Log in

Relevant bibliographies by topics / Malware Analysis / Journal articles

To see the other types of publications on this topic, follow the link: Malware Analysis.

Journal articles on the topic 'Malware Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 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 'Malware Analysis.'

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

Bavishi, Ujaliben Kalpesh, and Bhavesh Madanlal Jain. "Malware Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (2018): 27. http://dx.doi.org/10.23956/ijarcsse.v7i12.507.

Full text

Abstract:

Malware, also known as malicious software affects the user’s computer system or mobile devices by exploiting the system’s vulnerabilities. It is a major threat to the security of the computer systems. Some of the types of malwares that are most commonly used are viruses, trojans, worms, etc. Nowadays, there is a widespread use of malware which allows malware author to get sensitive information like bank details, contact information which is a serious threat in the world. Most of the malwares are spread through internet because of its frequent use which can destroy large systems piercing throug

APA, Harvard, Vancouver, ISO, and other styles

2

Chintha, Prithvi, and Kakelli Anil Kumar. "EMERGING MACHINE LEARNING TECHNIQUES IN MALWARE DETECTION AND ANALYSIS: A COMPARATIVE ANALYSIS." International Journal of Advanced Research 8, no. 10 (2020): 771–79. http://dx.doi.org/10.21474/ijar01/11900.

Full text

Abstract:

New types of malware with unique characteristics are being created daily in legion. This exponential increase in malwareis creating a threat to the internet. From the past decade, various techniques of malware analysis and malware detection have been developed to prevent the efficacy of malware. However, due to the fast-growing numbers and complexities in malware, it is getting difficult to detect and analyze the malware manually. Because of the inefficiency in manual malware analysis, automated malware detection and analysis would be a better solution. Thus, malware analysis supported by mach

APA, Harvard, Vancouver, ISO, and other styles

3

Panwala, Harshitkumar R. "A Methodological Study on Malware Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 450–52. http://dx.doi.org/10.22214/ijraset.2021.38416.

Full text

Abstract:

Abstract: Malware is an executable binary that is designed to be malicious. Malware can be used by attackers to carry out a range of malicious operations, such as spying on the victim using keyloggers or remote access tools (RATs) or deleting or encrypting data for "Ransom" payments. Malware is software that is designed to carry out malicious operations, and it comes in a variety of forms. Malware's impact, according to studies, is escalating. There are several tools available for malware analysis. The present study is the analysis of the malware known as “Malware Analysis”. Malware analysis i

APA, Harvard, Vancouver, ISO, and other styles

4

Krishna, T. Shiva Rama. "Malware Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1847–53. http://dx.doi.org/10.22214/ijraset.2021.35426.

Full text

Abstract:

Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malw

APA, Harvard, Vancouver, ISO, and other styles

5

Srivastava, Prerna, and Mohan Raj. "Feature extraction for enhanced malware detection using genetic algorithm." International Journal of Engineering & Technology 7, no. 2.8 (2018): 444. http://dx.doi.org/10.14419/ijet.v7i2.8.10479.

Full text

Abstract:

The use of internet has affected almost every field today. With the increase in use of internet, the number of malwares affecting the systems has also increased to a great deal. A number of techniques have been developed by the researchers in order to detect these malwares. The Malware Detection consists of two parts, the analysis part and the detection part. Malwares analysis can be categorized into Static analysis, Dynamic analysis and Hybrid Analysis. The Detection techniques can broadly be classified into Signature based techniques and Behaviour based techniques. A brief introduction of Ma

APA, Harvard, Vancouver, ISO, and other styles

6

Suryati, One Tika, and Avon Budiono. "Impact Analysis of Malware Based on Call Network API With Heuristic Detection Method." International Journal of Advances in Data and Information Systems 1, no. 1 (2020): 1–8. http://dx.doi.org/10.25008/ijadis.v1i1.176.

Full text

Abstract:

Malware is a program that has a negative influence on computer systems that don't have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements

APA, Harvard, Vancouver, ISO, and other styles

7

Jang, Jae-wook, and Huy Kang Kim. "Function-Oriented Mobile Malware Analysis as First Aid." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6707524.

Full text

Abstract:

Recently, highly well-crafted mobile malware has arisen as mobile devices manage highly valuable and sensitive information. Currently, it is impossible to detect and prevent all malware because the amount of new malware continues to increase exponentially; malware detection methods need to improve in order to respond quickly and effectively to malware. For the quick response, revealing the main purpose or functions of captured malware is important; however, only few recent works have attempted to find malware’s main purpose. Our approach is designed to help with efficient and effective inciden

APA, Harvard, Vancouver, ISO, and other styles

8

John Oluwafemi Ogun. "Advancements in automated malware analysis: evaluating the efficacy of open-source tools in detecting and mitigating emerging malware threats to US businesses." International Journal of Science and Research Archive 12, no. 2 (2024): 1958–64. http://dx.doi.org/10.30574/ijsra.2024.12.2.1488.

Full text

Abstract:

Malware, short for malicious software, represents a significant and evolving threat to computer systems, targeting individuals, corporations, and governments globally. This paper explores the multifaceted nature of malware, which includes viruses, worms, Trojans, and more, and delves into how they compromise systems by disrupting services, stealing sensitive data, and denying access. Modern malware is increasingly sophisticated, evading traditional detection methods and posing challenges to cybersecurity professionals. This review outlines key methodologies in malware analysis, including MARE

APA, Harvard, Vancouver, ISO, and other styles

9

Muath Alrammal, Muath Alrammal, Munir Naveed Muath Alrammal, Suzan Sallam Munir Naveed, and Georgios Tsaramirsis Suzan Sallam. "A Critical Analysis on Android Vulnerabilities, Malware, Anti-malware and Anti-malware Bypassing." 網際網路技術學刊 23, no. 7 (2022): 1651–61. http://dx.doi.org/10.53106/160792642022122307019.

Full text

Abstract:

<p>Android has become the dominant operating system for portable devices, making it a valuable asset that needs protection. Though Android is very popular; it has several vulnerabilities which attackers use for malicious intents. In this paper, we present a comprehensive study on the threats in Android OS that various malware developers exploit and the different malware functionality based on Android&rsquo;s threats. Furthermore, we analyze and evaluate the anti-malware approaches implemented to face the malware functionalities. Finally, we analyze and categorize malware developers&a

APA, Harvard, Vancouver, ISO, and other styles

10

Dener, Murat, Gökçe Ok, and Abdullah Orman. "Malware Detection Using Memory Analysis Data in Big Data Environment." Applied Sciences 12, no. 17 (2022): 8604. http://dx.doi.org/10.3390/app12178604.

Full text

Abstract:

Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this st

APA, Harvard, Vancouver, ISO, and other styles

11

Gandotra, Ekta, Divya Bansal, and Sanjeev Sofat. "Malware intelligence: beyond malware analysis." International Journal of Advanced Intelligence Paradigms 13, no. 1/2 (2019): 80. http://dx.doi.org/10.1504/ijaip.2019.099945.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Sofat, Sanjeev, Ekta Gandotra, and Divya Bansal. "Malware intelligence: beyond malware analysis." International Journal of Advanced Intelligence Paradigms 13, no. 1/2 (2019): 80. http://dx.doi.org/10.1504/ijaip.2019.10021412.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Christopher ., L. U., and I. T. Ayorinde. "Malware Detection Using Hidden Markov Model." Advances in Multidisciplinary & Scientific Research Journal Publications 12, no. 2 (2024): 37–46. http://dx.doi.org/10.22624/aims/digital/v11n2p4.

Full text

Abstract:

Malware is a broad term for harmful software that poses significant threats by damaging computer systems and spreading across networks. Traditional detection methods include signature-based and heuristic-based techniques, which are effective against known malware but struggle with new, unknown variants, particularly sophisticated ones like metamorphic, encrypted, and polymorphic viruses. Hence, this research aims at improving malware detection, specifically targeting metamorphic malware that can evade traditional detection methods. The study shows the effectiveness of dynamic analysis over sta

APA, Harvard, Vancouver, ISO, and other styles

14

M.Sunitha Reddy, Et al. "Exploiting And Estimating Malware Using Feature Impact Derived From API Call Sequence Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1406–9. http://dx.doi.org/10.17762/ijritcc.v11i10.8684.

Full text

Abstract:

Malware is a serious threat being posed and it has been a continuous process of protecting the systems from existing and new malware variants by defining new approaches for malware detection .In this process malware samples are first analyzed to understand the behavior of the vulnerable samples and accordingly statistical methods are defined for malware detection. Many approaches are defined for understanding the behavior of malware executables which are broadly classified in to static and dynamic assessments. The static analysis can only be used for identifying the existing types of malware b

APA, Harvard, Vancouver, ISO, and other styles

15

Louk, Maya, Hyotaek Lim, and HoonJae Lee. "An Analysis of Security System for Intrusion in Smartphone Environment." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/983901.

Full text

Abstract:

There are many malware applications in Smartphone. Smartphone’s users may become unaware if their data has been recorded and stolen by intruders via malware. Smartphone—whether for business or personal use—may not be protected from malwares. Thus, monitoring, detecting, tracking, and notification (MDTN) have become the main purpose of the writing of this paper. MDTN is meant to enable Smartphone to prevent and reduce the number of cybercrimes. The methods are shown to be effective in protecting Smartphone and isolating malware and sending warning in the form of notification to the user about t

APA, Harvard, Vancouver, ISO, and other styles

16

Singgam, Pritiy, Afifah Naila Nasution, and Pedro Stella Mario Meyar Waruwu. "Utilizing Capa in Kali Linux for Wannacry Malware Identification and Analysis." AURELIA: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia 4, no. 1 (2024): 1242–45. https://doi.org/10.57235/aurelia.v4i1.4775.

Full text

Abstract:

Purpose: This study aims to analyze the WannaCry ransomware using Kali Linux and the Common Access Platform Assistant (CAPA) method to provide a deeper understanding of the malware's attack tactics, capabilities, and behaviors. Methods/Study design/approach: The research was conducted by installing CAPA version 7.4.0 downloaded from GitHub, followed by file extraction and access permission configuration. The WannaCry malware was obtained from the "thezoo" repository on GitHub, extracted, and analyzed using CAPA commands in the Linux terminal. The analysis results were presented in tables showi

APA, Harvard, Vancouver, ISO, and other styles

17

Balaji K. M. and Subbulakshmi T. "Malware Analysis Using Classification and Clustering Algorithms." International Journal of e-Collaboration 18, no. 1 (2022): 1–26. http://dx.doi.org/10.4018/ijec.290290.

Full text

Abstract:

Malware analysis and detection are important tasks to be accomplished as malware is getting more and more arduous at every instance. The threats and problems posed by the public around the globe are also rapidly increasing. Detection of zero-day attacks and polymorphic viruses is also a challenging task to be done. The increasing threats and problems lead to the need for detection techniques which lead to the well-known and the most common approach called as machine learning. The purpose of this survey is to formulate the most effective feature extraction and classification ways that sums up t

APA, Harvard, Vancouver, ISO, and other styles

18

Nur Widiyasono, Siti Rahayu Selamat, Angga Sinjaya, Rianto, Randi Rizal, and Mugi Praseptiawan. "Investigation of Malware Redline Stealer Using Static and Dynamic Analysis Method Forensic." Journal of Advanced Research in Applied Sciences and Engineering Technology 48, no. 2 (2024): 49–62. http://dx.doi.org/10.37934/araset.48.2.4962.

Full text

Abstract:

Redline Stealer is a malware variant discovered in early March 2020 by proof point analyst. Redline is famous for its ability to bypass the antivirus scan. Redline Stealer was created by hacker with the purpose to steal victim’s information such as login data, password and credit card information from the browser application that used in infected computer. This research uses static and dynamic method to analyze redline stealer. The process of static analysis is carried out by observing the malware’s sample file, while dynamic analysis is carried out by monitoring malware’s activity when the ma

APA, Harvard, Vancouver, ISO, and other styles

19

Shoraimov, Khusanboy Uktamboyevich Akhmadjonov Islomjon Kozimjon o'gli. "A SYSTEMATIC LITERATURE REVIEW ON MALWARE ANALYSIS." EURASIAN JOURNAL OF ACADEMIC RESEARCH 2, no. 13 (2022): 860–66. https://doi.org/10.5281/zenodo.7471397.

Full text

Abstract:

Malware is a significant security danger on the Internet nowadays. Hostile to Virus organizations get a huge number of malwares tests each day. It is intended to harm PC frameworks without the information on the proprietor utilizing the framework and method headways are presenting enormous difficulties for scientists in both the scholarly world and the business.

APA, Harvard, Vancouver, ISO, and other styles

20

LYSENKO, S., and R. SCHUKA. "ANALYSIS OF MALWARE DETECTION METHODS IN COMPUTER SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 283, no. 2 (2020): 101–7. https://doi.org/10.31891/2307-5732-2020-283-2-101-107.

Full text

Abstract:

Malware (malicious software or malware) are programs that are designed to make harm and use the resources of the targeted computer. They are often masked in legal programs, imitate them or just hide in different folders and files in the computer. Moreover, they can get an access to the operating system that allows malware to encrypt files and steal personal information. In some cases malware are distributed by themselves, by e-mail from one computer to another, or through infected files and disks. Fast growing amount of malware makes the computer security researchers invent new methods to prot

APA, Harvard, Vancouver, ISO, and other styles

21

Tariq, Muhammad Arham, Muhammad Ismaeel Khan, Aftab Arif, Muhammad Aksam Iftikhar, and Ali Raza A. Khan. "Malware Images Visualization and Classification with Parameter Tunned Deep Learning Model." Metallurgical and Materials Engineering 31, no. 2 (2025): 68–73. https://doi.org/10.63278/1336.

Full text

Abstract:

Malwares can be termed as a malicious program that can gain unauthorized access to the computer. This unauthorized access can damage and harm computing world in many capacities. There are many malware detection approaches present in the world. These approaches include static and dynamic analysis, machine learning, semi -supervised and deep learning-based models. These approaches cannot be visualized, thus cyber security experts face difficulty in interpreting underlying patterns. Conversion of malware byte code into images exits. An improved approach that can not only visualize malware, but al

APA, Harvard, Vancouver, ISO, and other styles

22

Kumar, Rajeev, Mamdouh Alenezi, Md Ansari, Bineet Gupta, Alka Agrawal, and Raees Khan. "Evaluating the Impact of Malware Analysis Techniques for Securing Web Applications through a Decision-Making Framework under Fuzzy Environment." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 94–109. http://dx.doi.org/10.22266/ijies2020.1231.09.

Full text

Abstract:

Nowadays, most of the cyber-attacks are initiated by extremely malicious programs known as Malware. Malwares are very vigorous and can penetrate the security of information and communication systems. While there are different techniques available for malware analysis, it becomes challenging to select the most effective approach. In this context, the decision-making process may be an efficient means of empirically assessing the impact of different methods for securing the web applications. In this research study, we have used a methodology that includes the integration of Fuzzy AHP and Fuzzy TO

APA, Harvard, Vancouver, ISO, and other styles

23

Tahir, Naquash, Ahmad Yusuf, Singh Satyam, Kumar Shubhanshu, and Anjana Yash. "Detection of Malware Using Machine Learning Algorithms." Advancement in Image Processing and Pattern Recognition 6, no. 1 (2023): 1–12. https://doi.org/10.5281/zenodo.7609730.

Full text

Abstract:

<em>Malware is becoming a major cybersecurity threat with increasing frequency every day. There are several ways to classify the new malware based on signatures or code present. Traditional approaches are not very effective against newly emerging Malware- samples. More and more antivirus software offers protection against malware, but zero-day attacks have yet to be achieved. We use machine learning algorithms to improve the mechanism and accordingly provide excellent experimental results. To do Traditional signature approaches also fail, but the new malware does. This document defines malware

APA, Harvard, Vancouver, ISO, and other styles

24

Garg, Umang, Santosh Kumar, and Manoj Kumar. "IHBOT: An Intelligent and Hybrid Model for Investigation and Classification of IoT Botnet." International Journal of Computer Network and Information Security 16, no. 5 (2024): 98–112. http://dx.doi.org/10.5815/ijcnis.2024.05.08.

Full text

Abstract:

The Internet of Things (IoT) is revolutionizing the technological market with exponential growth year wise. This revolution of IoT applications has also brought hackers and malware to gain remote access to IoT devices. The security of IoT systems has become more critical for consumers and businesses because of their inherent heterogenous design and open interfaces. Since the release of Mirai in 2016, IoT malware has gained an exponential growth rate. As IoT system and their infrastructure have become critical resources that triggers IoT malware injected by various shareholders in different set

APA, Harvard, Vancouver, ISO, and other styles

25

Nair, Riya, Kiranbhai R. Dodiya, and Parth Lakhalani. "A Static Approach for Malware Analysis: A Guide to Analysis Tools and Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1451–74. http://dx.doi.org/10.22214/ijraset.2023.57649.

Full text

Abstract:

Abstract: Malicious code presents a severe risk to computer systems, making work difficult for information security and cyber experts. Malware analysis is in great demand because of its importance and function in digital forensics and cyber security. Malware, often known as malicious software, is purposefully written software that harms or damages people, computers, servers, or networks. An overview of malware analysis methods and techniques in the fields of digital forensics and cyber security is given in this article. The study examines several malware types, their characteristics, and analy

APA, Harvard, Vancouver, ISO, and other styles

26

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

27

Chawla, Karan. "A Study of Malware Analysis and Malware Detection Methods in Cyber-Security." International Journal of Scientific Engineering and Research 11, no. 5 (2023): 51–56. https://doi.org/10.70729/se23515123724.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Yildiz, Oktay, and Ibrahim Alper Doğru. "Permission-based Android Malware Detection System Using Feature Selection with Genetic Algorithm." International Journal of Software Engineering and Knowledge Engineering 29, no. 02 (2019): 245–62. http://dx.doi.org/10.1142/s0218194019500116.

Full text

Abstract:

As the use of smartphones increases, Android, as a Linux-based open source mobile operating system (OS), has become the most popular mobile OS in time. Due to the widespread use of Android, malware developers mostly target Android devices and users. Malware detection systems to be developed for Android devices are important for this reason. Machine learning methods are being increasingly used for detection and analysis of Android malware. This study presents a method for detecting Android malware using feature selection with genetic algorithm (GA). Three different classifier methods with diffe

APA, Harvard, Vancouver, ISO, and other styles

29

Syam, Gopi, Susan Jacob Evelyn, John Joel, Rajeev Raynell, and Alex Steve. "Malware Classification using Image Analysis." International Journal on Emerging Research Areas (IJERA) 05, no. 01 (2025): 178–82. https://doi.org/10.5281/zenodo.15289798.

Full text

Abstract:

Abstract—Malware detection and classification have evolved significantly with the integration of pattern recognition and image classification techniques. A pioneering study by Nataraj et al. (2011) [1] demonstrated that malware binaries could be visualized as grayscale images, revealing structural and textural similarities among malware families. Inspired by this approach, this research explores the effectiveness of deep learning-based architectures, specifically the hybrid CoatNet model, in improving malware classification accuracy. Using the MalImg dataset, we investigate the performan

APA, Harvard, Vancouver, ISO, and other styles

30

Jiao, Jian, Qiyuan Liu, Xin Chen, and Hongsheng Cao. "Behavior Intention Derivation of Android Malware Using Ontology Inference." Journal of Electrical and Computer Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/9250297.

Full text

Abstract:

Previous researches on Android malware mainly focus on malware detection, and malware’s evolution makes the process face certain hysteresis. The information presented by these detected results (malice judgment, family classification, and behavior characterization) is limited for analysts. Therefore, a method is needed to restore the intention of malware, which reflects the relation between multiple behaviors of complex malware and its ultimate purpose. This paper proposes a novel description and derivation model of Android malware intention based on the theory of intention and malware reverse

APA, Harvard, Vancouver, ISO, and other styles

31

S., Navaneethan, and Udhaya Kumar S. "ScanSavant: Malware Detection for Android Applications with Explainable AI." International Journal of Interactive Mobile Technologies (iJIM) 18, no. 19 (2024): 171–81. http://dx.doi.org/10.3991/ijim.v18i19.49437.

Full text

Abstract:

Mobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testing, our application detected 94% of SQL injection attempts, outperforming the 86% average detection rate in similar studies. For malware analysis, it achieved a 97% detection accuracy on a dataset of infected files, higher than the industry standard of 93%. Additionally, our app can detect 85 malware variants and assign 15 attributes (Trojan.Gen.8, Worm.Autorun, Adware.Elex, Spywar

APA, Harvard, Vancouver, ISO, and other styles

32

Shoraimov, Khusanboy Uktamboyevich Akhmadjonov Islomjon Kozimjon o'gli. "A MALWARE VARIANT RESISTANT TO TRADITIONAL ANALYSIS TECHNIQUES A FORENSIC ANALYSIS OF ANDROID MALWARE." EURASIAN JOURNAL OF ACADEMIC RESEARCH 2, no. 13 (2022): 867–78. https://doi.org/10.5281/zenodo.7471401.

Full text

Abstract:

In today’s world, the word malware is synonymous with mysterious programs that spread havoc and sow destruction upon the computing system it infects. These malware are analyzed and understood by malware analysts who reverse engineer the program in an effort to understand it and provide appropriate identifications or signatures that enable anti-malware programs to effectively combat and resolve threats. Malware authors develop ways to circumvent or prevent this analysis of their code thus rendering preventive measures ineffective. This paper discusses existing analysis subverting techniqu

APA, Harvard, Vancouver, ISO, and other styles

33

Joshi, Ankit, Komesh Borkar, Rohit Dhote, et al. "A Machine Learning Technique to Detect Malware." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 188–93. http://dx.doi.org/10.22214/ijraset.2022.47841.

Full text

Abstract:

Abstract: Organizations have been threatened by malware for a long time, but timely detection of the virus remains a challenge. Malware may quickly damage the system by doing pointless tasks that burden it and prevent it from operating efficiently. There are two ways to detect malware: the traditional method that relies on the malware's signature and the behavior-based approach. The malware's behavior is characterized by the action it conducts when active in the machine, such as executing the operating system functions and downloading infected files from the internet. Based on how it behaves,

APA, Harvard, Vancouver, ISO, and other styles

34

Waliulu, Raditya Faisal, and Teguh Hidayat Iskandar Alam. "Reverse Engineering Analysis Statis Forensic Malware Webc2-Div." Insect (Informatics and Security): Jurnal Teknik Informatika 4, no. 1 (2019): 15. http://dx.doi.org/10.33506/insect.v4i1.223.

Full text

Abstract:

At this paper focus on Malicious Software also known as Malware APT1 (Advance Persistent Threat) codename WEBC2-DIV the most variants malware has criteria consists of Virus, Worm, Trojan, Adware, Spyware, Backdoor either Rootkit. Although, malware could avoidance scanning antivirus but reverse engineering could be know how dangerous malware infect computer client. Lately, malware attack as a form espionage (cyberwar) one of the most topic on security internet, because of has massive impact. Forensic malware becomes indicator successful user to realized about malware infect. This research about

APA, Harvard, Vancouver, ISO, and other styles

35

SM-D. "Practical Malware Analysis." Network Security 2012, no. 12 (2012): 4. http://dx.doi.org/10.1016/s1353-4858(12)70109-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Potter, Bruce. "Scalable malware analysis." Network Security 2008, no. 4 (2008): 18–20. http://dx.doi.org/10.1016/s1353-4858(08)70055-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Wagener, Gérard, Radu State, and Alexandre Dulaunoy. "Malware behaviour analysis." Journal in Computer Virology 4, no. 4 (2007): 279–87. http://dx.doi.org/10.1007/s11416-007-0074-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Elizarov, D. A., and A. V. Katkov. "Practical Malware Analysis." Herald of Dagestan State Technical University. Technical Sciences 50, no. 3 (2023): 66–71. http://dx.doi.org/10.21822/2073-6185-2023-50-3-66-71.

Full text

Abstract:

Objective. Currently, the main method of attack on organizations is malware. The problem of strengthening protection against this type of attack remains relevant and requires new approaches. The main task is to analyze malware to assess threats and timely detection and take action.Method. For analysis, you should use an isolated environment with a customized environment and software.Result. This paper describes the process of creating an isolated stand for malware analysis and conducted a practical analysis of the malware.bin file taken from the cyberdefenders.org educational resource. Network

APA, Harvard, Vancouver, ISO, and other styles

39

Buriyev, Yusuf Absamat ugli. "NETWORK MALWARE ANALYSIS." EURASIAN JOURNAL OF ACADEMIC RESEARCH 2, no. 13 (2022): 1045–52. https://doi.org/10.5281/zenodo.7478806.

Full text

Abstract:

Malware is one of the problems really existing in the modern post-industrial society. Hackers continuously develop novel techniques to intrude into computer systems for various reasons, so many security researchers should analyze and track new malicious program to protect sensitive information for the computer system. In this paper, we integrate the Interval Type-2 Fuzzy Logic System (IT2FLS) with malware behavioral analysis: Malware Analysis Network in Uzbekistan (MAN in Uzbekistan, MiT). The core techniques of MiT are as follows: (1) automatically collect the logs the difference operation sy

APA, Harvard, Vancouver, ISO, and other styles

40

Michalopoulos, P., V. Ieronymakis, M. T. Khan, and D. Serpanos. "An Open Source, Extensible Malware Analysis Platform." MATEC Web of Conferences 188 (2018): 05009. http://dx.doi.org/10.1051/matecconf/201818805009.

Full text

Abstract:

A malware (such as viruses, ransomware) is the main source of bringing serious security threats to the IT systems and their users now-adays. In order to protect the systems and their legitimate users from these threats, anti-malware applications are developed as a defense against malware. However, most of these applications detect malware based on signatures or heuristics that are still created manually and are error prune. Some recent applications employ data mining and machine learning techniques to detect malware automatically. However, such applications fail to classify them appropriately

APA, Harvard, Vancouver, ISO, and other styles

41

Yucel, Cagatay, Adam Lockett, Ioannis Chalkias, Dimitrios Mallis, and Vasilios Katos. "MAIT: Malware Analysis and Intelligence Tool." Information & Security: An International Journal 50 (2021): 49–65. http://dx.doi.org/10.11610/isij.5024.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Leenu Singh, Leenu Singh, and Syed Imtiyaz Hassan. "Virtualization Evolution For Transparent Malware Analysis." International Journal of Scientific Research 2, no. 6 (2012): 101–4. http://dx.doi.org/10.15373/22778179/june2013/33.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Dhungana, Dikshyant, Ashish Sapkota, Sabigya Pokharel, Sudarshan Devkota, and Bishnu Hari Paudel. "Malware Classification using Static Analysis Approaches." Journal of Artificial Intelligence and Capsule Networks 6, no. 4 (2025): 494–511. https://doi.org/10.36548/jaicn.2024.4.008.

Full text

Abstract:

Malware threats are becoming increasingly complex, thereby posing greater challenges to effective mitigation efforts. It has become more essential than ever to address the malware, as it poses significant threats to individuals, organizations, and governments worldwide. Therefore, effective and more advanced malware classification techniques are necessary to address these malware threats. This proposed study presents an advanced approach to malware classification using static analysis which examines files without executing them. A structured framework was developed for systematic classificatio

APA, Harvard, Vancouver, ISO, and other styles

44

Al-Marghilani, A. "Comprehensive Analysis of IoT Malware Evasion Techniques." Engineering, Technology & Applied Science Research 11, no. 4 (2021): 7495–500. http://dx.doi.org/10.48084/etasr.4296.

Full text

Abstract:

Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the stat

APA, Harvard, Vancouver, ISO, and other styles

45

M Jameel, Naz faith, and Muna M. T. Jawhar. "A Survey on Malware Attacks Analysis and Detected." International Research Journal of Innovations in Engineering and Technology 07, no. 05 (2023): 32–40. http://dx.doi.org/10.47001/irjiet/2023.705005.

Full text

Abstract:

Malware is one of the biggest problems modern internet users face. Private data and pricey computing resources are seriously threatened by the rise in malware attacks. Anti-malware businesses rely on signatures, which do in fact involve regular expressions and strings, to find malware and its related families. Recent malware attacks in recent years have demonstrated that signature-based techniques are error-prone and easily avoided by sophisticated malware programs. This essay provides an introductory overview of malware and analysis techniques used, as well as detection techniques used by res

APA, Harvard, Vancouver, ISO, and other styles

46

Vidyarthi, Deepti, S. P. Choudhary, Subrata Rakshit, and C. R. S. Kumar. "Malware Detection by Static Checking and Dynamic Analysis of Executables." International Journal of Information Security and Privacy 11, no. 3 (2017): 29–41. http://dx.doi.org/10.4018/ijisp.2017070103.

Full text

Abstract:

The advanced malware continue to be a challenge in digital world that signature-based detection techniques fail to conquer. The malware use many anti-detection techniques to mutate. Thus no virus scanner can claim complete malware detection even for known malware. Static and dynamic analysis techniques focus upon different kinds of malware such as Evasive or Metamorphic malware. This paper proposes a comprehensive approach that combines static checking and dynamic analysis for malware detection. Static analysis is used to check the specific code characteristics. Dynamic analysis is used to ana

APA, Harvard, Vancouver, ISO, and other styles

47

РЕГІДА, ПАВЛО, ОЛЕКСАНДЕР БАРМАК, АНТОНІНА КАШТАЛЬЯН та ЕДУАРД МАНЗЮК. "КОНЦЕПЦІЯ ЗАСТОСУВАННЯ РОЗПОДІЛЕНИХ СИСТЕМ ДЛЯ АНАЛІЗУ ПОЛІМОРФНИХ ВІРУСІВ". Herald of Khmelnytskyi National University. Technical sciences 331, № 1 (2024): 38–43. http://dx.doi.org/10.31891/2307-5732-2024-331-4.

Full text

Abstract:

This work presents a model of the application of modern means to ensure the protection of personal data of users from the abnormal influence of polymorphic viruses, with the involvement of distributed computing for effective detection of threats. The challenge of detecting malware persists over an extended period, primarily due to the substantial number of malware instances being created today and the proliferation of software and web services in current use. Despite the large amount of detection tools, incidents of personal data leaks from various platforms used daily are recorded annually. T

APA, Harvard, Vancouver, ISO, and other styles

48

Singh, Veeraj R., Sharmila S P, and Narendra S. Chaudhari. "A Study on Analysis of Malware in Android Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 141–48. http://dx.doi.org/10.22214/ijraset.2022.47268.

Full text

Abstract:

Abstract: Malware is still a big problem around the world, but the nature of malware is changing. Malware is described as any malicious program designed to cause havoc or mischief in a computer system. the malware landscape changes every year, although long-term trends can be identified in year-on-year data reports. Despite numerous anti-malware measures, cybercriminals and hackers do not give up quickly, especially not when there is money to be made in malware. This study emphasizes the need to study malware and its effect on android applications. The primary objective is to infect an android

APA, Harvard, Vancouver, ISO, and other styles

49

Sumargo, Ruly, and Handri Santoso. "Uncovering Malware Families Using Convolutional Neural Networks (CNN)." Indonesian Journal of Artificial Intelligence and Data Mining 7, no. 1 (2023): 97. http://dx.doi.org/10.24014/ijaidm.v7i1.27243.

Full text

Abstract:

Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently class

APA, Harvard, Vancouver, ISO, and other styles

50

Brahara, Beni, Dedy Syamsuar, and Yesi Novaria Kunang. "Analysis of Malware Dns Attack on the Network Using Domain Name System Indicators." Journal of Information Systems and Informatics 2, no. 1 (2020): 131–53. http://dx.doi.org/10.33557/journalisi.v2i1.30.

Full text

Abstract:

University of Bina Darma Palembang has its own DNS server and in this study using log data from the Bina Darma University DNS server as data in the study, DNS log server data is analyzed by network traffic, using Network Analyzer tools to see the activity of a normal traffic or anomaly traffic, or even contains DGA Malware (Generating Algorthm Domain).DGA malware produces a number of random domain names that are used to infiltrate DNS servers. To detect DGA using DNS traffic, NXDomain. The result is that each domain name in a group domain is generated by one domain that is often used at short

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!