Analysis of Android malware detection performance using machine learning classifiers

Ham, Hyo-Sik; Choi, Mi-Jung

doi:10.1109/ictc.2013.6675404

Cited by 31 publications

(5 citation statements)

References 5 publications

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“…Threats to mobile devices have expanded along with the usage of mobile banking, transactions, and e-commerce. As a result, mobile devices are becoming more prone to cyber-attacks than personal computers [94].…”

Section: Malware Detection By Using MLmentioning

confidence: 99%

Cyber Security Using Machine Learning Techniques

Manjramkar¹,

Jondhale²

2023

Advances in Computer Science Research

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Machine learning (ML) is a subfield of Artificial Intelligence (AI) that contributes to the development of systems that can learn from previous data, spot patterns, and make logical judgments with little human interaction. Cybersecurity methodologies provide modern security solutions for detecting and responding to threats. As a result of thieves' ability to circumvent traditional security measures, the previously utilized security solutions are no longer enough. Protecting digital systems from hostile assaults, including those on computers, servers, mobile devices, networks, and associated data, is the practice of cyber security. Accounting for cyber security where machine learning is used and using ML to enable cyber security are the two main components of combining cyber security and ML. We may benefit from this union in a number of ways, including by giving ML models better security, enhancing the effectiveness of cyber security techniques, and enabling the efficient detection of zero-day threats with minimal human involvement. We combine cyber security and ML to address two distinct themes in this survey article. By providing ML strategies for cyber security, the purpose of this paper is to give a wide overview of ML methods employed in cyberspace security.

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Section: Malware Detection By Using MLmentioning

confidence: 99%

Cyber Security Using Machine Learning Techniques

Manjramkar¹,

Jondhale²

2023

Advances in Computer Science Research

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“…Ham and Choi [4] have proposed a new feature set and used the same to detect malware by inputting the feature set to machine learning classifiers. The malware application also hides any acknowledgments transmitted by the telecom operator.…”

Section: Related Workmentioning

confidence: 99%

Protecting Android based applications from malware affected through SMS messages

Bhanu

Sastry

Reddy

2021

IJEECS

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Users use Android-based applications for communicating through emailing, text messaging, and transmission of audio and video objects. The attackers manipulate the email, text, videos, or audio so that users' receipt of the messages causes malware through their handheld devices. A runtime routine is invoked, which causes damage to the local resources of the mobile phone. The manipulation of the messages is done using different signatures, making it difficult to recognize the same using a single approach. Multiple approaches are sometimes required to detect different signature-based incoming messages. Choosing a method that suits the signature of the incoming message is the key. Malware can also enter at the time of installing third-party apps, clicking on the links provided in the messages, installing and invoking the malware in the background. Many issues are involved in dealing with malware detecting, prevention, and curing. A comprehensive architecture is required to deal with every aspect of dealing malware. In this paper, a comprehensive architecture is presented that considers malware's issue, especially concerning malware affected through short message service (SMS) messages operated under the Android operating system. The disection of the SMS messages have been implemented and 99% accuracy has been achieved.

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“…The graph supports our assumption by showing the signicant deviation in the CPU utilisation pattern during the DDoS attack period. In the literature we note works such as [21], [22], [2], [15], [9], [16], [17] where deviations in smartphone resource utilisation are used as indicators of smartphone intrusions. For DroidLight, we use the OCC algorithm that is proposed by [10].…”

Section: Droidlight Approach To Intrusion Detectionmentioning

confidence: 99%

DroidLight

Barbhuiya

Kilpatrick

Nikolopoulos

2020

Proceedings of the 21st International Conference on Distributed Computing and Networking

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Smartphone malware attacks are increasing alongside the growth of smartphone applications in the market. Researchers have proposed techniques to detect malware attacks using various approaches, which broadly include signature and anomaly-based intrusion detection systems (IDSs). Anomaly-based IDSs usually require training machine learning models with datasets collected from running both benign and malware applications. This may result in low detection accuracy when detecting zero-day malwares, i.e. those not previously seen or recorded. In this paper, we propose DroidLight, a lightweight IDS which can detect zero-day malware eciently and eectively. We designed an algorithm for DroidLight that is based on one class classication and probability distribution analysis. For each smartphone application, the classication model learns its normal CPU utilisation and network trac pattern. The model ags an intrusion alert if there is any signicant deviation from the normal pattern. By deploying three self-developed malwares we performed realistic evaluation of DroidLight, i.e. the evaluation was performed on a real device while a real user was interacting with it. Evaluation results demonstrate that DroidLight can detect smartphone malwares with accuracy ranging from 93.3% to 100% while imposing only 1.5% total overhead on device resources. CCS CONCEPTS• Security and privacy → Malware and its mitigation; Intrusion detection systems; Denial-of-service attacks; • Humancentered computing → Smartphones; • Computing methodologies → Classication and regression trees.

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Analysis of Android malware detection performance using machine learning classifiers

Cited by 31 publications

References 5 publications

Cyber Security Using Machine Learning Techniques

Cyber Security Using Machine Learning Techniques

Protecting Android based applications from malware affected through SMS messages

DroidLight

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