[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118282-en":3,"doc-seo-118282-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118282,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox","Modern malware growth in variety and volume has become a major cybersecurity challenge. The study leverages machine learning to detect malware by learning hidden patterns from API call sequence datasets, and evaluates deep learning approaches with CNN and RNN for automatic feature and behavioral extraction. Deep learning is compared against conventional machine learning baselines including SVM, RF, KNN, XGB, and GBC using the same dataset. Results report very high detection accuracy, reaching up to 99% in some cases, enabled by dynamic malware analysis with Cuckoo Sandbox.","Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox  \nAmaal F. Alshmarni and Mohammed A. Alliheedi  \nDepartment of Computer Science, Al-Baha University, Al Bahah, Saudi Arabia  \n[amaalalshmarni@hotmail.com](amaalalshmarni@hotmail.com), [malliheedi@bu.edu.sa](malliheedi@bu.edu.sa)  \nAbstract— In the modern era, malware is experiencing a significant increase in both its variety and quantity, aligning with the widespread adoption of the digital world. This surge in malware has emerged as a critical challenge in the realm of cybersecurity, prompting numerous research endeavors and contributions to address the issue. Machine learning algorithms have been leveraged for malware detection due to their ability to uncover concealed patterns within vast datasets. However, deep learning algorithms, characterized by their multi-layered structure, surpass the limitations of traditional machine learning approaches. By employing deep learning techniques such as CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network), this study aims to classify and identify malware extracted from a dataset containing API call sequences. The performance of these algorithms is compared with that of conventional machine learning methods, including SVM (Support Vector Machine), RF (Random Forest), KNN (K-Nearest Neighbors), XGB (Extreme Gradient Boosting), and GBC (Gradient Boosting Classifier), all using the same dataset. The outcomes of this research demonstrate that both deep learning and machine learning algorithms achieve remarkably high levels of accuracy, reaching up to 99% in certain cases.  \nKeywords: Malware Analysis, Machine Learning, Deep Learning, Malware Dataset.  \nI. INTRODUCTION  \nIn the digital age, the ceaseless evolution of malware  \nremains an omnipresent threat to individuals,  \norganizations, and society at large. Malicious software, or malware, has evolved to become highly sophisticated, elusive, and continually adapted to evade traditional detection mechanisms. Amid this relentless onslaught, the fusion of deep learning techniques with the dynamic analysis capabilities of Cuckoo Sandbox emerges as a beacon of hope in the field of cybersecurity. This paper embarks on a transformative journey by introducing a groundbreaking malware dataset, meticulously curated through dynamic analysis using Cuckoo Sandbox, to drive innovation in malware detection.  \nDeep learning has emerged as a powerful force in various domains, including computer vision, natural language processing, and speech recognition. Its application to malware detection is compelling, as it enables the automatic extraction of intricate features and  \nbehavioral patterns exhibited by malware. However, the performance of deep learning models is inexorably tied to the quality and diversity of the data on which they are trained. Conventional malware datasets often fall short in providing the breadth and depth required to effectively combat emerging malware threats.  \nTo bridge this gap, this paper pioneers a methodology that leverages the dynamic analysis capabilities of Cuckoo Sandbox, a widely adopted and versatile malware analysis tool. Cuckoo Sandbox simulates the execution of suspicious files within a controlled environment [1] . Observing their behavior and interactions with the system. This dynamic approach offers an unparalleled opportunity to capture the nuanced tactics and evasion strategies employed by malware, making it an ideal partner for deep learning-based malware detection. The focal point of this paper revolves around the creation of a comprehensive and timely malware dataset, meticulously constructed through the detailed analysis of malware samples using Cuckoo Sandbox.  \nSince 1988, computer security breaches have increased dramatically. All malicious software that infiltrates a computer system without the user’s knowledge is referred to as” malware.” The terms” malicious software” and”software” were combined to create this ","cbCaihU8MycAsPjb","https://ap.wps.com/l/cbCaihU8MycAsPjb","pdf",586988,1,"English","en",105,"# Introduction\n## Malware evolution and cybersecurity challenge\n## Deep learning for malware detection\n## Dynamic analysis and Cuckoo Sandbox\n## Malware terminology and detection overview","[{\"question\":\"How does the study use Cuckoo Sandbox in malware detection?\",\"answer\":\"It relies on dynamic analysis by executing suspicious files in a controlled environment to observe malware behavior and interactions, which supports building a malware dataset for model training.\"},{\"question\":\"Which deep learning methods are evaluated for malware classification?\",\"answer\":\"The study evaluates CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) on malware extracted from API call sequence data.\"},{\"question\":\"How do the results compare deep learning with conventional machine learning algorithms?\",\"answer\":\"Both deep learning and conventional machine learning methods achieve high accuracy, with reported performance reaching up to 99% in certain cases when using the same dataset.\"}]","Enhancing Malware Detection by Integrating Machine Learning with Cuckoo Sandbox | 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does the study use Cuckoo Sandbox in malware detection?","Question",{"text":75,"@type":76},"It relies on dynamic analysis by executing suspicious files in a controlled environment to observe malware behavior and interactions, which supports building a malware dataset for model training.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which deep learning methods are evaluated for malware classification?",{"text":80,"@type":76},"The study evaluates CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) on malware extracted from API call sequence data.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results compare deep learning with conventional machine learning algorithms?",{"text":84,"@type":76},"Both deep learning and conventional machine learning methods achieve high accuracy, with reported performance reaching up to 99% in certain cases when using the same 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