Results 201 to 210 of about 168,179 (258)
Wormhole attack detection and mitigation model for Internet of Things and WSN using machine learning. [PDF]
PeerJ Comput SciAlshehri AH.
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Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning. [PDF]
PLoS OneBabbar H, Rani S, Driss M.
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Detection by Attack: Detecting Adversarial Samples by Undercover Attack
2020The 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 +4 more
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Detection of attack strategies
2013 International Conference on Risks and Security of Internet and Systems (CRiSIS), 2013An 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 +2 more
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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 +2 more
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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 +2 more
openaire +2 more sources
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 +2 more
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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 +2 more
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DDoS attack detection and wavelets
Proceedings. 12th International Conference on Computer Communications and Networks (IEEE Cat. No.03EX712), 2004This 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
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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.
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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
Scalable Detection of Cyber Attacks
2011Attackers can exploit vulnerabilities to incrementally penetrate a network and compromise critical systems. The enormous amount of raw security data available to analysts and the complex interdependencies among vulnerabilities make manual analysis extremely labor-intensive and error-prone. To address this important problem, we build on previous work on
Massimiliano Albanese +3 more
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