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Adversarial Machine Learning Attacks and Defenses in Network Intrusion Detection Systems
2022
International Journal of Wireless and Microwave Technologies
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In this article, we focus on the evasion attacks against Network Intrusion Detection System (NIDS) and specifically on designing novel adversarial attacks and defenses using adversarial training. ...
We propose white box attacks against intrusion detection systems. Under these attacks, the detection accuracy of model suffered significantly. ...
For the literature survey, search was done using keywords as, "Network Intrusion Detection System", "Machine Learning for Network Intrusion Detection System", "Adversarial Machine Learning", "Defenses ...
doi:10.5815/ijwmt.2022.01.02
fatcat:v76pnse6zjbwxe35wc7lfumhna
Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods
[article]
2019
arXiv
pre-print
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The literature covers different adversarial security attacks and perturbations on ML and DL methods and those have their own presentation styles and merits. ...
, as well as some of the relevant adversarial security attacks and perturbations. ...
An adversary has a complete knowledge of the ML and DL models or systems. • Gray box attack. An adversary has some knowledge of the ML and DL models or systems.• Black box attack. ...
arXiv:1907.07291v1
fatcat:7an2zwnhmveqncl3cpopgcousy
Adversarial Data Mining
2016
Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security - CCS'16
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As more and more cyber security incident data ranging from systems logs to vulnerability scan results are collected, manually analyzing these collected data to detect important cyber security events become ...
Especially, we discuss how some of these data mining techniques could be implemented on recent big data management systems such as Spark. ...
Having access to the enormous amount of personal information on this network is a great incentive for adversaries to attack the smart phone mobile world. ...
doi:10.1145/2976749.2976753
dblp:conf/ccs/KantarciogluX16
fatcat:n2hfosfgqffg5pg5ggroc4ydau
Law and Adversarial Machine Learning
[article]
2018
arXiv
pre-print
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We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine ...
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? ...
On the other hand, images and audio are copyrightable, so, the owner would be more likely to succeed against an adversary that reproduced those. ...
arXiv:1810.10731v3
fatcat:ylgab2xk3zaivgmaixb7afszaa
On learning and recognition of secure patterns
2014
Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop - AISec '14
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In machine learning and pattern recognition systems, we have started investi- ...
Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on pattern classifiers is one of the main open issues in the novel research field of adversarial machine learning ...
attacks. ...
doi:10.1145/2666652.2666653
dblp:conf/ccs/Biggio14
fatcat:24vko7zqbfe23gxtcrhqaxdznq
A dual watermark-fingerprint system
2004
IEEE Multimedia
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A dual-purpose watermarking and fingerprinting system for multimedia screening uses the same secret key to mark all content copies, but different detection keys within each media player. ...
Under optimal attacks, the system's collusion resistance is superlinear in object size. ...
Venkatesan for providing an analysis of the media collusion attack. ...
doi:10.1109/mmul.2004.1
fatcat:u3vd2nrpzbgbtici5om6go64pu
NIMBUS
2014
Proceedings of the 2014 ACM conference on SIGCOMM - SIGCOMM '14
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Our preliminary experiment is to detect flows with significant volume. The results are shown in Figure 1 , where we add 9 Gbps attack burst from 15th second. ...
As more VMs get started, the accuracy gradually recovers and the system throughput also increases to accommodate the attack traffic. In this experiment, The system has scaled-out to 10 VMs. ...
One challenge is a wise adversary may generate attacks in a short burst that are hard to detect from the aggregated traffic statistics in a measurement epoch. ...
doi:10.1145/2619239.2631446
dblp:conf/sigcomm/MiaoYJ14
fatcat:i3dxnmrwgvdp3e4b3phnnox4nu
NIMBUS
2014
Computer communication review
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Our preliminary experiment is to detect flows with significant volume. The results are shown in Figure 1 , where we add 9 Gbps attack burst from 15th second. ...
As more VMs get started, the accuracy gradually recovers and the system throughput also increases to accommodate the attack traffic. In this experiment, The system has scaled-out to 10 VMs. ...
One challenge is a wise adversary may generate attacks in a short burst that are hard to detect from the aggregated traffic statistics in a measurement epoch. ...
doi:10.1145/2740070.2631446
fatcat:rwz74m257fhbjbjbtxoaap3b4u
Machine Learning Methods for Computer Security (Dagstuhl Perspectives Workshop 12371)
2013
Dagstuhl Reports
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Examples of such applications are social media spam, plagiarism detection, authorship identification, copyright enforcement, computer vision (particularly in the context of biometrics), and sentiment analysis ...
The second group focused on the current approaches and methodical challenges for learning in security-sensitive adversarial domains. ...
Practical methods for detection of copyright infringement in media are mainly based on content fingerprinting. ...
doi:10.4230/dagrep.2.9.109
dblp:journals/dagstuhl-reports/JosephLRTN12
fatcat:4x3ng2szxfg5jnkf5rtwsmttrm
Using Deceptive Information in Computer Security Defenses
2014
International Journal of Cyber Warfare and Terrorism
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They show that by intelligently introducing deceit in information systems, the authors not only lead attackers astray, but also give organizations the ability to detect leakage; create doubt and uncertainty ...
in leaked data; add risk at the adversaries' side to using the leaked information; and significantly enhance our abilities to attribute adversaries. ...
These techniques are designed to lead attackers astray and augment our systems with decoys to detect stealthy adversaries. ...
doi:10.4018/ijcwt.2014070105
fatcat:7bw3f3xrf5gd7fqmezl7cviaju
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification
2020
Interspeech 2020
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Recently adversarial attacks on automatic speaker verification (ASV) systems attracted widespread attention as they pose severe threats to ASV systems. ...
Orthogonal to prior approaches, this work proposes to defend ASV systems against adversarial attacks with a separate detection network, rather than augmenting adversarial data into ASV training. ...
Adversarial attack performance The attack results on the x-vector system are shown in Table 2 . The results on the i-vector and r-vector systems have similar trends. ...
doi:10.21437/interspeech.2020-2441
dblp:conf/interspeech/LiLZWLSYM20
fatcat:ptifok6oc5hrvfcx4c7rhvyv2e
Universal Adversarial Attacks on Spoken Language Assessment Systems
2020
Interspeech 2020
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Four approaches to detect such adversarial attacks are also described. ...
In this paper the sensitivity of SLA systems to a universal black-box attack on the ASR text output is explored. ...
adversarial attacks, and how these attacks can be detected. ...
doi:10.21437/interspeech.2020-1890
dblp:conf/interspeech/RainaGK20
fatcat:mdepwovcfzcaddhulv3yptjtji
A new metric to compare anomaly detection algorithms in cyber-physical systems
2019
Proceedings of the 6th Annual Symposium on Hot Topics in the Science of Security - HotSoS '19
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However, to obtain the TPR it is necessary to generate attacks that will be detected, which is useless to evaluate detection strategies against more realistic adversaries that can adapt their attacks to ...
In this poster, we present a novel metric that is based on the maximum impact an adversary can cause while remaining stealthy, and on the expected time between false alarms. ...
INTRODUCTION One of the differences between detecting attacks in control systems when compared to detecting attacks in general IT systems is that researchers do not have readily available data from attacks ...
doi:10.1145/3314058.3318166
dblp:conf/hotsos/GiraldoC19
fatcat:ahpq3ali55dz7gl6rui67wflgy
Multimedia content screening using a dual watermarking and fingerprinting system
2002
Proceedings of the tenth ACM international conference on Multimedia - MULTIMEDIA '02
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Our dual system limits the scope of possible attacks, when compared to classic fingerprinting systems. ...
By knowing a detection key, an adversary cannot recreate the original content from the watermarked content. ...
We assume that the WM system is robust against signal-processing attacks on the protected object and focus on collusion attacks against the detection keys. ...
doi:10.1145/641083.641086
fatcat:g55of2n7vrazpfti4f2yh7gepm
Multimedia content screening using a dual watermarking and fingerprinting system
2002
Proceedings of the tenth ACM international conference on Multimedia - MULTIMEDIA '02
Preserved Fulltext
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Our dual system limits the scope of possible attacks, when compared to classic fingerprinting systems. ...
By knowing a detection key, an adversary cannot recreate the original content from the watermarked content. ...
We assume that the WM system is robust against signal-processing attacks on the protected object and focus on collusion attacks against the detection keys. ...
doi:10.1145/641007.641086
dblp:conf/mm/KirovskiMY02
fatcat:usinxqpsnjdafpkm6hiy4pex2a
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