Relevant bibliographies by topics / Evasion attack

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Evasion attack'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Evasion attack"

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Alshahrani, Ebtihaj, Daniyal Alghazzawi, Reem Alotaibi, and Osama Rabie. "Adversarial attacks against supervised machine learning based network intrusion detection systems." PLOS ONE 17, no. 10 (2022): e0275971. http://dx.doi.org/10.1371/journal.pone.0275971.

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Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this research, we performed two adversarial attack scenarios, we used a Generative Adversarial Network (GAN) to generate synthetic intrusion traffic to test the influence of these attacks on the accuracy of machine learning-based Intrusion Detection Systems(IDSs). We conducted two experiments on adversarial a
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Mrs.Ashvinee, N.Kharat* Prof.Mr.(Dr.).B.D.Phulpagar. "SURVEY ON METHODS FOR EVASION ATTACK DETECTION." INTERNATIONAL JOURNALOFRESEARCH SCIENCE& MANAGEMENT 4, no. 7 (2017): 54–60. https://doi.org/10.5281/zenodo.831440.

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Network security has become more necessary to personal computer users, organizations, and the military. With the advent of the internet, security became a major concern and the history of security sanctions a better understanding of the emergence of security technology. Network attacks had become a curse to technology, where attacker destroy or gain illegal access to system recourses and restrict the legitimate users from accessing the information. In this paper, we are going study different type of network security attack and learn counter measures for that attack. We are also going to study
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Brohi, Sarfraz, and Qurat-ul-ain Mastoi. "AI Under Attack: Metric-Driven Analysis of Cybersecurity Threats in Deep Learning Models for Healthcare Applications." Algorithms 18, no. 3 (2025): 157. https://doi.org/10.3390/a18030157.

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Incorporating Artificial Intelligence (AI) in healthcare has transformed disease diagnosis and treatment by offering unprecedented benefits. However, it has also revealed critical cybersecurity vulnerabilities in Deep Learning (DL) models, which raise significant risks to patient safety and their trust in AI-driven applications. Existing studies primarily focus on theoretical vulnerabilities or specific attack types, leaving a gap in understanding the practical implications of multiple attack scenarios on healthcare AI. In this paper, we provide a comprehensive analysis of key attack vectors,
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Nirmal, Santosh, and Pramod Patil. "Deceptive Maneuvers: Subverting CNN-AdaBoost Model for Energy Theft Detection." Electronics ETF 28, no. 2 (2024): 46–53. https://doi.org/10.53314/els2428046n.

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As deep learning models become more prevalent in smart grid systems, ensuring their accuracy in tasks like identifying abnormal customer behavior is increasingly important. As its use is increased in smart grids to detect energy theft, crafting adversarial data by attackers to deceive the model to get the desired output is also increased. Evasion attacks (EA) attempt to evade detection by misclassifying input data during testing. The manipulation of data inputs is done so that it is not noticeable to humans but can cause the machine learning (ML) model to produce incorrect results. Electricity
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Wang, Qingya, Yi Wu, Haojun Xuan, and Huishu Wu. "FLARE: A Backdoor Attack to Federated Learning with Refined Evasion." Mathematics 12, no. 23 (2024): 3751. http://dx.doi.org/10.3390/math12233751.

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Federated Learning (FL) is vulnerable to backdoor attacks in which attackers inject malicious behaviors into the global model. To counter these attacks, existing works mainly introduce sophisticated defenses by analyzing model parameters and utilizing robust aggregation strategies. However, we find that FL systems can still be attacked by exploiting their inherent complexity. In this paper, we propose a novel three-stage backdoor attack strategy named FLARE: A Backdoor Attack to Federated Learning with Refined Evasion, which is designed to operate under the radar of conventional defense strate
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Speth, Cornelia, and Günter Rambach. "Complement Attack againstAspergillusand Corresponding Evasion Mechanisms." Interdisciplinary Perspectives on Infectious Diseases 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/463794.

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Invasive aspergillosis shows a high mortality rate particularly in immunocompromised patients. Perpetually increasing numbers of affected patients highlight the importance of a clearer understanding of interactions between innate immunity and fungi. Innate immunity is considered to be the most significant host defence against invasive fungal infections. Complement represents a crucial part of this first line defence and comprises direct effects against invading pathogens as well as bridging functions to other parts of the immune network. However, despite the potency of complement to attack for
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Chen, Hongyi, Jinshu Su, Linbo Qiao, and Qin Xin. "Malware Collusion Attack against SVM: Issues and Countermeasures." Applied Sciences 8, no. 10 (2018): 1718. http://dx.doi.org/10.3390/app8101718.

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Android has become the most popular mobile platform, and a hot target for malware developers. At the same time, researchers have come up with numerous ways to deal with malware. Among them, machine learning based methods are quite effective in Android malware detection, the accuracy of which can be as high as 98%. Thus, malware developers have the incentives to develop more advanced malware to evade detection. This paper presents an adversary attack scenario (Collusion Attack) that will compromise current machine learning based malware detection methods, especially Support Vector Machines (SVM
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Evdokimenkov, Veniamin N., Dmitriy A. Kozorez, and Lev N. Rabinskiy. "Unmanned aerial vehicle evasion manoeuvres from enemy aircraft attack." Journal of the Mechanical Behavior of Materials 30, no. 1 (2021): 87–94. http://dx.doi.org/10.1515/jmbm-2021-0009.

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Abstract One of the most important problems associated with the combat use of unmanned aerial vehicles remains to ensure their high survivability in conditions of deliberate countermeasures, the source of which can be both ground-based air defence systems and fighter aircraft. For this reason, the study and optimization of evasive manoeuvres of an unmanned aerial vehicle from an enemy aircraft attack remains relevant. Based on the game approach, the authors of this paper propose an algorithm for guaranteeing control of the trajectory of an unmanned aerial vehicle, which ensures its evasion fro
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Sheikh, Zakir Ahmad, Yashwant Singh, Pradeep Kumar Singh, and Paulo J. Sequeira Gonçalves. "Defending the Defender: Adversarial Learning Based Defending Strategy for Learning Based Security Methods in Cyber-Physical Systems (CPS)." Sensors 23, no. 12 (2023): 5459. http://dx.doi.org/10.3390/s23125459.

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Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the other hand, rise in complexities, aiming for more powerful attacks and evasion from detections. The real-world applicability of CPS thus poses a question mark due to security infringements. Researchers have been developing new and robust techniques to enhance the security of these systems. Many techniques and security aspects are being considered to b
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Alzaidy, Sharoug, and Hamad Binsalleeh. "Adversarial Attacks with Defense Mechanisms on Convolutional Neural Networks and Recurrent Neural Networks for Malware Classification." Applied Sciences 14, no. 4 (2024): 1673. http://dx.doi.org/10.3390/app14041673.

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In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inputs, resulting in malicious files being misclassified. Cyber-Physical Systems (CPS) may be compromised by malicious files, which can have catastrophic consequences. This paper presents a method for classifying Windows portable executables (PEs) using Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To generate malware exec
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Dissertations / Theses on the topic "Evasion attack"

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Tajdini, M. "Developing an advanced IPv6 evasion attack detection framework." Thesis, Liverpool John Moores University, 2018. http://researchonline.ljmu.ac.uk/9864/.

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Internet Protocol Version 6 (IPv6) is the most recent generation of Internet protocol. The transition from the current Internet Version 4 (IPv4) to IPv6 raised new issues and the most crucial issue is security vulnerabilities. Most vulnerabilities are common between IPv4 and IPv6, e.g. Evasion attack, Distributed Denial of Service (DDOS) and Fragmentation attack. According to the IPv6 RFC (Request for Comment) recommendations, there are potential attacks against various Operating Systems. Discrepancies between the behaviour of several Operating Systems can lead to Intrusion Detection System (I
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Lejon, Sara. "Evasion and Attack: Structural Studies of a Bacterial Albumin-binding Protein and of a Cephalosporin Biosynthetic Enzyme." Doctoral thesis, Uppsala : University Library Universitetsbiblioteket, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8399.

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RUSSU, PAOLO. "Secure machine learning against evasion and poisoning attacks." Doctoral thesis, Università degli Studi di Cagliari, 2017. http://hdl.handle.net/11584/249561.

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In the last decades, machine learning has been widely used in security applications like spam filtering, intrusion detection in computer networks and biometric identity recognition. The adoption of such techniques has been mainly due to their high generalization capability, which allows one to identify also new kinds of attacks. However, in these applications, machine learning has to deal with intelligent and adaptive adversaries that aim to subvert its proper functioning to achieve their malicious scope. Since machine learning has not been designed to take into account the presence of atta
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DEMETRIO, LUCA. "Formalizing evasion attacks against machine learning security detectors." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1035018.

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Recent work has shown that adversarial examples can bypass machine learning-based threat detectors relying on static analysis by applying minimal perturbations. To preserve malicious functionality, previous attacks either apply trivial manipulations (e.g. padding), potentially limiting their effectiveness, or require running computationally-demanding validation steps to discard adversarial variants that do not correctly execute in sandbox environments. While machine learning systems for detecting SQL injections have been proposed in the literature, no attacks have been tested against the pro
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Delvecchio, Matthew David. "Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99792.

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Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods so that they no longer require expertly-defined features. Simultaneously, cybersecurity and electronic warfare are growing areas of focus and concern in an increasingly technology-driven world. Privacy and confidentiality of communication links are both more important and more difficult than ever in
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Maaz, Khan. "EVASIVE INTERNET PROTOCOL: END TO END PERFORMANCE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1308179859.

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Peddisetty, Naga Raju. "State-of-the-art Intrusion Detection: Technology, Challenges, and Evaluation." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2792.

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<p>Due to the invention of automated hacking tools, Hacking is not a black art anymore. Even script kiddies can launch attacks in few seconds. Therefore, there is a great emphasize on the Security to protect the resources from camouflage. Intrusion Detection System is also one weapon in the security arsenal. It is the process of monitoring and analyzing information sources in order to detect vicious traffic. With its unique capabilities like monitoring, analyzing, detecting and archiving, IDS assists the organizations to combat against threats, to have a snap-shot of the networks, and to condu
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Sultana, Saddiqa. "Transformation of brain drain of brain gain: incentives to attract talented workforce." reponame:Repositório Institucional do FGV, 2014. http://hdl.handle.net/10438/13396.

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Submitted by Saddiqa Sultana (saddiqa.sultana2015@fgvmail.br) on 2015-02-19T19:09:39Z No. of bitstreams: 1 Saddiqa TCC.pdf: 1436295 bytes, checksum: fc73fd71b12430c002a755814c4fa7fc (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-02-20T16:27:22Z (GMT) No. of bitstreams: 1 Saddiqa TCC.pdf: 1436295 bytes, checksum: fc73fd71b12430c002a755814c4fa7fc (MD5)<br>Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-02-24T12:07:02Z (GMT) No. of bitstreams: 1 Saddiqa TCC.pdf: 1436295 bytes, checksum: fc73fd71b12430c002a7
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Darwaish, Asim. "Adversary-aware machine learning models for malware detection systems." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7283.

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La popularisation des smartphones et leur caractère indispensable les rendent aujourd'hui indéniables. Leur croissance exponentielle est également à l'origine de l'apparition de nombreux logiciels malveillants et fait trembler le prospère écosystème mobile. Parmi tous les systèmes d'exploitation des smartphones, Android est le plus ciblé par les auteurs de logiciels malveillants en raison de sa popularité, de sa disponibilité en tant que logiciel libre, et de sa capacité intrinsèque à accéder aux ressources internes. Les approches basées sur l'apprentissage automatique ont été déployées avec s
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Su, Wen-De, and 蘇文德. "The Model of Evasion Attack Detection using Finite State Machine." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/23721610102307490411.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>91<br>With the electronic commerce is going popular, many enterprise turn their business to the internet. The electronic commerce brings many problems on security and the key point is the data of the enterprise and the privacy of the customer. Firewall is not enough and the IDS is needed to provide the acceptable security. With the network monitoring and intrusion detection techniques, we can detect the attacks, alarm the administrators, and write to log files. The log files can be analyzed to provide the prevention to the same types of attacks and protect the secu
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Books on the topic "Evasion attack"

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Office, General Accounting. Financial management: Status of the governmentwide efforts to address improper payment problems : report to the Subcommittee on Government Efficiency and Financial Management, Committee on Government Reform, House of Representatives. GAO, 2003.

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Office, General Accounting. Financial management: Survey of capitalization threshold and other policies for property, plant, and equipment : report to Agency officials. U.S. General Accounting Office, 2002.

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Office, General Accounting. Financial management: Profile of Army financial managers : report to the Assistant Secretary of the Army (Financial Management and Comptroller). The Office, 1998.

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Office, General Accounting. Financial management: Differences in Army and Air Force disbursing and accounting records : report to the Honorable Charles E. Grassley, U.S. Senate. The Office, 2000.

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Office, General Accounting. Financial management: Recommendations on Indian trust fund Strategic Plan proposals : report to the Secretary of the Interior. The Office, 1997.

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Office, General Accounting. Financial management: Coordinated approach needed to address the government's improper payments problems : report to the Ranking Minority Member, Committee on Governmental Affairs, U.S. Senate. United States General Accounting Office, 2002.

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Office, General Accounting. Financial management: Profile of Navy and Marine Corps financial managers : report to the Acting Assistant Secretary of the Navy (Financial Management and Comptroller). The Office, 1998.

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Office, General Accounting. Financial management: Problems in accounting for Navy transactions impair funds control and financial reporting : report to agency officials. The Office, 1999.

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Office, General Accounting. Financial management: An overview of finance and accounting activities in DOD : report to the Chairman, Subcommittee on Defense, Committee on Appropriations, U.S. Senate. The Office, 1997.

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Office, General Accounting. Financial management: Some DOD contractors abuse the federal tax system with little consequence : report to congressional requesters. U.S. General Accounting Office, 2004.

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Book chapters on the topic "Evasion attack"

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Lucas, Keane, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, and Saurabh Shintre. "Deceiving ML-Based Friend-or-Foe Identification for Executables." In Advances in Information Security. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16613-6_10.

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AbstractDeceiving an adversary who may, e.g., attempt to reconnoiter a system before launching an attack, typically involves changing the system’s behavior such that it deceives the attacker while still permitting the system to perform its intended function. We develop techniques to achieve such deception by studying a proxy problem: malware detection.Researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization fram
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Xiao, Han, Thomas Stibor, and Claudia Eckert. "Evasion Attack of Multi-class Linear Classifiers." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_18.

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Fritsch, Lothar, Aws Jaber, and Anis Yazidi. "An Overview of Artificial Intelligence Used in Malware." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17030-0_4.

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AbstractArtificial intelligence (AI) and machine learning (ML) methods are increasingly adopted in cyberattacks. AI supports the establishment of covert channels, as well as the obfuscation of malware. Additionally, AI results in new forms of phishing attacks and enables hard-to-detect cyber-physical sabotage. Malware creators increasingly deploy AI and ML methods to improve their attack’s capabilities. Defenders must therefore expect unconventional malware with new, sophisticated and changing features and functions. AI’s potential for automation of complex tasks serves as a challenge in the f
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Lakshmi, Sripada Manasa, Manvjeet Kaur, Awadhesh Kumar Shukla, and Nahita Pathania. "Evasion Attack for Fingerprint Biometric System and Countermeasure." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0324-5_7.

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Vitorino, João, Eva Maia, and Isabel Praça. "Adversarial Evasion Attack Efficiency Against Large Language Models." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76459-2_2.

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Maurya, Vikas, Rachit Agarwal, and Sandeep Shukla. "Evasion Attack Against Multivariate Singular Spectrum Analysis Based IDS." In Critical Information Infrastructures Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62139-0_8.

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Guarnera, Luca, Francesco Guarnera, Alessandro Ortis, Sebastiano Battiato, and Giovanni Puglisi. "Evasion Attack on Deepfake Detection via DCT Trace Manipulation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88223-4_12.

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Kwon, Hyun, Hyunsoo Yoon, and Daeseon Choi. "Friend-Safe Adversarial Examples in an Evasion Attack on a Deep Neural Network." In Information Security and Cryptology – ICISC 2017. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78556-1_20.

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Komeylian, Somayeh, Christopher Paolini, and Mahasweta Sarkar. "Overcoming an Evasion Attack on a CNN Model in the MIMO-OFDM Wireless Communication Channel." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1203-2_7.

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Lurski, Nicholas, and Mohamed Younis. "Application and Mitigation of the Evasion Attack against a Deep Learning Based IDS for IoT." In Machine Learning for Networking. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98978-1_6.

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Conference papers on the topic "Evasion attack"

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E R R, Arjun, Pavan Kulkarni, Yuvaraj Govindarajulu, Harshit Shah, and Manojkumar Parmar. "Evasion Attacks on Object Detection Models using Attack Transferability." In 2024 IEEE Secure Development Conference (SecDev). IEEE, 2024. http://dx.doi.org/10.1109/secdev61143.2024.00009.

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Chiaro, Diletta, Pian Qi, Edoardo Prezioso, Antonella Guzzo, and Francesco Piccialli. "FLAME: Federated Learning for Attack Mitigation and Evasion." In 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2025. https://doi.org/10.1109/ipdps64566.2025.00060.

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Nirmal, Santosh, and Pramod Patil. "Evasion Attack against CNN-Adaboost Electricity Theft Detection in Smart Grid." In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA). IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10829076.

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Kadri, Jawad, Alex Lott, Brendan Mailloux, et al. "Evasion Adversarial Attacks Perturbations in Network Security." In 2024 IEEE 10th World Forum on Internet of Things (WF-IoT). IEEE, 2024. https://doi.org/10.1109/wf-iot62078.2024.10811295.

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Permpoon, Nayotsakorn, Titichaya Thanamitsomboon, and Kanticha Kittipeerachon. "Analysis of Classification Models of Firewall Log Data Attacked by Data Poisoning and Evasion Attacks." In 2024 9th International Conference on Business and Industrial Research (ICBIR). IEEE, 2024. https://doi.org/10.1109/icbir61386.2024.10875909.

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Aryal, Kshitiz, Maanak Gupta, Mahmoud Abdelsalam, and Moustafa Saleh. "Explainability Guided Adversarial Evasion Attacks on Malware Detectors." In 2024 33rd International Conference on Computer Communications and Networks (ICCCN). IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637577.

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Koball, Carson, and Yong Wang. "Evasion Attacks on Tree-Based Machine Learning Models." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10929896.

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Jung, Kuchul, Jongseok Woo, and Saibal Mukhopadhyay. "A Hybrid Approach to Defend Against Adversarial Evasion Attacks." In 2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2024. http://dx.doi.org/10.1109/mwscas60917.2024.10658742.

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Krzysztoń, Mateusz, Bartosz Bok, Paweł Żakieta, and Joanna Kołodziej. "Evasion attacks on ML in domains with nonlinear constraints." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). IEEE, 2024. http://dx.doi.org/10.1109/ccgridw63211.2024.00020.

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Yudin, Matthew, Yingbo Song, Constantin Serban, and Ritu Chadha. "Exposing Vulnerabilities in PDF Malware Detectors to Evasion Attacks." In MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM). IEEE, 2024. https://doi.org/10.1109/milcom61039.2024.10773945.

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Reports on the topic "Evasion attack"

1

Farrell, Matthew. Evasion Attacks. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430776.

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