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.
Study detects and mitigates stealthy DDoS attacks in IoT networks.
Survey examines ML for IoT security, addressing new challenges.
Deep learning identifies unknown IoT devices in network traffic.
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…
Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML…
FDA3 defends IIoT applications against adversarial attacks by federating defense knowledge.
To reap the benefits of the Internet of Things (IoT), it is imperative to secure the system against cyber attacks in order to enable mission critical and real-time applications. To this end, intrusion detection systems (IDSs) have been widely used to detect anomalies caused by a cyber attacker in IoT systems. However, …
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 tackles cyber threats to PHM systems using adversarial examples.
Predictive Q-learning algorithm for IoT networks with human operators.
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and I…
RAD detects anomalies in unreliable data streams with up to 98% accuracy.
Efficiently preserves privacy in logistic regression for IoT data.
Project compares KDDCup99 and NSL-KDD datasets using ML classifiers.
FedHDPrivacy uses DP to improve FL in IoT, maintaining high accuracy.
This paper studies the effect of various hyper-parameters and their selection for the best performance of the deep learning model proposed in [1] for distributed attack detection in the Internet of Things (IoT). The findings show that there are three hyper-parameters that have more influence on the best performance ach…
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 attack intensity of distributed denial of service (DDoS) attacks is increasing every year. Botnets based on internet of things (IOT) devices are now being used to conduct DDoS attacks. The estimation of direct and indirect economic damages caused by these attacks is a complex problem. One of the indirect damage of …
This work surveys attacks and defenses on edge neural networks.
APGE protects graph node representations from inference attacks.
Study on collaboration vs. independent data collection in sensor networks.
Survey of IoT recommendation systems and their limitations.
This paper improves AI defenses against network attacks using ML and adversarial learning.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches develope…
Paper uses ensemble learning for IoT cybersecurity anomaly detection.
Adaptive anomaly detection for IoT data reduces delay by 84%.
This paper compares machine and deep learning algorithms for IoT data classification.
IoT nodes compress measurements into DNN outputs for efficient communication.
DarkneTZ protects edge devices from DNN model leaks using TEE and model partitioning.
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 …
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
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…
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…
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load. In order to support a real-time execution of the considered DL model at the IoT unit level, DL sol…
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
A framework for real-time edge intelligence using federated meta-learning.
DOMKL learns functions from IoT data with minimal regret and consensus constraints.
This review explores federated learning for IoT data privacy.
FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.
Paper proposes SDS for 5G security using machine learning.
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…
The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT. In this paper, we consider a voting-based MARL problem, in which the agents vote to make grou…
State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability t…
Paper proposes DP-PASGD for efficient, private IoT learning.
Researchers analyze inverse optimal transport, deriving theoretical and empirical insights.
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 …
The paper uses interpretable ML to secure data quality in IoT edge computing.