Commercial Antivirus Software Effectiveness: An Empirical Study

Sukwong, Orathai; Kim, Hyong S.; Hoe, James C.

doi:10.1109/mc.2010.187

Cited by 54 publications

(54 citation statements)

References 8 publications

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“…An interesting analysis of "at risk time" for single AVs is given in [4]. In this paper the authors analyzed how long it takes for different AV vendors to detect a malware.…”

Section: Related Workmentioning

confidence: 99%

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A study of the relationship between antivirus regressions and label changes

Gashi

Sobesto

Mason

et al. 2013

2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE)

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-AntiVirus (AV) products use multiple components to detect malware. A component which is found in virtually all AVs is the signature-based detection engine: this component assigns a particular signature label to a malware that the AV detects. In previous analysis [1-3], we observed cases of regressions in several different AVs: i.e. cases where on a particular date a given AV detects a given malware but on a later date the same AV fails to detect the same malware. We studied this aspect further by analyzing the only externally observable behaviors from these AVs, namely whether AV engines detect a malware and what labels they assign to the detected malware. In this paper we present the results of the analysis about the relationship between the changing of the labels with which AV vendors recognize malware and the AV regressions.

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“…An interesting analysis of "at risk time" for single AVs is given in [4]. In this paper the authors analyzed how long it takes for different AV vendors to detect a malware.…”

Section: Related Workmentioning

confidence: 99%

“…Because of the nature of the exposed vulnerabilities, Dionaea essentially captures Windows Portable Executable (PE) files 4 , the executable file format used on Windows platforms.…”

Section: Dionaeamentioning

confidence: 99%

A study of the relationship between antivirus regressions and label changes

Gashi

Sobesto

Mason

et al. 2013

2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE)

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“…One such study, which provides analysis of "at risk time" for single AVs, is given in [10]. Several sites 1 report rankings and comparisons of AVs, though readers should be careful about the definitions of "system under test" when comparing the results from different reports.…”

Section: Related Workmentioning

confidence: 99%

Does Malware Detection Improve with Diverse AntiVirus Products? An Empirical Study

Gashi

Sobesto

Stanković

et al. 2013

Lecture Notes in Computer Science

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Abstract. We present results of an empirical study to evaluate the detection capability of diverse AntiVirus products (AVs). We used malware samples collected in a geographically distributed honeypot deployment in several different countries and organizations. The malware was collected in August 2012: the results are relevant to recent and current threats observed in the internet. We sent these malware to 42 AVs available from the VirusTotal service to evaluate the benefits in detection from using more than one AV. We then compare these findings with similar ones performed in the past to evaluate diversity with AVs. In general we found that the new findings are consistent with previous ones, despite some differences. This study provides additional evidence that detection capabilities are improved by diversity with AVs.

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“…Malware running on a medical device can negatively impact the confidentiality, integrity, availability and performance of the system. Traditionally, Anti-Virus solutions are used as a golden standard to detect & prevent malware intrusions [1]. However, these solutions proved ineffective on IoT-based medical devices.…”

Section: Introductionmentioning

confidence: 99%

Combating Malware with Whitelisting in IoT-based Medical Devices

Chakravartula¹,

Lakshmi²

2017

IJCA

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With the rapid advancements in the mobile, Internet and wireless technologies, the computing environment is seamlessly getting integrated into the physical world and being connected to the Internet leading to Internet of Things (IoT). In this environment, heterogeneous devices can communicate with one another, leading to innovative applications in healthcare. Malware in IoT environment possesses a great challenge due to interconnected and interoperated systems. Traditional signature based anti-malware solutions will not suffice to healthcare based IoT devices. The paper presents a novel approach of using whitelisting in IoT-based healthcare medical devices and illustrate the performance improvements over traditional solutions. General TermsSecurity, Internet of Things (IoT)

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Commercial Antivirus Software Effectiveness: An Empirical Study

Cited by 54 publications

References 8 publications

A study of the relationship between antivirus regressions and label changes

A study of the relationship between antivirus regressions and label changes

Does Malware Detection Improve with Diverse AntiVirus Products? An Empirical Study

Combating Malware with Whitelisting in IoT-based Medical Devices

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