What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer security
- Software
Yier Jin mainly focuses on Computer security, Embedded system, Hardware Trojan, Cloud computing security and Cryptography.
The various areas that Yier Jin examines in his Computer security study include Software deployment, Logic gate, Power management, Smart grid and Web of Things.
His Embedded system study integrates concerns from other disciplines, such as Domain, Formal equivalence checking, Emerging technologies, Hardware security module and Overhead.
His work carried out in the field of Hardware Trojan brings together such families of science as Field-programmable gate array, Computer hardware and Encryption.
The Encryption study combines topics in areas such as Register-transfer level and Pre-play attack.
His research in Cryptography intersects with topics in Integrated circuit design, Internet security, Wearable technology and Design flow.
His most cited work include:
- Hardware Trojan detection using path delay fingerprint (500 citations)
- Hardware Trojans: Lessons Learned after One Decade of Research (183 citations)
- Experiences in Hardware Trojan design and implementation (170 citations)
What are the main themes of his work throughout his whole career to date?
Yier Jin mostly deals with Embedded system, Computer security, Cryptography, Hardware security module and Hardware Trojan.
His biological study spans a wide range of topics, including Trojan, Integrated circuit, Register-transfer level, Side channel attack and Formal verification.
His Integrated circuit research integrates issues from Digital electronics, Reverse engineering, Chip and Logic gate.
Encryption is the focus of his Computer security research.
The study incorporates disciplines such as Integrated circuit design and Wireless in addition to Cryptography.
His work deals with themes such as Computer hardware and Key, which intersect with Hardware Trojan.
He most often published in these fields:
- Embedded system (35.43%)
- Computer security (30.29%)
- Cryptography (14.86%)
What were the highlights of his more recent work (between 2019-2021)?
- Embedded system (35.43%)
- Artificial intelligence (6.29%)
- Side channel attack (7.43%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Embedded system, Artificial intelligence, Side channel attack, Adversarial system and Distributed computing.
Yier Jin has included themes like Hardware Trojan, Software, Reverse engineering and Code in his Embedded system study.
Hardware Trojan is the subject of his research, which falls under Trojan.
His studies deal with areas such as Machine learning, Electric power system, State and State variable as well as Artificial intelligence.
His Side channel attack research is within the category of Cryptography.
His Cryptography research focuses on subjects like Integrated circuit design, which are linked to Hardware security module.
Between 2019 and 2021, his most popular works were:
- Robust Adversarial Objects against Deep Learning Models (18 citations)
- Survey of machine learning methods for detecting false data injection attacks in power systems (12 citations)
- DeepEM: Deep Neural Networks Model Recovery through EM Side-Channel Information Leakage (9 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer security
- Software
Artificial intelligence, Embedded system, Deep learning, Adversarial system and Artificial neural network are his primary areas of study.
His studies in Artificial intelligence integrate themes in fields like Network topology, Machine learning, Residual and State variable.
His Machine learning research includes themes of Process, State and Electric power system.
His Embedded system study combines topics from a wide range of disciplines, such as Trojan, Hardware Trojan, Measure and Component.
His Deep learning research is multidisciplinary, incorporating perspectives in Point cloud and Data science.
His work investigates the relationship between Artificial neural network and topics such as Masking that intersect with problems in Side channel attack.
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