What is he best known for?
The fields of study he is best known for:
- Operating system
- The Internet
- Computer security
Ali Dehghantanha focuses on Computer security, Android, Cloud computing, Digital forensics and Malware.
Ali Dehghantanha has researched Computer security in several fields, including Event, Multitier architecture and Damages.
His work deals with themes such as Cloud storage, Support vector machine, Embedded system, Mobile device forensics and Mobile device, which intersect with Android.
Ali Dehghantanha works mostly in the field of Cloud computing, limiting it down to topics relating to Intrusion detection system and, in certain cases, Public service, Classifier, Linear discriminant analysis, Resilience and Dimensionality reduction.
His Digital forensics research is multidisciplinary, relying on both Watson and Internet privacy.
His Malware research incorporates elements of Data mining, Identification, Cryptography, Opcode and Artificial intelligence.
His most cited work include:
- Internet of Things security and forensics: Challenges and opportunities (252 citations)
- Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing (161 citations)
- A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks (152 citations)
What are the main themes of his work throughout his whole career to date?
Ali Dehghantanha spends much of his time researching Computer security, Malware, Artificial intelligence, Digital forensics and Cloud computing.
His study focuses on the intersection of Computer security and fields such as Mobile device with connections in the field of Android.
His studies deal with areas such as Computer network, Data mining, Support vector machine and Opcode as well as Malware.
The various areas that he examines in his Artificial intelligence study include Machine learning and Ransomware.
His research investigates the connection with Digital forensics and areas like Field which intersect with concerns in Data science.
His work carried out in the field of Cloud computing brings together such families of science as World Wide Web and OS X.
He most often published in these fields:
- Computer security (43.78%)
- Malware (19.90%)
- Artificial intelligence (17.91%)
What were the highlights of his more recent work (between 2019-2021)?
- Computer security (43.78%)
- Artificial intelligence (17.91%)
- Malware (19.90%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Computer security, Artificial intelligence, Malware, Machine learning and Blockchain.
His study in the field of Personally identifiable information is also linked to topics like Attribution.
His work in Artificial intelligence tackles topics such as Information technology which are related to areas like Internet privacy, Cyberwarfare and Espionage.
His work deals with themes such as Random forest, The Internet, Opcode and Identification, which intersect with Malware.
His work in the fields of Machine learning, such as Convolutional neural network, intersects with other areas such as Static analysis.
His Blockchain research includes themes of Domain, Network security and Data science.
Between 2019 and 2021, his most popular works were:
- A systematic literature review of blockchain cyber security (101 citations)
- Know Abnormal, Find Evil: Frequent Pattern Mining for Ransomware Threat Hunting and Intelligence (74 citations)
- An Energy-Efficient SDN Controller Architecture for IoT Networks With Blockchain-Based Security (29 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- The Internet
- Law
Ali Dehghantanha mainly focuses on Computer security, Malware, Blockchain, Machine learning and Artificial intelligence.
His biological study spans a wide range of topics, including Smart city and Cyber-physical system.
His study in Malware focuses on Ransomware in particular.
His work carried out in the field of Ransomware brings together such families of science as Artificial neural network and Robustness.
His Blockchain research is multidisciplinary, relying on both Domain, Data science, Public-key cryptography, Web application and Personally identifiable information.
His is doing research in Deep learning and Convolutional neural network, both of which are found in Machine learning.
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