Relevant bibliographies by topics / Detecting backdoor trojans

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Detecting backdoor trojans'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Detecting backdoor trojans"

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Tatomur, Irina. "UNIVERSITY CYBER SECURITY AS A METHOD FOR ANTI-FISHING FRAUD." Economic discourse, no. 1 (March 2020): 59–67. http://dx.doi.org/10.36742/2410-0919-2020-1-7.

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Introduction. With the rapid adoption of computer and networking technologies, educational institutions pay insufficient attention to the implementation of security measures to ensure the confidentiality, integrity and accessibility of data, and thus fall prey to cyber-attacks. Methods. The following methods were used in the process of writing the article: methods of generalization, analogy and logical analysis to determine and structure the motives for phishing attacks, ways to detect and prevent them; statistical analysis of data – to build a chronological sample of the world's largest cyber
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Gao, Hui, Yunfang Chen, and Wei Zhang. "Detection of Trojaning Attack on Neural Networks via Cost of Sample Classification." Security and Communication Networks 2019 (November 29, 2019): 1–12. http://dx.doi.org/10.1155/2019/1953839.

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To overcome huge resource consumption of neural networks training, MLaaS (Machine Learning as a Service) has become an irresistible trend, just like SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service) have been. But it comes with some security issues of untrustworthy third-party services. Especially machine learning providers may deploy trojan backdoors in provided models for the pursuit of extra profit or other illegal purposes. Against the redundant nodes-based trojaning attack on neural networks, we proposed a novel detecting method, which only
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Schofield, Matthew, Gulsum Alicioglu, Bo Sun, et al. "Comparison of Malware Classification Methods using Convolutional Neural Network based on API Call Stream." International Journal of Network Security & Its Applications 13, no. 2 (2021): 1–19. http://dx.doi.org/10.5121/ijnsa.2021.13201.

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Malicious software is constantly being developed and improved, so detection and classification of malwareis an ever-evolving problem. Since traditional malware detection techniques fail to detect new/unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the API (Application Program Interface) calls. This research uses a database of 7107 instances of API call streams and 8 different malware types:Adware, Backdoor, Downloader, Dropper, Spyware, Trojan, Virus,Worm. We used
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Catak, Ferhat Ozgur, Ahmet Faruk Yazı, Ogerta Elezaj, and Javed Ahmed. "Deep learning based Sequential model for malware analysis using Windows exe API Calls." PeerJ Computer Science 6 (July 27, 2020): e285. http://dx.doi.org/10.7717/peerj-cs.285.

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Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection. This study is focused on metamorphic malware, which is the most advanced member of the malware family. It is quite impossible for anti-virus applications using traditional signature-based methods to detect metamorphic malware, which makes it difficult to classify this type of malware accordingly. Recent research literature about malware detection and classification discusses this issue related
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Kulkarni, Ameya, and Chengying Xu. "A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains." Frontiers in Mechanical Engineering 7 (July 27, 2021). http://dx.doi.org/10.3389/fmech.2021.709924.

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Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase of printed circuit boards to secure electronic supply chains. Trojans can serve as backdoors of accessing on chip data, can potentially alter functioning and in some cases may even deny intended service of the chip. Apart from consumer electronics, printed circuit boards are used in mission critical applications like military and space equipmen
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"Detection of Malware attacks in smart phones using Machine Learning." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (2019): 4396–400. http://dx.doi.org/10.35940/ijitee.a5082.119119.

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In recent years, security has become progressively vital in mobile devices. The biggest security problems in android devices are malware attack which has been exposed to different threats. The volume of new applications by the production of mobile devices and their related app-stores is too big to manually examine the each and every application for malicious behavior. Installing applications which may leads to security vulnerabilities on the smart phones request access to sensitive information. There are various malwares can attack android device namely virus, worms, Botnet, Trojans, Backdoor
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Dissertations / Theses on the topic "Detecting backdoor trojans"

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Caravut, Sinchai. "MULTIPLE LOGS ANALYSIS FOR DETECTING ZERO-DAY BACKDOOR TROJANS." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1210831685.

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(9034049), Miguel Villarreal-Vasquez. "Anomaly Detection and Security Deep Learning Methods Under Adversarial Situation." Thesis, 2020.

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<p>Advances in Artificial Intelligence (AI), or more precisely on Neural Networks (NNs), and fast processing technologies (e.g. Graphic Processing Units or GPUs) in recent years have positioned NNs as one of the main machine learning algorithms used to solved a diversity of problems in both academia and the industry. While they have been proved to be effective in solving many tasks, the lack of security guarantees and understanding of their internal processing disrupts their wide adoption in general and cybersecurity-related applications. In this dissertation, we present the findings of a comp
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Conference papers on the topic "Detecting backdoor trojans"

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Zhou, Xinzhe, Wenhao Jiang, Sheng Qi, and Yadong Mu. "Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/477.

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Visual backdoor attack is a recently-emerging task which aims to implant trojans in a deep neural model. A trojaned model responds to a trojan-invoking trigger in a fully predictable manner while functioning normally otherwise. As a key motivating fact to this work, most triggers adopted in existing methods, such as a learned patterned block that overlays a benigh image, can be easily noticed by human. In this work, we take image recognition and detection as the demonstration tasks, building trojaned networks that are significantly less human-perceptible and can simultaneously attack multiple
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2

Chen, Huili, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar. "DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/647.

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Deep Neural Networks (DNNs) are vulnerable to Neural Trojan (NT) attacks where the adversary injects malicious behaviors during DNN training. This type of ‘backdoor’ attack is activated when the input is stamped with the trigger pattern specified by the attacker, resulting in an incorrect prediction of the model. Due to the wide application of DNNs in various critical fields, it is indispensable to inspect whether the pre-trained DNN has been trojaned before employing a model. Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deploymen
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3

Meade, Travis, Shaojie Zhang, Yier Jin, Zheng Zhao, and David Pan. "Gate-Level Netlist Reverse Engineering Tool Set for Functionality Recovery and Malicious Logic Detection." In ISTFA 2016. ASM International, 2016. http://dx.doi.org/10.31399/asm.cp.istfa2016p0342.

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Abstract Reliance on third-party resources, including thirdparty IP cores and fabrication foundries, as well as wide usage of commercial-off-the-shelf (COTS) components has raised concerns that backdoors and/or hardware Trojans may be inserted into fabricated chips. Defending against hardware backdoors and/or Trojans has primarily focused on detection at various stages in the supply chain. Netlist reverse engineering tools have been investigated as an alternative to existing chip-level reverse engineering methods which can help recover functional netlists from fabricated chips, but fall short
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