Results 201 to 210 of about 171,069 (261)

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence. [PDF]

Sci Rep
Anjum M, Dutta AK, Elrashidi A, Shahab S, Aldrees A, Shaikh ZA, Aljohani A.   +6 more
europepmc   +1 more source

Intelligent cybersecurity management in industrial IoT system using attribute reduction with collaborative deep learning enabled false data injection attack detection approach. [PDF]

Sci Rep
Alrashdi I, Duraibi S.
europepmc   +1 more source

Identifying significant features in adversarial attack detection framework using federated learning empowered medical IoT network security. [PDF]

Sci Rep
Sharaf SA, Nooh S.
europepmc   +1 more source

Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms. [PDF]

Sci Rep
Jayanthi S, Bavirthi SS, Murali P, Kumar KV, Alkahtani HK, Ishak MK, Mostafa SM.   +6 more
europepmc   +1 more source

Distributed denial-of-service (DDOS) attack detection using supervised machine learning algorithms. [PDF]

Sci Rep
Abiramasundari S, Ramaswamy V.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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

Detection of attack strategies

2013 International Conference on Risks and Security of Internet and Systems (CRiSIS), 2013
An intrusion and attack detection system usually focuses on classifying a record as either normal or abnormal. In some cases such as insider attacks, attackers rely on feedback from the attacked system, which enables them to gradually manipulate their attempts in order to avoid detection. This paper proposes the notion of accumulative manipulation that
Suliman A. Alsuhibany, Charles Morisset, Aad P. A. van Moorsel   +2 more
openaire   +2 more sources

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

Attack chain detection

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2015
AbstractA targeted network intrusion typically evolves through multiple phases, termed the attack chain. When appropriate data are monitored, these phases will generate multiple events across the attack chain on a compromised host. It is shown empirically that events in different parts of the attack chain are largely independent under nonattack ...
Joseph Sexton, Curtis B. Storlie, Joshua Neil   +2 more
openaire   +1 more source

DDoS attack detection and wavelets

Proceedings. 12th International Conference on Computer Communications and Networks (IEEE Cat. No.03EX712), 2004
This paper presents a systematic method for DDoS attack detection. DDoS attack can be considered a system anomaly or misuse from which abnormal behavior is imposed on network traffic. Attack detection can be performed via abnormal behavior identification.
Lan Li, Gyungho Lee
openaire   +2 more sources