A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning identifies unknown IoT devices in network traffic.
Paper uses ensemble learning for IoT cybersecurity anomaly detection.
Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time. This disjunction makes the state-of-art deep learning algorithms, i.e. CNN (Convolutional Neural Networ…
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new paradigm called EdgeAI to address major impediments associated with deploying deep networks at the edge. Specifically, we discuss the existing …
Study detects and mitigates stealthy DDoS attacks in IoT networks.
Federated Learning helps IoT devices learn without central servers.
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices around the world, where the IoT devices collect and share information to reflect status of the physical world. The Autonomous Control System (ACS), on the other hand, performs control functions on the physical systems without ex…
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy is…
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in…
Survey examines ML for IoT security, addressing new challenges.
By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wir…
Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can barely afford complex DNN models due to limited computational power and energy supply. While one can offload anomaly detection tasks to the cloud, it incurs long delay and requires large bandwidth …
Paper proposes DP-PASGD for efficient, private IoT learning.
Energy-efficient DL inference for IoT devices reduces power consumption and improves performance.
This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.
Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the number of new applications that can be deployed on an IoT system. A number of compression techniques have been pr…
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
Federated learning for data streams tackles real-time learning from IoT devices.
Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical so…
Paper proposes a multi-phase pruning pipeline for deep ensemble learning on IIoT devices.
Model compression has emerged as an important area of research for deploying deep learning models on Internet-of-Things (IoT). However, for extremely memory-constrained scenarios, even the compressed models cannot fit within the memory of a single device and, as a result, must be distributed across multiple devices. Th…
The paper uses interpretable ML to secure data quality in IoT edge computing.
IoT nodes compress measurements into DNN outputs for efficient communication.
In recent years, the growth of Internet of Things (IoT) as an emerging technology has been unbelievable. The number of networkenabled devices in IoT domains is increasing dramatically, leading to the massive production of electronic data. These data contain valuable information which can be used in various areas, such …
Work addresses long-term accuracy issues in IoT air quality sensors.
New algorithms improve signal processing in federated learning.
DOMKL learns functions from IoT data with minimal regret and consensus constraints.
Recently, a wide range of smart devices are deployed in a variety of environments to improve the quality of human life. One of the important IoT-based applications is smart homes for healthcare, especially for elders. IoT-based smart homes enable elders' health to be properly monitored and taken care of. However, elder…
FLFE improves machine learning by efficiently and securely transforming features.
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to …
Paper proposes DRL for unsupervised IoT localization.
Efficiently preserves privacy in logistic regression for IoT data.
Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inferen…
FDA3 defends IIoT applications against adversarial attacks by federating defense knowledge.
To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …
The preponderance of connected devices provides unprecedented opportunities for fine-grained monitoring of the public infrastructure. However while classical models expect high quality application-specific data streams, the promise of the Internet of Things (IoT) is that of an abundance of disparate and noisy datasets …
FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.
New method reduces cloud usage for mobile/IoT predictions.
SGQuant reduces GNN memory usage without significant accuracy loss.
This paper reviews methods to create compact neural networks for IoT applications.
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on …
Flexible framework improves communication efficiency across various systems.
DarkneTZ protects edge devices from DNN model leaks using TEE and model partitioning.
Internet-of-Things (IoT) devices that are limited in power and processing are susceptible to physical layer (PHY) spoofing (signal exploitation) attacks owing to their inability to implement a full-blown protocol stack for security. The overwhelming adoption of multicarrier techniques such as orthogonal frequency divis…
Paper removes sensitive data from IoT and Big Data for privacy.
PSO optimizes hyperparameters for edge ML models in FL.