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Relevant bibliographies by topics / Active Malware Analysis

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Journal articles

Academic literature on the topic 'Active Malware Analysis'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Active Malware Analysis"

1

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.

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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,

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2

Miraglia, Armando, and Matteo Casenove. "Fight fire with fire: the ultimate active defence." Information & Computer Security 24, no. 3 (2016): 288–96. http://dx.doi.org/10.1108/ics-01-2015-0004.

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Purpose This paper proposes an approach to deal with malware and botnets, which in recent years have become one of the major threats in the cyber world. These malicious pieces of software can cause harm not only to the infected victims but also to actors at a much larger scale. For this reason, defenders, namely, security researchers and analysts, and law enforcement have fought back and contained the spreading infections. However, the fight is fundamentally asymmetric. Design/methodology/approach In this paper, the authors argue the need to equip defenders with more powerful active defence to

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3

Zhang, Hong, Shumin Yang, Guowen Wu, Shigen Shen, and Qiying Cao. "Steady-State Availability Evaluation for Heterogeneous Edge Computing-Enabled WSNs with Malware Infections." Mobile Information Systems 2022 (April 11, 2022): 1–16. http://dx.doi.org/10.1155/2022/4743605.

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To evaluate the steady-state availability of heterogeneous edge computing-enabled wireless sensor networks (HECWSNs) with malware infections, we first propose a Stackelberg attack-defence game to predict the optimal strategies of malware and intrusion detection systems (IDSs) deployed in heterogeneous sensor nodes (HSNs). Next, we present a new malware infection model—heterogeneous susceptible-threatened-active-recovered-dead (HSTARD) based on epidemic theory. Then, considering the heterogeneity of sink sensor nodes and common sensor nodes and the malware attack correlation, we derive the stat

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4

Shatnawi, Ahmed S., Aya Jaradat, Tuqa Bani Yaseen, Eyad Taqieddin, Mahmoud Al-Ayyoub, and Dheya Mustafa. "An Android Malware Detection Leveraging Machine Learning." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/1830201.

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Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic anal

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5

Londoño, Sebastián, Christian Urcuqui, Manuel Fuentes Amaya, Johan Gómez, and Andrés Navarro Cadavid. "SafeCandy: System for security, analysis and validation in Android." Sistemas y Telemática 13, no. 35 (2015): 89–102. http://dx.doi.org/10.18046/syt.v13i35.2154.

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Android is an operating system which currently has over one billion active users for all their mobile devices, a market impact that is influencing an increase in the amount of information that can be obtained from different users, facts that have motivated the development of malicious software by cybercriminals. To solve the problems caused by malware, Android implements a different architecture and security controls, such as a unique user ID (UID) for each application, while an API permits its distribution platform, Google Play applications. It has been shown that there are ways to violate th

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6

Sartea, Riccardo, Alessandro Farinelli, and Matteo Murari. "SECUR-AMA: Active Malware Analysis Based on Monte Carlo Tree Search for Android Systems." Engineering Applications of Artificial Intelligence 87 (January 2020): 103303. http://dx.doi.org/10.1016/j.engappai.2019.103303.

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7

O'Callaghan, Derek, Martin Harrigan, Joe Carthy, and Pádraig Cunningham. "Network Analysis of Recurring YouTube Spam Campaigns." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (2021): 531–34. http://dx.doi.org/10.1609/icwsm.v6i1.14288.

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As the popularity of content sharing websites has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to malicious third-party websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies, and present a

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8

Duraisamy Soundrapandian, Pradeepkumar, and Geetha Subbiah. "MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric." Symmetry 14, no. 10 (2022): 2221. http://dx.doi.org/10.3390/sym14102221.

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Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example of a symmetric model of communication, and app collusion, where apps collaborate dynamically with unknown malware apps, is an example of a serious threat with an asymmetric pattern. The symmetric nature of app collaboration can become vulnerable when a vulnerability called PendingIntent is exchanged during Inter-Component Communication (ICC). The

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9

Nawaz, Umair, Muhammad Aleem, and Jerry Chun-Wei Lin. "On the evaluation of android malware detectors against code-obfuscation techniques." PeerJ Computer Science 8 (June 21, 2022): e1002. http://dx.doi.org/10.7717/peerj-cs.1002.

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The Android mobile platform is the most popular and dominates the cell phone market. With the increasing use of Android, malware developers have become active in circumventing security measures by using various obfuscation techniques. The obfuscation techniques are used to hide the malicious code in the Android applications to evade detection by anti-malware tools. Some attackers use the obfuscation techniques in isolation, while some attackers use a mixed approach (i.e., employing multiple obfuscation techniques simultaneously). Therefore, it is crucial to analyze the impact of the different

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10

Wu, Xiaojun, Qiying Cao, Juan Jin, Yuanjie Li, and Hong Zhang. "Nodes Availability Analysis of NB-IoT Based Heterogeneous Wireless Sensor Networks under Malware Infection." Wireless Communications and Mobile Computing 2019 (January 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/4392839.

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The Narrowband Internet of Things (NB-IoT) is a main stream technology based on mobile communication system. The combination of NB-IoT and WSNs can active the application of WSNs. In order to evaluate the influence of node heterogeneity on malware propagation in NB-IoT based Heterogeneous Wireless Sensor Networks, we propose a node heterogeneity model based on node distribution and vulnerability differences, which can be used to analyze the availability of nodes. We then establish the node state transition model by epidemic theory and Markov chain. Further, we obtain the dynamic equations of t

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