62 citations · 107 across the 24 of their papers we have counts for
24 papers
FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities
Haochen Liang, Jie Zhang, Hideya Ochiai
Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective c…
FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization
Shunan Zhu, Jiawei Chen, Yonghao Yu +1
As high quality public data becomes scarce, Federated Learning (FL) provides a vital pathway to leverage valuable private user data while preserving privacy. However, real-world cl…
NeuroSCA: Neuro-Symbolic Constraint Abstraction for Smart Contract Hybrid Fuzzing
Haochen Liang, Jiawei Chen, Hideya Ochiai
Hybrid fuzzing combines greybox fuzzing's throughput with the precision of symbolic execution to uncover deep smart contract vulnerabilities. However, its effectiveness is often li…
University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system
Takara Taniguchi, Yudai Ueda, Atsuya Muramatsu +4
Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements…
Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera
Hideya Ochiai, Kohki Hashimoto, Takuya Sakamoto +5
Artificial intelligence enables smarter control in building automation by its learning capability of users' preferences on facility control. Reinforcement learning (RL) was one of…
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication
Hideya Ochiai, Riku Nishihata, Eisuke Tomiyama +2
Anomaly detection is an important function in IoT applications for finding outliers caused by abnormal events. Anomaly detection sometimes comes with high-frequency data sampling w…
Associative Transformer
Yuwei Sun, Hideya Ochiai, Zhirong Wu +2
Emerging from the pairwise attention in conventional Transformers, there is a growing interest in sparse attention mechanisms that align more closely with localized, contextual lea…
Detection of Anomalous Network Nodes via Hierarchical Prediction and Extreme Value Theory
Sevvandi Kandanaarachchi, Mahdi Abolghasemi, Hideya Ochiai +2
Continuously evolving cyber-attacks against industrial networks reduce the effectiveness of signature-based detection methods. Once malware has infiltrated a network (for example,…
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Trojan attacks embed perturbations in input data leading to malicious behavior in neural network models. A combination of various Trojans in different modalities enables an adversa…
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks
Naoya Tezuka, Hideya Ochiai, Yuwei Sun +1
Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to con…
Bidirectional Contrastive Split Learning for Visual Question Answering
Yuwei Sun, Hideya Ochiai
Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a rob…
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning
Hideya Ochiai, Yuwei Sun, Qingzhe Jin +2
Privacy-sensitive data is stored in autonomous vehicles, smart devices, or sensor nodes that can move around with making opportunistic contact with each other. Federation among suc…
Detecting Anomalous LAN Activities under Differential Privacy
Norrathep Rattanavipanon, Donlapark Ponnoprat, Hideya Ochiai +3
Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely stu…
Feature Distribution Matching for Federated Domain Generalization
Yuwei Sun, Ng Chong, Hideya Ochiai
Multi-source domain adaptation has been intensively studied. The distribution shift in features inherent to specific domains causes the negative transfer problem, degrading a model…
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Model poisoning attacks on federated learning (FL) intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such comprom…
MAC address randomization tolerant crowd monitoring system using Wi-Fi packets
Yuyi Cai, Manabu Tsukada, Hideya Ochiai +1
Media access control (MAC) addresses inside Wi-Fi packets can be used for beneficial activities such as crowdedness estimation, marketing, and hazard maps. However, the MAC address…
Misbehavior Detection Using Collective Perception under Privacy Considerations
Manabu Tsukada, Shimpei Arii, Hideya Ochiai +1
In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for pr…
Homogeneous Learning: Self-Attention Decentralized Deep Learning
Yuwei Sun, Hideya Ochiai
Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth…
Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection
Yuwei Sun, Ng Chong, Hideya Ochiai
With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instea…
Suspicious ARP Activity Detection and Clustering Based on Autoencoder Neural Networks
Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
The rapidly increasing number of smart devices on the Internet necessitates an efficient inspection system for safeguarding our networks from suspicious activities such as Address…
Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving
Mai Hirata, Manabu Tsukada, Keisuke Okumura +3
Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS app…
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks
Yuwei Sun, Ng Chong, Hideya Ochiai
An attack on deep learning systems where intelligent machines collaborate to solve problems could cause a node in the network to make a mistake on a critical judgment. At the same…
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) s…
Honeyboost: Boosting honeypot performance with data fusion and anomaly detection
Sevvandi Kandanaarachchi, Hideya Ochiai, Asha Rao
With cyber incidents and data breaches becoming increasingly common, being able to predict a cyberattack has never been more crucial. The ability of Network Anomaly Detection Syste…