Results 171 to 180 of about 6,155,073 (227)

Detecting DDoS Attack

2021
The major network security problems faced by many internet users is the DDoS (distributed denial of service) attack. This attack makes the service inaccessible by exhausting the network and resources with high repudiation and economic loss. It denies the network services to the potential users.
Megala G., S. Prabu, Liyanapathirana B. C.   +2 more
openaire   +1 more source

Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection

IEEE Transactions on Information Forensics and Security, 2021
Face presentation attack detection (PAD) is essential for securing the widely used face recognition systems. Most of the existing PAD methods do not generalize well to unseen scenarios because labeled training data of the new domain is usually not ...
Guoqing Wang, Hu Han, S. Shan, Xilin Chen   +3 more
semanticscholar   +1 more source

An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment

Applied Soft Computing, 2021
Detection of Denial of Service (DoS) attack is one of the most critical issues in cloud computing. The attack detection framework is very complex due to the nonlinear thought of interruption activities, unusual conduct of systems traffic, and many ...
Reddy Saisindhutheja, Gopal K. Shyam
semanticscholar   +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
openaire   +1 more source

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, Weiping Li, Tong Mo   +4 more
openaire   +1 more source

A novel architecture for web-based attack detection using convolutional neural network

Computers & security, 2021
Unprotected Web applications are vulnerable places for hackers to attack an organization's network. Statistics show that 42% of Web applications are exposed to threats and hackers. Web requests that Web users request from Web applications are manipulated
Adem Tekerek
semanticscholar   +1 more source

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 ...
Sexton, Joseph, Storlie, Curtis, Neil, Joshua   +2 more
openaire   +1 more source

Unified Physical-Digital Face Attack Detection

International Joint Conference on Artificial Intelligence
Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time.
Hao Fang, Ajian Liu, Haocheng Yuan, Junze Zheng, Dingheng Zeng, Yanhong Liu, Jiankang Deng, Sergio Escalera, Xiaoming Liu, Jun Wan, Zhen Lei   +10 more
semanticscholar   +1 more source

Attack Detection in Power Distribution Systems Using a Cyber-Physical Real-Time Reference Model

IEEE Transactions on Smart Grid, 2022
This paper develops a novel intrusion detection system for power distribution systems that utilizes a cyber-physical real-time reference model to accurately replicate the complex behavior of power distribution components for attack detection.
M. Khan, J. Giraldo, M. Parvania
semanticscholar   +1 more source

Deep Learning-Based DDoS-Attack Detection for Cyber–Physical System Over 5G Network

IEEE Transactions on Industrial Informatics, 2021
With the advent of 5G, cyber–physical systems (CPSs) employed in the vertical industries and critical infrastructures will depend on the cellular network more than ever; making their attack surface wider.
Bilal Hussain, Qinghe Du, Bo Sun, Zhiqiang Han   +3 more
semanticscholar   +1 more source