Log in

Relevant bibliographies by topics / Portable Executable / Journal articles

To see the other types of publications on this topic, follow the link: Portable Executable.

Journal articles on the topic 'Portable Executable'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Portable Executable.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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%).  

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

<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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

H. Al-Khshali, Hasan, та Muhammad Ilyas. "Impact of Portable Executable Header Features on Malware Detection燗ccuracy". Computers, Materials & Continua 74, № 1 (2023): 153–78. http://dx.doi.org/10.32604/cmc.2023.032182.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Patil, Jeet. "Malware Detection of Portable Executable Using Machine Learning and Neural Networks." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 51–59. http://dx.doi.org/10.22214/ijraset.2023.57836.

Full text

Abstract:

Abstract: Among the more well-known types of automatic detection technology, malware detection includes detection and protection methods against viruses caused by viruses, worms, Trojan horses, spyware, and various types of malicious code. Failure to find a malware program at its inception leaves a space where it will send a significant threat and value to online security not only for individuals, organizations but also the community and the nation. And it seems that antivirus software may fail to detect viruses if it is not updated on a website with an anti-virus engine. The great struggle is

APA, Harvard, Vancouver, ISO, and other styles

13

Patil, Jeet. "Malware Detection of Portable Executable Using Machine Learning and Neural Networks." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 51–59. http://dx.doi.org/10.22214/ijraset.2024.57836.

Full text

Abstract:

Abstract: Among the more well-known types of automatic detection technology, malware detection includes detection and protection methods against viruses caused by viruses, worms, Trojan horses, spyware, and various types of malicious code. Failure to find a malware program at its inception leaves a space where it will send a significant threat and value to online security not only for individuals, organizations but also the community and the nation. And it seems that antivirus software may fail to detect viruses if it is not updated on a website with an anti-virus engine. The great struggle is

APA, Harvard, Vancouver, ISO, and other styles

14

S. Sai Nithish, V. Singvalliyappa, A. Sabarees, and V. Praveenkumar. "Detecting File based and Network (BGP) Based Anomalies Using Machine Learning for Enhanced Security." International Research Journal on Advanced Science Hub 7, no. 04 (2025): 269–77. https://doi.org/10.47392/irjash.2025.033.

Full text

Abstract:

The Border Gateway Protocol (BGP) serves as the center of global web routing; however, BGP's reliance upon trust and lack of solid authentication tools make it prone to multiple security threats such as path hijacking, prefix leaks, and ransomware-based events. Typical anomaly finding techniques, dependent on fixed rule systems or small datasets, frequently do not change to complex, changing dangers. For these shortcomings, cybersecurity is improved by way of a scalable, machine learning framework integrating real-time BGP monitoring with anomaly detection through analyzing Portable Executable

APA, Harvard, Vancouver, ISO, and other styles

15

Zhang, Yunchun, Jiaqi Jiang, Chao Yi, et al. "A Robust CNN for Malware Classification against Executable Adversarial Attack." Electronics 13, no. 5 (2024): 989. http://dx.doi.org/10.3390/electronics13050989.

Full text

Abstract:

Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP), and spatial pyramid pooling (SPP), are proposed. Second, we designed an executable adversarial attack to construct adversarial malware by changing the meaningless and unimportant segments within the Portable Executable (PE) header file. Finally, to consolidate the GMP-based CNN, a header-aware loss

APA, Harvard, Vancouver, ISO, and other styles

16

Hilabi, Rawabi, and Ahmed Abu-Khadrah. "Windows operating system malware detection using machine learning." Bulletin of Electrical Engineering and Informatics 13, no. 5 (2024): 3401–10. http://dx.doi.org/10.11591/eei.v13i5.8018.

Full text

Abstract:

Over the years, cybercriminals have become more sophisticated in manipulating network users. Malware is a popular tool they use to exploit victims, targeting valuable assets such as identities and credit cards in the realm of digital technology. Cybersecurity professionals are consistently innovating to detect malicious activities. Machine learning (ML) algorithms are now a leading method for rapidly identifying unseen malware, offering efficiency and intelligence beyond traditional approaches. In fact, attackers like to see the victims suffer from damage caused by malware. Malware can destroy

APA, Harvard, Vancouver, ISO, and other styles

17

Dam, Tien Quang, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, and Thang Le-Dinh. "Visualizing Portable Executable Headers for Ransomware Detection: A Deep Learning-Based Approach." JUCS - Journal of Universal Computer Science 30, no. 2 (2024): 262–86. http://dx.doi.org/10.3897/jucs.104901.

Full text

Abstract:

In recent years, the rapid evolution of ransomware has led to the development of numerous techniques designed to evade traditional malware detection methods. To address this issue, a novel approach is proposed in this study, leveraging machine learning to encode critical information from Portable Executable (PE) headers into visual representations of ransomware samples. The proposed method selects highly impactful features for data sample classification and encodes them as images based on predefined color rules. A deep learning model named peIRCECon (PE Header-Image-based Ransomware Classifica

APA, Harvard, Vancouver, ISO, and other styles

18

Dam, Tien Quang, Nghia Thinh Nguyen, Trung Viet Le, Tran Duc Le, Sylvestre Uwizeyemungu, and Thang Le-Dinh. "Visualizing Portable Executable Headers for Ransomware Detection: A Deep Learning-Based Approach." JUCS - Journal of Universal Computer Science 30, no. (2) (2024): 262–86. https://doi.org/10.3897/jucs.104901.

Full text

Abstract:

In recent years, the rapid evolution of ransomware has led to the development of numerous techniques designed to evade traditional malware detection methods. To address this issue, a novel approach is proposed in this study, leveraging machine learning to encode critical information from Portable Executable (PE) headers into visual representations of ransomware samples. The proposed method selects highly impactful features for data sample classification and encodes them as images based on predefined color rules. A deep learning model named peIRCECon (PE Header-Image-based Ransomware Classifica

APA, Harvard, Vancouver, ISO, and other styles

19

Shiva Darshan, S. L., and C. D. Jaidhar. "Performance Evaluation of Filter-based Feature Selection Techniques in Classifying Portable Executable Files." Procedia Computer Science 125 (2018): 346–56. http://dx.doi.org/10.1016/j.procs.2017.12.046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Kumar, Ajit, K. S. Kuppusamy, and G. Aghila. "A learning model to detect maliciousness of portable executable using integrated feature set." Journal of King Saud University - Computer and Information Sciences 31, no. 2 (2019): 252–65. http://dx.doi.org/10.1016/j.jksuci.2017.01.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Penmatsa, Ravi Kiran Varma, Akhila Kalidindi, and S. Kumar Reddy Mallidi. "Feature Reduction and Optimization of Malware Detection System Using Ant Colony Optimization and Rough Sets." International Journal of Information Security and Privacy 14, no. 3 (2020): 95–114. http://dx.doi.org/10.4018/ijisp.2020070106.

Full text

Abstract:

Malware is a malicious program that can cause a security breach of a system. Malware detection and classification is one of the burning topics of research in information security. Executable files are the major source of input for static malware detection. Machine learning techniques are very efficient in behavioral-based malware detection and need a dataset of malware with different features. In windows, malware can be detected by analyzing the portable executable (PE) files. This work contributes to identifying the minimum feature set for malware detection employing a rough set dependent fea

APA, Harvard, Vancouver, ISO, and other styles

22

Namanya, Anitta Patience, Irfan U. Awan, Jules Pagna Disso, and Muhammad Younas. "Similarity hash based scoring of portable executable files for efficient malware detection in IoT." Future Generation Computer Systems 110 (September 2020): 824–32. http://dx.doi.org/10.1016/j.future.2019.04.044.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Syeda, Durre Zehra, and Mamoona Naveed Asghar. "Dynamic Malware Classification and API Categorisation of Windows Portable Executable Files Using Machine Learning." Applied Sciences 14, no. 3 (2024): 1015. http://dx.doi.org/10.3390/app14031015.

Full text

Abstract:

The rise of malware attacks presents a significant cyber-security challenge, with advanced techniques and offline command-and-control (C2) servers causing disruptions and financial losses. This paper proposes a methodology for dynamic malware analysis and classification using a malware Portable Executable (PE) file from the MalwareBazaar repository. It suggests effective strategies to mitigate the impact of evolving malware threats. For this purpose, a five-level approach for data management and experiments was utilised: (1) generation of a customised dataset by analysing a total of 582 malwar

APA, Harvard, Vancouver, ISO, and other styles

24

Aula Hamed Naji Al-ojaimi. "Advanced Framework for Detecting Malware in Portable Executable (PE) Files Using a Multi-Model." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 769–81. https://doi.org/10.52783/jisem.v10i36s.6562.

Full text

Abstract:

This paper presents Malware detection in Portable Executable (PE) files remains a critical challenge in cybersecurity, with attackers increasingly using obfuscation, polymorphism, and zero-day exploits to evade detection. Malware has emerged as a major problem in today's digital era. The malware goals are to interfere with, damage, or compromise information system and computer system without the operator's approval or knowledge. At present, malware is considered among the most common cyber threat We combine static, dynamic, and hybrid analysis techniques with ensemble learning to achieve super

APA, Harvard, Vancouver, ISO, and other styles

25

Jiang, Jian, and Fen Zhang. "Detecting Portable Executable Malware by Binary Code Using an Artificial Evolutionary Fuzzy LSTM Immune System." Security and Communication Networks 2021 (July 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/3578695.

Full text

Abstract:

As the planet watches in shock the evolution of the COVID-19 pandemic, new forms of sophisticated, versatile, and extremely difficult-to-detect malware expose society and especially the global economy. Machine learning techniques are posing an increasingly important role in the field of malware identification and analysis. However, due to the complexity of the problem, the training of intelligent systems proves to be insufficient in recognizing advanced cyberthreats. The biggest challenge in information systems security using machine learning methods is to understand the polymorphism and metam

APA, Harvard, Vancouver, ISO, and other styles

26

Jawwad, Mohammad Hussein. "A Proposed System for Hiding Information In Portable Executable Files Based on Analyzing Import Section." IOSR Journal of Engineering 4, no. 1 (2014): 21–30. http://dx.doi.org/10.9790/3021-04172130.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Jang, Jiwon, and Daehee Jang. "Design and Countermeasures of PowerShell-based Attack Techniques to Bypass Portable Executable Image Detection Methods." Journal of KIISE 50, no. 9 (2023): 813–20. http://dx.doi.org/10.5626/jok.2023.50.9.813.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

29

Choi, Sunoh. "Malicious Powershell Detection Using Graph Convolution Network." Applied Sciences 11, no. 14 (2021): 6429. http://dx.doi.org/10.3390/app11146429.

Full text

Abstract:

The internet’s rapid growth has resulted in an increase in the number of malicious files. Recently, powershell scripts and Windows portable executable (PE) files have been used in malicious behaviors. To solve these problems, artificial intelligence (AI) based malware detection methods have been widely studied. Among AI techniques, the graph convolution network (GCN) was recently introduced. Here, we propose a malicious powershell detection method using a GCN. To use the GCN, we needed an adjacency matrix. Therefore, we proposed an adjacency matrix generation method using the Jaccard similarit

APA, Harvard, Vancouver, ISO, and other styles

30

t M.D.Shelar, t. M. D. Shelar, Aishwarya C. Ayare Aishwarya.C.Ayare, Trushna S. Bagade Trushna.S.Bagade, Shivashree P. Nimbalkar Shivashree.P.Nimbalkar, and Muskan A. Mujawar Muskan.A.Mujawar. "Multi-View Feature Fusion for Effective Malware Classification Using Deep Learning." International Journal of Pharmaceutical Research and Applications 10, no. 3 (2025): 1070–76. https://doi.org/10.35629/4494-100310701076.

Full text

Abstract:

The rapid increase in global malware infections has necessitated the development of robust malware detection systems to mitigate threats, such as ransomware and crypto-miners, that aim for financial gain. Deep learning-based Convolutional Neural Network (CNN) model for classifying malware in Portable Executable (PE) binary files using a fusion feature set approach. An extensive evaluation of various deep learning architectures and machine learning classifiers, including Support Vector Machines (SVM), was conducted across multi-aspect feature sets encompassing static, dynamic, and image-based f

APA, Harvard, Vancouver, ISO, and other styles

31

M.D.Shelar, M. D. Shelar, Aishwarya C. Ayare Aishwarya.C.Ayare, Trushna S. Bagade Trushna.S.Bagade, Shivashree P. Nimbalkar Shivashree.P.Nimbalkar, and Muskan A. Mujawar Muskan.A.Mujawar. "Multi-View Feature Fusion for Effective Malware Classification Using Deep Learning." International Journal of Advances in Engineering and Management 7, no. 6 (2025): 01–09. https://doi.org/10.35629/5252-07060109.

Full text

Abstract:

The rapid increase in global malware infections has necessitated the development of robust malware detection systems to mitigate threats, such as ransomware and cryptominers, that aim for financial gain. Deep learningbased Convolutional Neural Network (CNN) model for classifying malware in Portable Executable (PE) binary files using a fusion feature set approach. An extensive evaluation of various deep learning architectures and machine learning classifiers, including Support Vector Machines (SVM), was conducted across multi-aspect feature sets encompassing static, dynamic, and image-based fea

APA, Harvard, Vancouver, ISO, and other styles

32

Rizvi, Syed Khurram Jah, Warda Aslam, Muhammad Shahzad, Shahzad Saleem, and Muhammad Moazam Fraz. "PROUD-MAL: static analysis-based progressive framework for deep unsupervised malware classification of windows portable executable." Complex & Intelligent Systems 8, no. 1 (2021): 673–85. http://dx.doi.org/10.1007/s40747-021-00560-1.

Full text

Abstract:

AbstractEnterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet, instant detection using static analysis has become very difficult due to the exponential rise in volume and variety of malware. The compelling need of early stage detection of malware-based a

APA, Harvard, Vancouver, ISO, and other styles

33

James, Peter, and Don Griffiths. "A secure portable execution environment to support teleworking." Information Management & Computer Security 22, no. 3 (2014): 309–30. http://dx.doi.org/10.1108/imcs-07-2013-0052.

Full text

Abstract:

Purpose – This paper presents the design, development and trialling of the mobile execution environment (MEE), a secure portable execution environment designed to support secure teleworking. Teleworking is an established work practice, yet often the information security controls in the teleworking location are weaker than those in a corporate office. Security concerns also prevent organisations allowing personnel to telework. Design/methodology/approach – The design science research methodology was applied to develop the MEE, and this paper is structured using the process elements of the metho

APA, Harvard, Vancouver, ISO, and other styles

34

Kattamuri, Santosh Jhansi, Ravi Kiran Varma Penmatsa, Sujata Chakravarty, and Venkata Sai Pavan Madabathula. "Swarm Optimization and Machine Learning Applied to PE Malware Detection towards Cyber Threat Intelligence." Electronics 12, no. 2 (2023): 342. http://dx.doi.org/10.3390/electronics12020342.

Full text

Abstract:

Cyber threat intelligence includes analysis of applications and their metadata for potential threats. Static malware detection of Windows executable files can be done through the analysis of Portable Executable (PE) application file headers. Benchmark datasets are available with PE file attributes; however, there is scope for updating the data and also to research novel attribute reduction and performance improvement algorithms. The existing benchmark dataset contains non-PE header attributes, and few ignored attributes. In this work, a critical analysis was conducted to develop a new dataset

APA, Harvard, Vancouver, ISO, and other styles

35

Cypto, J., G. Srikanth, K. Surya Prakash, and B. Gunal. "Intelligent Malware Classification Using PE File Metadata and Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5742–50. https://doi.org/10.22214/ijraset.2025.69729.

Full text

Abstract:

Abstract: This study presents a machine learning-based approach to enhance malware detection by analyzing structural and statistical features extracted from Portable Executable (PE) files. Utilizing the ClaMP_Integrated-5184.csv dataset—which includes metadata from PE headers, entropy values, and packer-related information—the research aims to distinguish between benign and malicious software effectively. Traditional signature-based detection methods often fail to detect modern threats due to evasion techniques like obfuscation and polymorphism. In contrast, machine learning offers a more adap

APA, Harvard, Vancouver, ISO, and other styles

36

Go, Lance Jansen C., Ma Sheila A. Magboo, and Vincent Peter C. Magboo. "A Portable Executable Clinical Decision Support Tool for Pneumonia Classification using Average Probability on an Ensemble Model." Procedia Computer Science 218 (2023): 1591–600. http://dx.doi.org/10.1016/j.procs.2023.01.137.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Kotenko, Igor, Konstantin Izrailov, and Mikhail Buinevich. "The Method and Software Tool for Identification of the Machine Code Architecture in Cyberphysical Devices." Journal of Sensor and Actuator Networks 12, no. 1 (2023): 11. http://dx.doi.org/10.3390/jsan12010011.

Full text

Abstract:

This work solves the problem of identification of the machine code architecture in cyberphysical devices. A basic systematization of the Executable and Linkable Format and Portable Executable formats of programs, as well as the analysis mechanisms used and the goals achieved, is made. An ontological model of the subject area is constructed, introducing the basic concepts and their relationships. The specificity of the machine code is analyzed, and an analytical record of the process of identifying the architecture of the machine code (MC) processor is obtained. A method for identifying the MC

APA, Harvard, Vancouver, ISO, and other styles

38

Oktaviani, Anisa, and Melwin Syafrizal. "GandCrab Ransomware Analysis on Windows Using Static Method." Buletin Ilmiah Sarjana Teknik Elektro 3, no. 2 (2021): 163–75. http://dx.doi.org/10.12928/biste.v3i2.4884.

Full text

Abstract:

Malware-infected operating systems may experience system damage, files or loss of important data. Ransomware is a type of malware that works by attacking the internet network and then encrypting the victim's computer. So that the victim can access his computer again, the victim is asked to redeem (ransom) with some money in the form of Bitcoin. One of them is GandCrab. Gandcrab is a very powerful ransomware and only the creators of Gandcrab know the description of the encrypted files.Static analysis is done by importing malware samples into Virustotal, Dependency walker, PEStudio, Exeinfo PE,

APA, Harvard, Vancouver, ISO, and other styles

39

Ismail, Hazim, Rio Guntur Utomo, and Marastika Wicaksono Aji Bawono. "Comparison of Support Vector Machine and Random Forest Method on Static Analysis Windows Portable Executable (PE) Malware Detection." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (2024): 154. http://dx.doi.org/10.30865/mib.v8i1.7110.

Full text

Abstract:

Malware has emerged as a significant concern for computer system security, as it spreads rapidly and adversely affects system performance. Detecting malware has become crucial, and one of the methods utilized is Machine Learning classification, which learns the characteristics of an application without executing it. In this study, the author evaluates the efficacy of malware detection in the static analysis of Windows Portable Executable (PE) files using the Support Vector Machine (SVM) and Random Forest algorithms. The author employs a dataset containing both malware-related PE files and safe

APA, Harvard, Vancouver, ISO, and other styles

40

Yehorov, Serhii, and Tetyana Shkvarnytska. "ADVANCED METHOD OF ANALYSIS OF MALICIOUS SOFTWARE FOR THE PURPOSE OF CREATING SIGNATURES." Visnyk Universytetu “Ukraina”, no. 1 (28) 2020 (2020): 161–70. http://dx.doi.org/10.36994/2707-4110-2020-1-28-14.

Full text

Abstract:

The method of basic static analysis of harmful software is considered, which is based on searching and analyzing the term in files that are built using the PE (Portable Executable) format. The method of basic static analysis of malicious software is considered, which is based on the analysis of headers of executable files, and dynamic libraries, which are built using the PE format. An extended static analysis method is considered, which, in addition to analyzing the term and file headers, uses disassembly of executable files and dynamic libraries and further analysis of the resulting assembler

APA, Harvard, Vancouver, ISO, and other styles

41

Toàn, Nguyễn Tấn, Vũ Thanh Nguyên, Trịnh Quốc Sơn та Lê Đình Tuấn. "Phương pháp phát hiện virus máy tính dựa trên hệ miễn dịch nhân tạo kết hợp thông tin từ cấu trúc PE của tập tin trên hệ điều hành Windows". Tạp chí Khoa học 15, № 12 (2019): 82. http://dx.doi.org/10.54607/hcmue.js.15.12.2321(2018).

Full text

Abstract:

Bài báo này nghiên cứu về một phương pháp phát hiện virus dựa trên giải thuật của hệ miễn dịch nhân tạo (AIS), kết hợp với thông tin được trích xuất từ cấu trúc Portable Executable (PE) của các tập tin trên hệ điều hành Windows, nhằm giúp giảm chi phí trích xuất đặc trưng từ việc dùng đặc trưng của cấu trúc PE và tăng thêm sự đa dạng của các bộ phát hiện thông qua giải thuật hệ miễn dịch nhân tạo. Phương pháp đã được thực nghiệm với các bộ dữ liệu và các bộ phân lớp khác nhau (SVM, Naïve Bayes và Decision Tree). Kết quả thực hiện cho thấy độ chính xác của phương pháp có thể đạt lần lượt 89,25%

APA, Harvard, Vancouver, ISO, and other styles

42

Noever, David A., and Samantha E. Miller Noever. "Deep Learning Classification Methods Applied to Tabular Cybersecurity Benchmarks." International Journal of Network Security & Its Applications 13, no. 03 (2021): 1–13. http://dx.doi.org/10.5121/ijnsa.2021.13301.

Full text

Abstract:

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 54% accuracy. Using feature importance rank, a random forest solution on subsets shows the most important

APA, Harvard, Vancouver, ISO, and other styles

43

Yang, June Ho, and Yeonseung Ryu. "Design and Development of a Command-line Tool for Portable Executable File Analysis and Malware Detection in IoT Devices." International Journal of Security and Its Applications 9, no. 8 (2015): 127–36. http://dx.doi.org/10.14257/ijsia.2015.9.8.10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Baghirov, Elshan. "ADVANCED MACHINE LEARNING AND INTERPRETABILITY FOR WINDOWS MALWARE DETECTION." Journal of Modern Technology and Engineering 9, no. 3 (2024): 165–77. https://doi.org/10.62476/jmte93165.

Full text

Abstract:

Machine learning (ML) has emerged as a powerful tool for detecting and mitigating malware, addressing the evolving challenges in cybersecurity. This paper presents a comprehensive overview of ML techniques applied to Windows Portable Executable (PE) malware detection, spanning from theoretical foundations to practical implementations. Theoretical underpinnings such as feature engineering, model selection, and evaluation metrics are explored, followed by discussions on practical aspects including data preprocessing, model training, and deployment considerations. The experimental setup using the

APA, Harvard, Vancouver, ISO, and other styles

45

David, A. Noever, and E. Miller Noever Samantha. "DEEP LEARNING CLASSIFICATION METHODS APPLIED TO TABULAR CYBERSECURITY BENCHMARKS." International Journal of Network Security & Its Applications (IJNSA) 13, no. 3 (2021): 01–13. https://doi.org/10.5281/zenodo.4910770.

Full text

Abstract:

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2’s convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 54% accuracy. Using feature importance rank, a random forest solution on subsets shows the most imp

APA, Harvard, Vancouver, ISO, and other styles

46

Das, Pragya Paramita. "Malware Analysis Using Memory Forensics." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 488–95. http://dx.doi.org/10.22214/ijraset.2022.47021.

Full text

Abstract:

Abstract: Malware is still the most dangerous issue facing internet users in today's online environment. The newly created malware is separate from the traditional kind, has a more dynamic design, and typically combines traits from two or more different malware types. comparing the various memory acquisition tools that are available, each of which has a varying performance dependent on the setups, installed hardware, and operating system version. If the ending character is not present. To address the growing malware issue, new methodologies like machine learning must be employed. Investigate h

APA, Harvard, Vancouver, ISO, and other styles

47

Bai, Jinrong, Junfeng Wang, and Guozhong Zou. "A Malware Detection Scheme Based on Mining Format Information." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/260905.

Full text

Abstract:

Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using clas

APA, Harvard, Vancouver, ISO, and other styles

48

Malik, Shairoze. "The Machine Learning in Malware Detection." International Journal for Electronic Crime Investigation 5, no. 3 (2022): 29–36. http://dx.doi.org/10.54692/ijeci.2022.050387.

Full text

Abstract:

Malware has become one of the biggest cyberthreats today with the rapid growth of the Internet. Malware can be referred to as any program that performs malicious acts, including data theft, espionage, etc. In a world of growing technology, protection should also increase at the same time. Machine learning has played a significant role in operating systems over the years. Cybersecurity is capable of using machine learning to boost organizations’detection of malware, triage, breach recognition and security alert. Machine learning will significantly change the cyber security climate. New techniqu

APA, Harvard, Vancouver, ISO, and other styles

49

Nguyễn Đức, Việt. "Hướng tiếp cận phát hiện mã độc dựa trên phân tích tĩnh kết hợp thuật toán học máy". Journal of Military Science and Technology 90 (25 жовтня 2023): 134–39. http://dx.doi.org/10.54939/1859-1043.j.mst.90.2023.134-139.

Full text

Abstract:

Kỹ thuật tấn công phát tán mã độc thông qua người dùng rồi từ đó, leo thang lên hệ thống ngày càng được nhiều kẻ tấn công ưu thích sử dụng. Do đó, để phát hiện mã độc thì hướng tiếp cận phát hiện mã độc dựa trên hành vi với sự hỗ trợ của các thuật toán học máy đã mang lại nhiều hiệu quả cao. Mặt khác, trong thực tế những kẻ tấn công thường tìm nhiều cách thức và kỹ thuật khác nhau nhằm che giấu hình vi của mã độc dựa trên Portable Executable File Format (PE File) của mã độc. Điều này đã gây ra nhiều khó khăn cho quá trình phát hiện mã độc của các hệ thống giám sát. Từ những lý do trên, trong b

APA, Harvard, Vancouver, ISO, and other styles

50

Ramesh Prasad Pokhrel. "Behavioral analysis of malware using sandboxing techniques." International Journal of Science and Research Archive 15, no. 3 (2025): 582–86. https://doi.org/10.30574/ijsra.2025.15.3.1781.

Full text

Abstract:

This research paper investigates the dynamic behavioral analysis of Windows-based Portable Executable (PE) malware samples using sandboxing techniques. The study focuses on comparing various sandboxing methodologies with an emphasis on their ability to detect sophisticated malware behaviors in a controlled environment. In particular, techniques such as the incorporation of realistic user behavior emulation and the integration of machine learning with sandbox environments are examined. The methodology involves deploying agent-based and agent-less sandbox systems to monitor malware execution and

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!