Toward an in-Kernel High Performance Key-Value Store Implementation

Lazri, Kahina; Blin, Antoine; Sopena, Julien; Muller, Gilles

doi:10.1109/srds47363.2019.00038

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…eBPF has been used to rewrite a significant number of the network's fundamental functionalities, including but not limited to routing [18], switching [19], and firewalls [20]. These features include load balancing [21], key-value storage [22], application level filtering [23], and DDoS mitigation [24]. In-KeV [25] is a framework for developing network services that run within the kernel.…”

Section: Related Workmentioning

confidence: 99%

High-performance Intrusion Detection Systemusing eBPF with Machine Learning algorithms

ANAND,

Aakula

2023

Preprint

View full text Add to dashboard Cite

Denial of Service (DoS) and Distributed DoS (DDoS) attacks are standard prob-lems organizations that rely on network services face. Detecting these attackspromptly and accurately is crucial to mitigating the damage caused. This paperproposes an Intrusion Detection System (IDS) that utilizes the extended Berke-ley Packet Filter (eBPF) with machine learning algorithms, namely Decision Tree(DT), Random Forest (RF), Support Vector Machine (SVM), and TwinSVM.eBPF is a bytecode-based virtual machine that runs programs without modifyingthe kernel source code. It can implement various services, such as observabil-ity, security, and networking. Socket filters are an eBPF program attached tothe socket in the Linux kernel that allows for efficient filtering and manip-ulation of network packets at the socket after packets are received from thenetwork stack. Packets that are filtered at the socket level before enteringthe user space. The steps involved in the proposed model are: a) collectingdata from famous repository, CIC-IDS-2017. b) Once the raw data is obtained,it undergoes preprocessing, which includes data transmission, cleaning, reduc-tion, and discretization. c) Following the preprocessing step, an ANOVA F-testextracts specific features from the preprocessed data. d) Lastly, the extractedfeatures are analyzed for intrusion detection using various ML algorithms:DT, RF, SVM, and TwinSVM. e) The eBPF program captures network traf-fic and utilizes trained model parameters to detect attacks within the kernel.Our experimental results show that the accuracy of our proposed ML algo-rithms, DT, RF, SVM, and TwinSVM, outperforms the existing related work:99.38, 99.44, 88.73, and 93.82, respectively. The experimental code available inhttps://github.com/NemalikantiAnand/Project.git

show abstract

Section: Related Workmentioning

confidence: 99%

High-performance Intrusion Detection Systemusing eBPF with Machine Learning algorithms

ANAND,

Aakula

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A trainable set is used to aggregate the node features in the spatial layer. Represented by n(i), the node features are weighted by the continuous kernel function [74]. Te spatial relation of the nodes is stated by pseudocoordinates in U. Presenting no restrictions on U, no values are lost in a local neighborhood as it can contain edge weights, features of nodes, and local data.…”

Section: Spatial-based Gcnmentioning

confidence: 99%

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

Chowdhury,

Sany,

Ahamed

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.

show abstract

Design and implementation of an intrusion detection system by using Extended BPF in the Linux kernel

Wang

Chang

2022

Journal of Network and Computer Applications

View full text Add to dashboard Cite

Toward an in-Kernel High Performance Key-Value Store Implementation

Cited by 4 publications

References 3 publications

High-performance Intrusion Detection Systemusing eBPF with Machine Learning algorithms

High-performance Intrusion Detection Systemusing eBPF with Machine Learning algorithms

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

Design and implementation of an intrusion detection system by using Extended BPF in the Linux kernel

Contact Info

Product

Resources

About