A multiview learning method for malware threat hunting: windows, IoT and android as case studies

Darabian, Hamid; Dehghantanha, Ali; Hashemi, Sattar; Taheri, Mohammad; Azmoodeh, Amin; Homayoun, Sajad; Choo, Kim‐Kwang Raymond; Parizi, Reza M.

doi:10.1007/s11280-019-00755-0

Cited by 40 publications

(15 citation statements)

References 43 publications

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“…In contrast, the theoretical processing speed of a subsequence-based music retrieval system is much faster than that of a speech recognitionbased system, making it possible to handle large amounts of Internet audio data. Existing research has achieved certain results for music retrieval, and if the retrieval system for music can achieve better results for speech retrieval at the same time, then it can make speech retrieval and audio retrieval be applied in the same system without the need for two systems and two algorithms [16]. In a real-time system, how to retrieve the information users want quickly is one of the most important problems facing retrieval.…”

Section: Related Workmentioning

confidence: 99%

“…Wavelet transform is a new transform analysis method, which inherits and develops the idea of localization of shorttime Fourier transform, while overcoming the shortcomings such as window size does not change with frequency and can provide a time-frequency window that changes with frequency, which is an ideal tool for signal time-frequency analysis and processing [16]. In this algorithm, we use the Hal wavelet transform to process the subspeech spectrogram.…”

Section: Subsequence Music Retrieval Feature Processingmentioning

confidence: 99%

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[Retracted] Matching Subsequence Music Retrieval in a Software Integration Environment

Qin

2021

Complexity

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This paper firstly introduces the basic knowledge of music, proposes the detailed design of a music retrieval system based on the knowledge of music, and analyzes the feature extraction algorithm and matching algorithm by using the features of music. Feature extraction of audio data is the important research of this paper. In this paper, the main melody features, MFCC features, GFCC features, and rhythm features, are extracted from audio data and a feature fusion algorithm is proposed to achieve the fusion of GFCC features and rhythm features to form new features under the processing of principal component analysis (PCA) dimensionality reduction. After learning the main melody features, MFCC features, GFCC features, and rhythm features, based on the property that PCA dimensionality reduction can effectively reduce noise and improve retrieval efficiency, this paper proposes vector fusion by dimensionality reduction of GFCC features and rhythm features. The matching retrieval of audio features is an important task in music retrieval. In this paper, the DTW algorithm is chosen as the main algorithm for retrieving music. The classification retrieval of music is also achieved by the K-nearest neighbor algorithm. In this paper, after implementing the research and improvement of algorithms, these algorithms are integrated into the system to achieve audio preprocessing, feature extraction, feature postprocessing, and matching retrieval. This article uses 100 different kinds of MP3 format music as the music library and randomly selects 4 pieces each time, and it tests the system under different system parameters, recording duration, and environmental noise. Through the research of this paper, the efficiency of music retrieval is improved and theoretical support is provided for the design of music retrieval software integration system.

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Section: Related Workmentioning

confidence: 99%

Section: Subsequence Music Retrieval Feature Processingmentioning

confidence: 99%

[Retracted] Matching Subsequence Music Retrieval in a Software Integration Environment

Qin

2021

Complexity

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“…The majority of previous efforts, include having multiple ML threat hunting module [1] instead of a single comprehensive model. On the other hand, deep learning approaches have shown to have a significant impact on detecting malware [4,5,6,7,8,9] in different domains. The most complex challenge in any attack attribution task is building a verifiable and scientific method to relate different pieces of evidence and building a complete picture that depicts possible sources of an attack.…”

Section: Introductionmentioning

confidence: 99%

MVFCC: A Multi-View Fuzzy Consensus Clustering Model for Malware Threat Attribution

et al. 2020

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The rise of emerging cyberthreats has led to a shift of focus on identifying the source of threat instead of the type of attack to provide a more effective defense to compromised environments against malicious acts. The most complex type of cyberthreat is the Advanced Persistent Threat (APT) attack that is usually backed by one or more states and lunched using a range of clandestine techniques aiming at high-value targets. Finding the source of the attackers and the associated campaign behind the threats can lead to taking an optimum defense decision in a more timely fashion. Threat attribution is an act of attributing an attack to the source of the attack. Threat attribution can not be fully achieved by a single piece of evidence (i.e. single view) from malicious actors as the evidence could get obfuscated by the actor to evade the detection mechanism. In this paper, we propose a multi-view fuzzy consensus clustering model for attributing cyber threat payloads (malware) to its actor. We conduct over 4000 experiments to find out the best combinations of all 12 extracted views for the attribution task. Our experiments use five well-know APT families payloads. To avoid bias in the results, we apply a fuzzy pattern tree and multi-modal fuzzy classifier for our inference engines of all views. To define an optimum distinction among the malicious actor's behavior we implemented the consensus clustering technique. The comparison analysis of a singleview versus multi-view result justifies a significant improvement in the accuracy rate of attribution for all actors. The obtained results from the multi-view aspect of our proposed model give 95.2% accuracy.

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“…A machine learning method was used in their paper to automatically detect ransomware. Darabian et al [4] proposed a Multiview learning method to provide multiple views for malware threat hunting in the Internet of Things platforms. While these threat hunting methods could improve the accuracy of detection in a specific kind of threat actions, they did not address the diversity of threat activities.…”

Section: Related Workmentioning

confidence: 99%

A Threat Hunting Framework for Industrial Control Systems

Jadidi¹,

Lü

2021

IEEE Access

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A multiview learning method for malware threat hunting: windows, IoT and android as case studies

Cited by 40 publications

References 43 publications

[Retracted] Matching Subsequence Music Retrieval in a Software Integration Environment

[Retracted] Matching Subsequence Music Retrieval in a Software Integration Environment

MVFCC: A Multi-View Fuzzy Consensus Clustering Model for Malware Threat Attribution

A Threat Hunting Framework for Industrial Control Systems

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