An Efficient Boosting-Based Windows Malware Family Classification System Using Multi-Features Fusion

Chen, Zhiguo; Ren, Xuanyu

doi:10.3390/app13064060

Cited by 6 publications

(2 citation statements)

References 39 publications

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“…The GBC's effectiveness in managing intricate data relationships is attributed to its ensemble learning approach and the optimization of specific hyperparameters. The GBC lies in constructing an additive model in a forward stage-wise manner (Chen and Ren 2023). Formally, this is expressed as:…”

Section: Model Training Process With Gradient Boosting Classifiermentioning

confidence: 99%

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

Hossain,

Islam

2024

Cybersecurity

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In the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.

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Section: Model Training Process With Gradient Boosting Classifiermentioning

confidence: 99%

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

Hossain,

Islam

2024

Cybersecurity

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show abstract

“…Our method, on the other hand, benefits from dynamic feature extraction and compression, enhancing detection efficacy against polymorphic and variant malware while maintaining stability under noisy conditions. Zhiguang Chen et al [33] proposed an efficient boosting-based malware family classification system using multi-features fusion. Utilizing the BIG2015 dataset, their approach employed various tree models such as XGBoost, LightGBM, and CatBoost, demonstrating outstanding performance.…”

Section: Related Workmentioning

confidence: 99%

CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing

Wu,

Peng,

Zhu

et al. 2024

Sensors

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With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare. However, these intelligent sensors face many security threats, particularly from malware attacks. Identifying and classifying malware is crucial for preventing such attacks. As the number of sensors and their applications grow, malware targeting sensors proliferates. Processing massive malware samples is challenging due to limited bandwidth and resources in IoT environments. Therefore, compressing malware samples before transmission and classification can improve efficiency. Additionally, sharing malware samples between classification participants poses security risks, necessitating methods that prevent sample exploitation. Moreover, the complex network environments also necessitate robust classification methods. To address these challenges, this paper proposes CSMC (Compressed Sensing Malware Classification), an efficient malware classification method based on compressed sensing. This method compresses malware samples before sharing and classification, thus facilitating more effective sharing and processing. By introducing deep learning, the method can extract malware family features during compression, which classical methods cannot achieve. Furthermore, the irreversibility of the method enhances security by preventing classification participants from exploiting malware samples. Experimental results demonstrate that for malware targeting Windows and Android operating systems, CSMC outperforms many existing methods based on compressed sensing and machine or deep learning. Additionally, experiments on sample reconstruction and noise demonstrate CSMC’s capabilities in terms of security and robustness.

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Using multi-audio feature fusion for android malware detection

Tarwireyi

Terzoli

Adigun

2023

Computers & Security

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An Efficient Boosting-Based Windows Malware Family Classification System Using Multi-Features Fusion

Cited by 6 publications

References 39 publications

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing

Using multi-audio feature fusion for android malware detection

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