial intelligence study.
His research integrates issues of Data mining and Pattern recognition in his study of Machine learning.
His research investigates the connection between Pattern recognition and topics such as Contextual image classification that intersect with problems in Probabilistic neural network and Time delay neural network.
His biological study deals with issues like Adversarial machine learning, which deal with fields such as Adversary, Unsupervised learning and Cluster analysis.
His studies in Malware integrate themes in fields like Feature extraction, Static analysis and Evasion.
His most cited work include:
- Evasion attacks against machine learning at test time (939 citations)
- Design of effective neural network ensembles for image classification purposes (331 citations)
- McPAD: A multiple classifier system for accurate payload-based anomaly detection (227 citations)
What are the main themes of his work throughout his whole career to date?
Giorgio Giacinto mainly focuses on Artificial intelligence, Machine learning, Computer security, Pattern recognition and Malware.
His research on Artificial intelligence frequently connects to adjacent areas such as Data mining.
His biological study spans a wide range of topics, including Adversary and Attack patterns.
Giorgio Giacinto works mostly in the field of Computer security, limiting it down to topics relating to Field and, in certain cases, Taxonomy, as a part of the same area of interest.
Giorgio Giacinto interconnects Cluster analysis and Biometrics in the investigation of issues within Pattern recognition.
His Malware study integrates concerns from other disciplines, such as Adversarial system, Adversarial machine learning, Android and Evasion.
He most often published in these fields:
- Artificial intelligence (48.48%)
- Machine learning (33.94%)
- Computer security (27.27%)
What were the highlights of his more recent work (between 2017-2020)?
- Malware (24.24%)
- Computer security (27.27%)
- Artificial intelligence (48.48%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Malware, Computer security, Artificial intelligence, Adversarial system and Android.
The study incorporates disciplines such as Static analysis and Cluster analysis in addition to Malware.
In his research, Scripting language and Deep learning is intimately related to Executable, which falls under the overarching field of Computer security.
The concepts of his Artificial intelligence study are interwoven with issues in Evasion, Machine learning and Vulnerability.
His Machine learning research is multidisciplinary, incorporating elements of Feature extraction, Content-based image retrieval, Semantic gap and Relevance feedback.
While the research belongs to areas of Android, Giorgio Giacinto spends his time largely on the problem of Ransomware, intersecting his research to questions surrounding Encryption and Interpretability.
Between 2017 and 2020, his most popular works were:
- Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (124 citations)
- Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (79 citations)
- Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
Giorgio Giacinto mostly deals with Malware, Computer security, Artificial intelligence, Adversarial system and Android malware.
The Malware study combines topics in areas such as Deep learning, Adversarial machine learning, Support vector machine, Evasion and Feature extraction.
His study focuses on the intersection of Adversarial machine learning and fields such as Field with connections in the field of JavaScript.
His research in Feature extraction intersects with topics in Algorithm design, Machine learning and Scalability.
His Machine learning study frequently draws parallels with other fields, such as Static analysis.
His studies in Android malware integrate themes in fields like Ransomware, Encryption and Obfuscation.
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