2,500 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
IoT Anomaly Detection Methods and Applications: A Survey
Ongoing research on anomaly detection for the Internet of Things (IoT) is a
rapidly expanding field. This growth necessitates an examination of application
trends and current gaps. The vast majority of those publications are in areas
such as network and infrastructure security, sensor monitoring, smart home, and
smart city applications and are extending into even more sectors. Recent
advancements in the field have increased the necessity to study the many IoT
anomaly detection applications. This paper begins with a summary of the
detection methods and applications, accompanied by a discussion of the
categorization of IoT anomaly detection algorithms. We then discuss the current
publications to identify distinct application domains, examining papers chosen
based on our search criteria. The survey considers 64 papers among recent
publications published between January 2019 and July 2021. In recent
publications, we observed a shortage of IoT anomaly detection methodologies,
for example, when dealing with the integration of systems with various sensors,
data and concept drifts, and data augmentation where there is a shortage of
Ground Truth data. Finally, we discuss the present such challenges and offer
new perspectives where further research is required.Comment: 22 page
Clustered Federated Learning Architecture for Network Anomaly Detection in Large Scale Heterogeneous IoT Networks
There is a growing trend of cyberattacks against Internet of Things (IoT)
devices; moreover, the sophistication and motivation of those attacks is
increasing. The vast scale of IoT, diverse hardware and software, and being
typically placed in uncontrolled environments make traditional IT security
mechanisms such as signature-based intrusion detection and prevention systems
challenging to integrate. They also struggle to cope with the rapidly evolving
IoT threat landscape due to long delays between the analysis and publication of
the detection rules. Machine learning methods have shown faster response to
emerging threats; however, model training architectures like cloud or edge
computing face multiple drawbacks in IoT settings, including network overhead
and data isolation arising from the large scale and heterogeneity that
characterizes these networks.
This work presents an architecture for training unsupervised models for
network intrusion detection in large, distributed IoT and Industrial IoT (IIoT)
deployments. We leverage Federated Learning (FL) to collaboratively train
between peers and reduce isolation and network overhead problems. We build upon
it to include an unsupervised device clustering algorithm fully integrated into
the FL pipeline to address the heterogeneity issues that arise in FL settings.
The architecture is implemented and evaluated using a testbed that includes
various emulated IoT/IIoT devices and attackers interacting in a complex
network topology comprising 100 emulated devices, 30 switches and 10 routers.
The anomaly detection models are evaluated on real attacks performed by the
testbed's threat actors, including the entire Mirai malware lifecycle, an
additional botnet based on the Merlin command and control server and other
red-teaming tools performing scanning activities and multiple attacks targeting
the emulated devices
A Self-Configuration Controller To Detect, Identify, and Recover Misconfiguration At IoT Edge Devices and Containerized Cluster System
Source at https://icissp.scitevents.org/.Securing workloads and information flow against misconfiguration in container-based clusters and edge medical devices is an important part of overall system security. This paper presented a controller that analyzes the misconfiguration, maps the observation to its hidden misconfiguration type, and selects the optimal recovery policy to maximize the performance of defined metrics. In the future, we will integrate streaming from different edge devices, expand the recovery mechanism, and conduct more experiments
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