Vanity, cracks and malware

Kammerstetter, Markus; Platzer, Christian; Wondracek, Gilbert

doi:10.1145/2382196.2382282

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…Thus, cyber-criminals have shifted from a volume focused strategy to a more targeted approach. One such approach has been the bundling of malware with pirated software [14]. Computing equipment infected with malware at the point-of-sale or subsequently downloaded/installed by users who are willing to trade cost for security by opting for pirated software, either knowingly or unknowingly, provide cyber-criminals with an easier means of acquiring compromised targets.…”

Section: Background and Literature Review A Malware: Characteristics ...mentioning

confidence: 99%

Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study

Iqbal,

Aman,

Rejendran

et al. 2024

IEEE Open J. Comput. Soc.

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Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software. We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms using peerto-peer services. Our dataset comprises a total of 750 pirated software samples. To analyze these samples, we employed seven distinct antivirus (AV) engines. The malware identified by the AV engines was classified into four categories: adware, Trojans, viruses, and a miscellaneous category termed others. Our findings reveal that adware and Trojans are the most prevalent types of malware, with average infection rates of 34% and 35%, respectively, among our pirated software samples. Notably, our evaluation of AV detection performance highlights variations in sensitivity, ranging from a high of 132% to a low of 30% across all AV engines. Furthermore, upon installing pirated software, the most adversely affected operating system settings are the firewall and user account control configurations. Given the potential for malware to steal information or create malicious backdoors, its high prevalence within pirated software poses a substantial security risk to end users.

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Section: Background and Literature Review A Malware: Characteristics ...mentioning

confidence: 99%

Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study

Iqbal,

Aman,

Rejendran

et al. 2024

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

show abstract

“…As previously suggested, malicious websites need to balance two opposing requirements to successfully function: escaping detection tools while attracting visitors [21,22]. To attract visitors, a website needs to signal its claimed functionality to potential users by leveraging appearance, content and experience [23].…”

Section: Introductionmentioning

confidence: 99%

Malicious website identification using design attribute learning

Naim

Cohen

Ben‐Gal

2023

Int. J. Inf. Secur.

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Malicious websites pose a challenging cybersecurity threat. Traditional tools for detecting malicious websites rely heavily on industry-speci c domain knowledge, are maintained by large-scale research operations, and result in a never-ending attacker-defender dynamic. Malicious websites need to balance two opposing requirements to successfully function: escaping malware detection tools while attracting visitors. This fundamental con ict can be leveraged to create a robust and sustainable detection approach based on the extraction, analysis and learning of design attributes for malicious website identi cation.In this paper, we propose a next-generation algorithm for extended design attribute learning that learns and analyzes web page structures, contents, appearances and reputations to detect malicious websites. A large-scale experiment that was conducted on more than 35,000 websites suggests that the proposed algorithm effectively detects more than 83% of all malicious websites while maintaining a low falsepositive rate of 2%. In addition, the proposed method can incorporate user feedback and ag new suspicious websites and thus can be effective against zero-day attacks.

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“…The malware programs are created for diverse purposes such as to destroy the user's data, steal important information from the users, and then demand money. This malicious activity creates a problem for the users because the antivirus cannot detect almost 33% of malware attacks [1]. Each malware has its type and its different damages to the user's computer system.…”

Section: Introductionmentioning

confidence: 99%

Detection of Malware Attacks using Artiﬁcial Neural Network

Rana,

Minhaj Ahmad Khan

2023

VAWKUM trans. comput. sci.

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Malware attacks are increasing rapidly as the technology continues to become prevalent. These attacks have become extremely difficult to detect as they continuously change their mechanism for exploitation of vulnerabilities in software. The conventional approaches to malware detection become ineffective due to a large number of varying patterns and sequences, thereby requiring artificial intelligence-based approaches for the detection of malware attacks. In this paper, we propose an artificial neural network-based model for malware detection. Our proposed model is generic as it can be applied to multiple datasets. We have compared our model with different machine-learning approaches. The experimentation results show that the proposed model can outperform other well-known approach as it achieves 99.6\% , 98.9\% and 99.9\% accuracy on the Windows API call dataset, Top PE Imports Dataset and Malware Dataset, respectively.

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Vanity, cracks and malware

Cited by 12 publications

References 7 publications

Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study

Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study

Malicious website identification using design attribute learning

Detection of Malware Attacks using Artiﬁcial Neural Network

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