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.
Paper uses ensemble learning for IoT cybersecurity anomaly detection.
Study finds companies react negatively to material cybersecurity incident disclosures.
Study evaluates early-stage cybersecurity firms' performance using Crunchbase data.
Model for optimal cybersecurity investment considering clustered cyberattacks.
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
Deep Neural Network (DNN) workloads are quickly moving from datacenters onto edge devices, for latency, privacy, or energy reasons. While datacenter networks can be protected using conventional cybersecurity measures, edge neural networks bring a host of new security challenges. Unlike classic IoT applications, edge ne…
Deep learning identifies unknown IoT devices in network traffic.
Introduces an artificial cyber lab to test and identify cyber resilience measures.
Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulner…
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…
Survey examines ML for IoT security, addressing new challenges.
Survey of IoT recommendation systems and their limitations.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
Paper proposes SDS for 5G security using machine learning.
Develops a Bonus-Malus model for cyber risk insurance to incentivize cybersecurity.
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…
IoT nodes compress measurements into DNN outputs for efficient communication.
This work develops agents to learn generalizable policies for dynamic network environments.
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…
TinyML models detect RF and cyber threats in spacecraft with low latency.
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 …
Study detects and mitigates stealthy DDoS attacks in IoT networks.
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 …
Paper tackles RCA in complex networks with unknown interdependencies.
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
DOMKL learns functions from IoT data with minimal regret and consensus constraints.
This review explores federated learning for IoT data privacy.
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…
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
Paper proposes DP-PASGD for efficient, private IoT learning.
Researchers analyze inverse optimal transport, deriving theoretical and empirical insights.
Paper tackles cybersecurity attack detection with an ensemble approach.
Predictive Q-learning algorithm for IoT networks with human operators.
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.
The paper examines the feasibility of managing aggregate cyber-risk in IoT environments.
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, …
Energy-efficient DL inference for IoT devices reduces power consumption and improves performance.
The paper shows supply chain features improve cyber risk prediction.
Paper proposes DRL for unsupervised IoT localization.
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…
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…