Results 261 to 270 of about 193,280 (308)

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning. [PDF]

PLoS One
Babbar H, Rani S, Driss M.
europepmc   +1 more source

Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach.

Sci Rep
Almadhor A, Altalbe A, Bouazzi I, Hejaili AA, Kryvinska N.   +4 more
europepmc   +1 more source

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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   +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 0018, Weiping Li, Tong Mo   +4 more
openaire   +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 ...
Joseph Sexton, Curtis B. Storlie, Joshua Neil   +2 more
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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   +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   +1 more source

Detecting attacks on networks

Computer, 1997
As Internet based and intranet based network systems have evolved, they have become invaluable tools that businesses can use to share information and conduct business with online partners. However, hackers have also learned to use these systems to access private networks and their resources.
openaire   +1 more source

Lightweight Detection of DoS Attacks

2007 15th IEEE International Conference on Networks, 2007
Denial of service (DoS) attacks have continued to evolve and impact availability of the Internet infrastructure. Many researchers in the field of network security and system survivability have been developing mechanisms to detect DoS attacks. By doing so they hope to maximize accurate detections (true-positive) and minimize non-justified detections ...
Sirikarn Pukkawanna, Vasaka Visoottiviseth, Panita Pongpaibool   +2 more
openaire   +1 more source