Results 71 to 80 of about 26,597 (201)
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
Applied AI Letters, Volume 7, Issue 2, June 2026.This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang +3 more
wiley +1 more source
AlgorithmsWith the escalating global cyber threats, Distributed Denial of Service (DDoS) attacks have become one of the most disruptive and prevalent network attacks.
Yi Li, Xingzhou Deng, Ang Yang, Jing Gao
doaj +1 more source
CALD : surviving various application-layer DDoS attacks that mimic flash crowd
, 2010 Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The
Jia, Weijia +4 more
core +1 more source
The Role of N6‐Methyladenosine Modification in Health and Disease
MedComm, Volume 7, Issue 6, June 2026.N6‐methyladenosine (m6A) is the most prevalent internal RNA modification in eukaryotes, acting as a pivotal epitranscriptomic regulator of RNA metabolism. This modification plays a dual role: it maintains physiological homeostasis under normal conditions but drives disease progression when dysregulated.
Linghuan Li +6 more
wiley +1 more source
Network attack detection at flow level
, 2011 In this paper, we propose a new method for detecting unauthorized network intrusions, based on a traffic flow model and Cisco NetFlow protocol application.
C. Douligeris +3 more
core +1 more source
Generating Pattern‐Based Datasets for Cyber Attack Detection Using Machine‐Learning Techniques
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.The aim of this work is to review the state of the art in the design, generation, and labeling of attack pattern datasets for training of detection systems based on machine learning. ABSTRACT This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on ...
Pedro Díaz García +4 more
wiley +1 more source
DDoS Attacks with Randomized Traffic Innovation: Botnet Identification Challenges and Strategies
, 2016 Distributed Denial-of-Service (DDoS) attacks are usually launched through the $botnet$, an "army" of compromised nodes hidden in the network. Inferential tools for DDoS mitigation should accordingly enable an early and reliable discrimination of the ...
Di Mauro, Mario +2 more
core +1 more source
MdRLKT1–MdRAX2–MdMKS1 Module Positively Regulating Resistance to Cytospora mali in Apple
Plant Biotechnology Journal, Volume 24, Issue 6, Page 4105-4120, June 2026.ABSTRACT Valsa canker (caused by Cytospora mali = Valsa mali. C. mali) is one of the most destructive diseases affecting apple cultivation. The scarcity of natural germplasm resources with high resistance and immunity underscores the importance of exploring plant immune regulation factors of disease‐resistant breeding.
Yanan Tang +5 more
wiley +1 more source
Cogent EngineeringDistributed Denial of Service (DDoS) attack makes a server inaccessible by flooding it with fallacious traffic. It uses many intermediate devices such as computers, servers, smartphones, and even IoT Devices to generate false traffic.
Bindu Madavi K. P. +3 more
doaj +1 more source
DDoS Attack Detection Based on Self-organizing Mapping Network in Software Defined Networking
MATEC Web of Conferences, 2018 The software defined networking is a new kind of network architecture, the programmability of SDN enables hackers to easily launch DDoS attack on the network through software programming. To solve the problem, a DDoS attack detection scheme based on self-
Zhao Chanchan, Liu Feng
doaj +1 more source