Results 101 to 110 of about 175,116 (163)

Informer-based DDoS attack detection method for the power Internet of Things. [PDF]

PLoS One
Cui W, Liao X, Yang Y, Feng S, Song M.
europepmc   +1 more source

Modelling of hybrid deep learning framework with recursive feature elimination for distributed denial of service attack detection systems. [PDF]

Sci Rep
Alkhliwi S.
europepmc   +1 more source

IM-ZDD: A Feature-Enhanced Inverse Mapping Framework for Zero-Day Attack Detection in Internet of Vehicles. [PDF]

Sensors (Basel)
Chen T, Zhang G, Xu B.
europepmc   +1 more source

DDoS attack detection in Edge-IIoT digital twin environment using deep learning approach. [PDF]

PeerJ Comput Sci
Al-Obeidat F, Amin A, Shuhaiber A, Haq IU.   +3 more
europepmc   +1 more source

Revolutionary hybrid ensembled deep learning model for accurate and robust side-channel attack detection in cloud computing. [PDF]

Sci Rep
Reddy CL, Malathi K.
europepmc   +1 more source

DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system. [PDF]

Sci Rep
Usha G, Karthikeyan H, Gautam K, Pachauri N.   +3 more
europepmc   +1 more source

Hybrid quantum enhanced federated learning for cyber attack detection. [PDF]

Sci Rep
Subramanian G, Chinnadurai M.
europepmc   +1 more source

PSO-DT based BagDT: a robust lightweight ensemble framework for efficient feature selection and DDoS attack detection in IoT environment. [PDF]

Sci Rep
Shirley JJ, Priya M.
europepmc   +1 more source

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Detection by Attack: Detecting Adversarial Samples by Undercover Attack

2020
The safety of artificial intelligence systems has aroused great concern due to the vulnerability of deep neural networks. Studies show that malicious modifications to the inputs of a network classifier, can fool the classifier and lead to wrong predictions. These modified inputs are called adversarial samples.
Qifei Zhou, Rong Zhang, Bo Wu 0018, Weiping Li, Tong Mo   +4 more
openaire   +1 more source

Statistical attack detection

Proceedings of the third ACM conference on Recommender systems, 2009
It has been shown in recent years that effective profile injection or shilling attacks can be mounted on standard recommendation algorithms. These attacks consist of the insertion of bogus user profiles into the system database in order to manipulate the recommendation output, for example to promote or demote the predicted ratings for a particular ...
Neil Hurley, Zunping Cheng, Mi Zhang 0001   +2 more
openaire   +2 more sources