A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

Pachhala, Nagababu; Jothilakshmi, S.; Battula, Bhanu Prakash

doi:10.1109/icosec51865.2021.9591763

Cited by 16 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Viruses are self-replicating programs that associate themselves with legitimate files or programs and infect other files or computers‫۔‬ They can damage data by corrupting or altering it, interfering with system functionality, and spreading it to other devices [2].…”

Section: Virusesmentioning

confidence: 99%

See 1 more Smart Citation

Malware and Windows APIs: A Dangerous Duo

Muhammad Taseer Suleman

2024

IJECI

View full text Add to dashboard Cite

This paper introduces its interaction with malware and Windows APIs (application programming interface). The first section describes malware and investigates various types such as viruses, worms and trojans, as well as provides a brief history of malware and its evolution. The second section provides an overview of the Windows APIs. It shows how these interfaces allow software and operating systems to communicate with each other. It also highlights the most commonly used Windows APIs and their functions۔ The follow-up section explores how malware uses Windows APIs for malicious purposes، Explains the common methods used by malware to communicate with these interfaces۔ Includes real-world examples of malware attacks that use some Windows APIs. The study then turns its attention to the Windows API security mechanism, given the security measures taken by Windows to prevent the use of unauthorized APIs۔ The importance of user account control (UAC) and various monitoring and access control systems has been highlighted. The next section introduces the API Hoking and its application to malware. Which explains the strategies used by malware to hook Windows APIs۔ The effects of API hooking and possible detection methods are also discussed. This article provides an in-depth overview of real-world malware that exploits Windows APIs through case studies and analysis. Notable malware analyzes examples using family and API-based attacks۔ The article discusses security tools and ways to identify and block API-based malware, as well as how to design secure programs with Windows APIs Suggestions for this have also been discussed. Finally, malware tactics targeting Windows APIs discuss potential trends and issues, as well as expected API security challenges in the Windows context. This study continues to look at advances in Windows API security and their implications for malware prevention.

show abstract

Section: Virusesmentioning

confidence: 99%

“…Worms are stand-alone programs that replicate and spread freely across networks, often exploiting security vulnerabilities‫۔‬ Unlike viruses, they do not need to be linked to existing files‫۔‬ Insects can use network bandwidth, subdue the system, and help spread other malware [2].…”

Section: Wormsmentioning

confidence: 99%

Malware and Windows APIs: A Dangerous Duo

Muhammad Taseer Suleman

2024

IJECI

View full text Add to dashboard Cite

show abstract

“…The authors obtained labels from videos annotated by human judges [7] Then, the authors in used computer vision and machine learning techniques to identify the affect of students in a school computer laboratory, where the students were interacting with an educational game aimed to explain fundamental concepts of classical mechanics. [8] Unauthorized Access: Using malware to gain access to data for a deep learning project without proper authorization is illegal and unethical. It violates the privacy rights of individuals and the terms of service of the online platform.…”

Section: Literature Surveymentioning

confidence: 99%

Student Expression Detection in E-Learning Platforms

K. Sai Bhavya Sri,

K. Sai Swetha,

K. Bhavani

et al. 2024

EPRA

View full text Add to dashboard Cite

Our research suggests a deep learning method for determining how engaged students are with their online courses. Our method makes use of convolutional neural networks (CNNs) to interpret facial expressions that are recorded via webcams during online conversations. To provide the best possible model input, we preprocess face photos and select a varied dataset. The CNN architecture captures both spatial and temporal dependencies, enhancing engagement level detection accuracy. Our model performs well in classifying expressions that indicate curious, distracted, enthusiastic, observant, uninterested through training and validation. In conclusion, that the online learning experience may be enhanced by using deep learning algorithms for engagement detection, making it more advantageous and successful for students. KEYWORDS – CNN, Webcams, Engagement level, Online learning, Deep Learning, Facial expressions

show abstract

“…Numerous studies have explored the use of machine learning techniques to detect ransomware based on network traffic analysis. Machine learning algorithms, such as decision trees, neural networks, support vector machines, and ensemble methods, were employed to analyze complex and often non-linear data structures inherent in network traffic, and they achieved varying levels of success, highlighting the potential and limitations of each approach [1,2]. Neural networks were particularly noted for their ability to model non-linear relationships and adapt to new, previously unseen patterns of ransomware behavior, providing a dynamic approach to detection [3,4].…”

Section: Related Studiesmentioning

confidence: 99%

Ransomware Detection with a 2-Tier Machine Learning Approach Using a Novel Clustering Algorithm

Zhang,

Liu

2024

Preprint

View full text Add to dashboard Cite

Ransomware poses a significant threat to cybersecurity, causing extensive financial and operational damage by encrypting critical data and demanding ransom for its release. The proposed novel two-tier machine learning approach significantly enhances ransomware detection through the integration of network and file system activities, providing a comprehensive view of system behaviors and improving detection accuracy. Initial clustering of network activities followed through by a refined analysis of file system data enables the identification of complex ransomware patterns. Extensive experimentation has demonstrated that this approach outperforms existing methods, achieving higher precision, recall, and overall accuracy while maintaining scalability and robustness. The research highlights the importance of leveraging diverse data sources and advanced machine learning techniques to create more resilient and effective cybersecurity defenses. The findings demonstrate the potential for practical applications in real-world scenarios, offering a significant advancement in the fight against ransomware and contributing to the protection of critical organizational assets.

show abstract

A Comprehensive Survey on Identification of Malware Types and Malware Classification Using Machine Learning Techniques

Cited by 16 publications

References 22 publications

Malware and Windows APIs: A Dangerous Duo

Malware and Windows APIs: A Dangerous Duo

Student Expression Detection in E-Learning Platforms

Ransomware Detection with a 2-Tier Machine Learning Approach Using a Novel Clustering Algorithm

Contact Info

Product

Resources

About