Malware Classification Using Ensemble Classifiers

Hijazi, Mohd Hanafi Ahmad; Tan, Choon Beng; Mountstephens, James; Lim, Yuto; Nisar, Kashif

doi:10.1166/asl.2018.10710

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial speech detection techniques have witnessed significant evolution, falling into three primary categories: classic machine learning, end-to-end learning, and hybrid methodologies. Classic machine learning entails the manual crafting and extraction of predetermined features from data samples, which are then subject to separate classification modules [4,5]. In contrast, end-to-end learning orchestrates the automatic and joint identification and learning of all data sample features to determine their class labels.…”

Section: Related Workmentioning

confidence: 99%

“…This approach extracts text-based features from the hexadecimal representation of audio data to create a feature space. Drawing inspiration from previous studies [5,9], which effectively counted opcode occurrences in executable files for malware classification, we adapted this method for artificial speech detection. Leveraging the hexadecimal representation of speech, our study aimed to extract features capable of distinguishing between bonafide and spoofed speech.…”

Section: Feature Engineering Using Data Transformation Approachmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection

Tan,

Ahmad Hijazi

2024

ITM Web Conf.

View full text Add to dashboard Cite

The rise of voice biometrics has transformed user authentication and offered enhanced security and convenience while phasing out less secure methods. Despite these advancements, Automatic Speaker Verification (ASV) systems remain vulnerable to spoofing, particularly with artificial speech generated swiftly using advanced speech synthesis and voice conversion algorithms. A recent data transformation technique achieved an impressive Equal Error Rate (EER) of 1.42% on the ASVspoof 2019 Logical Access Dataset. While this approach predominantly relies on Support Vector Machine (SVM) as the backend classifier for artificial speech detection, it is vital to explore a broader range of classifiers to enhance resilience. This paper addresses this research gap by systematically assessing classifier efficacy in artificial speech detection. The objectives are twofold: first, to evaluate various classifiers, not limited to SVM, and identify those best suited for artificial speech detection; second, to compare this approach's performance with existing methods. The evaluation demonstrated SVM-Polynomial as the top-performing classifier, surpassing the end-to-end learning approach. This work contributes to a deeper understanding of classifier efficacy and equips researchers and practitioners with a diversified toolkit for building robust ASV spoofing detection systems.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Feature Engineering Using Data Transformation Approachmentioning

confidence: 99%

A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection

Tan,

Ahmad Hijazi

2024

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…A work that utilizes features extracted from hexadecimal represented data for classification problems were found in [6], [7]. In the works [6], [7] the occurrences of each opcode in the executable file were counted and used as features to classify malicious software (malware). The approach used by [6], [7] produced high accuracy in malware classification.…”

Section: Hexadecimal Frequenciesmentioning

confidence: 99%

“…In the works [6], [7] the occurrences of each opcode in the executable file were counted and used as features to classify malicious software (malware). The approach used by [6], [7] produced high accuracy in malware classification. The approach was able to achieve good performance because the different classes of malware usually have a higher frequency of certain opcodes.…”

Section: Hexadecimal Frequenciesmentioning

confidence: 99%

A comparison of different support vector machine kernels for artificial speech detection

Tan

Hijazi²,

Nohuddin³

2023

TELKOMNIKA

View full text Add to dashboard Cite

As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.

show abstract

“…The application of multiple classifiers in PAD systems also can be found in the literature. In many domains involving machine learning and classification, it has been shown that applying ensemble classifiers may improve the performance of a system [42,79]. Nevertheless, in the field of voice recognition, it has been shown that applying multiple classifiers with the same feature hardly improves the performance of the voice PAD system [15].…”

Section: Classifiersmentioning

confidence: 99%

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Tan

Hijazi

Khamis

et al. 2021

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

show abstract

Malware Classification Using Ensemble Classifiers

Cited by 5 publications

References 0 publications

A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection

A Comparative Evaluation on Data Transformation Approach for Artificial Speech Detection

A comparison of different support vector machine kernels for artificial speech detection

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Contact Info

Product

Resources

About