Relevant bibliographies by topics / Portable Executable

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Portable Executable'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Portable Executable"

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Li, Yang, Ye Liang, and Jing Zhang Liang. "Applied-Information Technology with Trojan Horse Detection Method Based on C5.0 Decision Tree." Applied Mechanics and Materials 540 (April 2014): 439–42. http://dx.doi.org/10.4028/www.scientific.net/amm.540.439.

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This paper discusses the Trojan horse detection methods by analysis on Portable Executable File Format through which we can get much useful information. In order to deal with the information extracted from Portable Executable file, our methods constructed a decision tree based on C5.0 decision tree algorithm. Our approach can be divided into two steps. Firstly, we extracted some features from Portable Executable file by a portable executable attribute filter. Secondly, we handled the features extracted and then construct a classifier to identify the Trojan horse. The original in this paper is
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Dendere, Tanatswa Ruramai, and Avinash Singh. "Ransomware Detection Using Portable Executable Imports." International Conference on Cyber Warfare and Security 19, no. 1 (2024): 66–74. http://dx.doi.org/10.34190/iccws.19.1.2031.

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In recent years, there has been a substantial surge in ransomware attacks, wreaking havoc on both organizations and individuals. These attacks, driven by the lure of profits, particularly with the widespread use of cryptocurrencies, have prompted attackers to continuously develop innovative evasion techniques and obfuscation tactics to avoid detection. Ransomware, employing seemingly benign functions such as encryption and file-locking, poses a formidable challenge for detection as it evolves beyond traditional signature-based methods. Consequently, there is a growing need to identify previous
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Gritzalis, Stefanos, George Aggelis, and Diomidis Spinellis. "Architectures for secure portable executable content." Internet Research 9, no. 1 (1999): 16–24. http://dx.doi.org/10.1108/10662249910251273.

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Tuan, Nguyen Kim, Nguyen Hoang Ha, and Tran Truong Thien Nguyen. "DETECTING MALWARE IN PORTABLE EXECUTABLE FILES USING MACHINE LEARNING APPROACH." International Journal of Network Security & Its Applications (IJNSA) 14, no. 3 (2022): 11–17. https://doi.org/10.5281/zenodo.6629970.

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There have been many solutions proposed to increase the ability to detection of malware in executable files in general and in Portable Executable files in particular. In this paper, we rely on the PE header structure of Portable Executablefiles to propose another approach in using Machine learning to classify these files, as malware files or benign files. Experimental results show that the proposed approach still uses the Random Forest algorithm for the classification problem but the accuracy and execution time are improved compared to some recent publications (accuracy reaches 99.71%).  
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Ahmed, Eman, Amin A. Sorrour, Mohamed A. Sobh, and Ayman M. Bahaa-Eldin. "A Cloud-based Malware Detection Framework." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 2 (2017): 113. http://dx.doi.org/10.3991/ijim.v11i2.6577.

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<p class="Els-Abstract-text">Malwares are increasing rapidly. The nature of distribution and effects of malwares attacking several applications requires a real-time response. Therefore, a high performance detection platform is required. In this paper, Hadoop is utilized to perform static binary search and detection for malwares and viruses in portable executable files deployed mainly on the cloud. The paper presents an approach used to map the portable executable files to Hadoop compatible files. The Boyer–Moore-Horspool Search algorithm is modified to benefit from the distribution of Ha
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Alqahtani, Ali, Sumayya Azzony, Leen Alsharafi, and Maha Alaseri. "Web-Based Malware Detection System Using Convolutional Neural Network." Digital 3, no. 3 (2023): 273–85. http://dx.doi.org/10.3390/digital3030017.

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In this article, we introduce a web-based malware detection system that leverages a deep-learning approach. Our primary objective is the development of a robust deep-learning model designed for classifying malware in executable files. In contrast to conventional malware detection systems, our approach relies on static detection techniques to unveil the true nature of files as either malicious or benign. Our method makes use of a one-dimensional convolutional neural network 1D-CNN due to the nature of the portable executable file. Significantly, static analysis aligns perfectly with our objecti
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Dieta Wahyu Asry, Eko Siswanto, Dendy Kurniawan, and Haris Ihsanil Huda. "Deteksi Malware Statis Menggunakan Deep Neural Networks Pada Portable Executable." Teknik: Jurnal Ilmu Teknik dan Informatika 3, no. 1 (2023): 19–34. http://dx.doi.org/10.51903/teknik.v3i1.325.

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Latar Belakang: Dua komponen utama pada analisismalware adalah analisis malware statis yang melibatkan pemeriksaan struktur dasar malware yang dapat dieksekusi tanpa mengeksekusinya sedangkan analisis malware dinamis bergantung pada pemeriksaan perilaku malware setelah menjalankannya di lingkungan yang terkendali. Analisis malware statis biasanya dilakukan oleh perangkat lunak anti-malware modern dengan menggunakan analisis berbasis tanda tangan atau analisis berbasis heuristik. Tujuan Utama: Tujuan dari penelitian ini adalah megusulkan dan mengevaluasi deep neural network untuk menganalisis f
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Zhang, Shuhui, Changdong Hu, Lianhai Wang, Miodrag J. Mihaljevic, Shujiang Xu, and Tian Lan. "A Malware Detection Approach Based on Deep Learning and Memory Forensics." Symmetry 15, no. 3 (2023): 758. http://dx.doi.org/10.3390/sym15030758.

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As cyber attacks grow more complex and sophisticated, new types of malware become more dangerous and challenging to detect. In particular, fileless malware injects malicious code into the physical memory directly without leaving attack traces on disk files. This type of attack is well concealed, and it is difficult to find the malicious code in the static files. For malicious processes in memory, signature-based detection methods are becoming increasingly ineffective. Facing these challenges, this paper proposes a malware detection approach based on convolutional neural network and memory fore
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Tayyab, Umm-e.-Hani, Faiza Babar Khan, Asifullah Khan, Muhammad Hanif Durad, Farrukh Aslam Khan, and Aftab Ali. "ISAnWin: inductive generalized zero-shot learning using deep CNN for malware detection across windows and android platforms." PeerJ Computer Science 10 (December 23, 2024): e2604. https://doi.org/10.7717/peerj-cs.2604.

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Effective malware detection is critical to safeguarding digital ecosystems from evolving cyber threats. However, the scarcity of labeled training data, particularly for cross-family malware detection, poses a significant challenge. This research proposes a novel architecture ConvNet-6 to be used in Siamese Neural Networks for applying Zero-shot learning to address the issue of data scarcity. The proposed model for malware detection uses the ConvNet-6 architecture even with limited training samples. The proposed model is trained with just one labeled sample per sub-family. We conduct extensive
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Khammassi, N., I. Ashraf, J. V. Someren, et al. "OpenQL: A Portable Quantum Programming Framework for Quantum Accelerators." ACM Journal on Emerging Technologies in Computing Systems 18, no. 1 (2022): 1–24. http://dx.doi.org/10.1145/3474222.

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With the potential of quantum algorithms to solve intractable classical problems, quantum computing is rapidly evolving, and more algorithms are being developed and optimized. Expressing these quantum algorithms using a high-level language and making them executable on a quantum processor while abstracting away hardware details is a challenging task. First, a quantum programming language should provide an intuitive programming interface to describe those algorithms. Then a compiler has to transform the program into a quantum circuit, optimize it, and map it to the target quantum processor resp
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Dissertations / Theses on the topic "Portable Executable"

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Namanya, Anitta P., Irfan U. Awan, J. P. Disso, and M. Younas. "Similarity hash based scoring of portable executable files for efficient malware detection in IoT." Elsevier, 2019. http://hdl.handle.net/10454/17168.

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Yes<br>The current rise in malicious attacks shows that existing security systems are bypassed by malicious files. Similarity hashing has been adopted for sample triaging in malware analysis and detection. File similarity is used to cluster malware into families such that their common signature can be designed. This paper explores four hash types currently used in malware analysis for portable executable (PE) files. Although each hashing technique produces interesting results, when applied independently, they have high false detection rates. This paper investigates into a central issue of how
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Namanya, Anitta P. "A Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable files." Thesis, University of Bradford, 2016. http://hdl.handle.net/10454/15863.

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Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building
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Chhabra, Dalbir Kaur R. "Feature selection and clustering for malicious and benign software characterization." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1864.

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Malware or malicious code is design to gather sensitive information without knowledge or permission of the users or damage files in the computer system. As the use of computer systems and Internet is increasing, the threat of malware is also growing. Moreover, the increase in data is raising difficulties to identify if the executables are malicious or benign. Hence, we have devised a method that collects features from portable executable file format using static malware analysis technique. We have also optimized the important or useful features by either normalizing or giving weightage to the
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Mirza, Qublai K. A. "A Cloud-Based Intelligent and Energy Efficient Malware Detection Framework. A Framework for Cloud-Based, Energy Efficient, and Reliable Malware Detection in Real-Time Based on Training SVM, Decision Tree, and Boosting using Specified Heuristics Anomalies of Portable Executable Files." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16043.

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The continuity in the financial and other related losses due to cyber-attacks prove the substantial growth of malware and their lethal proliferation techniques. Every successful malware attack highlights the weaknesses in the defence mechanisms responsible for securing the targeted computer or a network. The recent cyber-attacks reveal the presence of sophistication and intelligence in malware behaviour having the ability to conceal their code and operate within the system autonomously. The conventional detection mechanisms not only possess the scarcity in malware detection capabilities, they
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Lai, Qinan. "Enabling high quality executable domain specific language specification." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/enabling-high-quality-executable-domain-specific-language-specification(bc6a9e87-b624-4072-9430-fbacdaac8289).html.

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Domain Specific Languages (DSL) are becoming a common practice for describing models at a higher abstraction, using a notation that domain experts understand. Designing a DSL usually starts from creating a language specification, and the other tools of the DSLs are derived from the specification. Hence, the quality of the language specification can crucially impact the quality of the complete DSL tool chain. Although many methods for defining a language specification have been proposed, the quality of the language specification they produced is not emphasised. This thesis explores the quality
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Pospíšil, Petr. "Knihovna pro efektivní záznam videa v 3D aplikaci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236587.

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This thesis deals with library for recording video in the background of 3D application. A library is designed to work under the Microsoft Windows and Linux operation systems.  It records image and also sound. Image recording is supported in OpenGL, Direct3D9, Direct3D10 and Direct3D11. To reduce video data size, library supports image compression using MJPG codec. Audio is recorded by WaveForm audio, Windows Core Audio or ALSA. Recorded sound is for whole operation system. A library is able to record up to two audio streams to accommodate possible microphone input. It can mix audio data togeth
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Šálek, Jiří. "Obrana programů před reverzním inženýrstvím." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-431853.

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This thesis deals with illegal distribution of programs. It describes methods of licencing programs and ways their restrictions. It focuses on description basic tools and techniques used to program cracking. Farther this work describes structure of executables files on MS Windows. The work is completed with malware analysis for clarification techniques masking of code. This work is complemented by a demonstration application for program code masking.
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Book chapters on the topic "Portable Executable"

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Mohanta, Abhijit, and Anoop Saldanha. "Virtual Memory and the Portable Executable (PE) File." In Malware Analysis and Detection Engineering. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6193-4_4.

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Jadvani, Nisarg, Mohit Agarwal, and Kavisankar Leelasankar. "Malware Detection Based on Portable Executable File Features." In International Conference on Computing, Communication, Electrical and Biomedical Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86165-0_31.

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Kumar, Avinash, and Meena Chaudhary. "Portable Executable Analysis for Malware Detection for Reducing Risk." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82377-0_46.

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Awan, Saba, and Nazar Abbas Saqib. "Detection of Malicious Executables Using Static and Dynamic Features of Portable Executable (PE) File." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49145-5_6.

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El Mouhtadi, Walid, Yassine Maleh, and Soufyane Mounir. "Machine Learning Techniques for Enhanced Malware Detection in Portable Executable Files." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88653-9_74.

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Tian, Zuwei, and Hengfu Yang. "Software Information Hiding Algorithm Based on Import Table of Portable Executable File." In Advances in Artificial Intelligence and Security. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78621-2_24.

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Belaoued, Mohamed, Bouchra Guelib, Yasmine Bounaas, Abdelouahid Derhab, and Mahmoud Boufaida. "Malware Detection System Based on an In-Depth Analysis of the Portable Executable Headers." In Machine Learning for Networking. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19945-6_11.

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Nanaiah, Nachappa Muckatira, Amit Gantra, and Trapty Agarwal. "Diagnosis and exploration of portable executable (PE) malware using XGboost and other ML classifiers." In Sustainability in Digital Transformation Era: Driving Innovative & Growth. CRC Press, 2024. http://dx.doi.org/10.1201/9781003534136-20.

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Kaminsky, Dan. "Portable Executable and Executable and Linking Formats." In Reverse Engineering Code with IDA Pro. Elsevier, 2008. http://dx.doi.org/10.1016/b978-1-59749-237-9.00003-x.

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Auty, D., A. Burns, C. W. McKay, C. Randall, and P. Rogers. "A Portable Common Executable Environment for Ada." In Distributed Ada: Developments and Experiences. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511526541.013.

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Conference papers on the topic "Portable Executable"

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Vashishtha, Lalit Kumar, Ashish Kumar, and Kakali Chatterjee. "Machine Learning Approaches for Malware Detection in Cloud Portable Executable Files." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752287.

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Balakrishnan, S., S. Leelavathy, Sunil Kumar RM, and S. Simonthomas. "Machine Learning based Portable Executable Malware and DDoS Attacks Detection in IoT Networks." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984796.

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Sathish, A., N.Babu, M. Bhavani, K. Divya, B. Jhoshna, and T. Hemavathi. "Adaptive Malware Detection In Windows Portable Executables (Wpes)." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838133.

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Wang, Tzu-Yen, Chin-Hsiung Wu, and Chu-Cheng Hsieh. "Detecting Unknown Malicious Executables Using Portable Executable Headers." In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.385.

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Santos, Rico S., and Enrique D. Festijo. "Generating Features of Windows Portable Executable Files for Static Analysis using Portable Executable Reader Module (PEFile)." In 2021 4th International Conference of Computer and Informatics Engineering (IC2IE). IEEE, 2021. http://dx.doi.org/10.1109/ic2ie53219.2021.9649225.

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Kumar, Ajit, and G. Aghila. "Portable executable scoring: What is your malicious score?" In 2014 International Conference on Science Engineering and Management Research (ICSEMR). IEEE, 2014. http://dx.doi.org/10.1109/icsemr.2014.7043649.

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Yousaf, M. Shahid, Muhammad Hanif Durad, and Maleeha Ismail. "Implementation of Portable Executable File Analysis Framework (PEFAF)." In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST - 2019). IEEE, 2019. http://dx.doi.org/10.1109/ibcast.2019.8667202.

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Santos, Rico S., and Enrique D. Festijo. "Classifying Portable Executable Malware Using Deep Neural Decision Tree." In 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom). IEEE, 2022. http://dx.doi.org/10.1109/cyberneticscom55287.2022.9865320.

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Kamble, Manojkumar T., and Sridevi. "Feature Extraction and Analysis of Portable Executable Malicious File." In 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, 2022. http://dx.doi.org/10.1109/iccsea54677.2022.9936121.

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Mello, Alexandre, Vitor Gama Lemos, Flávio Barbosa, and Emílio Simoni. "Malware identification on Portable Executable files using Opcodes Sequence." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-006.

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Malicious software (malware) is a relevant cybersecurity threat, as it can damage target systems, hijack data or credentials, and allow remote code execution. In recent years, researchers and companies have focused on uncovering distinct methods for malware detection to avoid system infection. This paper assesses a method that employs opcode sequence analysis, Graph Theory, and Machine Learning to identify malware on Portable Execution files without the need for execution. An approach used by many researchers is to find patterns through the opcode sequences of a file and use some Artificial In
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