[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118443-en":3,"doc-seo-118443-105":30,"detail-sidebar-cat-0-en-105":92},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118443,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Shellcode Classification with Machine Learning - Based on Binary Classification","Machine learning-based binary classification is used to detect whether network records represent Shellcode or non-Shellcode attacks. The study addresses the growing relevance of cyber security, where malware such as Shellcode can disrupt or destroy devices and increase organizational exposure. Experiments train and test models using the UNSW_NB15 dataset and implement K-Nearest Neighbour, Decision Tree, and Naïve Bayes classifiers. Results show accuracy of 96.82% (KNN), 97.08% (Decision Tree), and 63.43% (Naïve Bayes).","Shellcode Classification with Machine Learning Based on  \nBinary Classification  \nJaka Naufal Semendawai1*, Deris Stiawan2, Iwan Pahendra3  \nUniversitas Sriwijaya, Indonesia  \n[Email: j](Email: jaka.semendawai@gmail.com)[aka.semendawai@gmail.com](Email: jaka.semendawai@gmail.com), [deris@unsri.ac.id](deris@unsri.ac.id),[3](3iwanpahendra@unsri.ac.id)[iwanpahendra@unsri.ac.id](3iwanpahendra@unsri.ac.id)  \n*Correspondence  \n\n| ABSTRACT |\n| --- |\n| Keywords: binary The Internet can link one person to another using their\u003Cbr>classification; cyber respective devices. The internet itself has both positive and\u003Cbr>security; machine negative impacts. One example of the internet's negative\u003Cbr>learning; supervised impact is malware that can disrupt or even kill a device or its\u003Cbr>machine learning; users; that is why cyber security is required. Many methods\u003Cbr> hyperparameter tuning  can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a record in the testing data is Shellcode or non-Shellcode attack. The KNN, Decision Tree, and Naïve Bayes classifiers achieve accuracy levels of 96.82%, 97.08%, and 63.43%, respectively. The results of this research are expected to provide insight into the use of machine learning in detecting or classifying malware or other types of cyber attacks. |\n|  |\n\nIntroduction  \nAwareness of the importance of cyber security in Indonesia is still very low. This is evidenced by data published by the International Communication Union (ITU), where the level of cyber security in Indonesia is ranked 70th. This states that Indonesia is very vulnerable to cyber attacks from hackers in other countries. In addition, according to data from treat exposure rate (TER), Indonesia has an attack vulnerability rate of malware by 23.54%(Ashari, 2020) .  \n(Patterson et al., 2023) Stated that any organization must think about the essence of cyber security. This is due to the increasing number of attack cases, which must be able to be resisted by the knowledge of cyber security because it is already concerned with data privacy and infrastructure resilience issues. This can be seen from the research conducted by (Singelton et al., 2021) In 2021, ransomware attacks were used against 10 different types of companies, accounting for an average of 17.4% of the total types of attacks that occurred in these companies.  \nOne malware currently trending to be used as an attack tool is Shellcode. Using Shellcodes in cyber attacks has become a trend among hackers. Shellcode can carry out illegal activities such as DoS attacks, data theft, and automatic system destruction on the destination computer. (Yang et al., 2022) Shellcode is code designed to perform its tasks automatically. It can grant an attacker permission to exploit the destination computer thoroughly. Shellcode is generally produced using assembly.  \nThe basic structure of the Shellcode is as follows: The first is No Operation Instructions (NOP Sled). NOPsledis used to ensure that the execution does not fail. Then, Bootstrap Code is used to set up the execution environment. Bootstrap code usually consists ofa value code register used by the payload. Next is payload. The payload used to perform the main tasks of a hacker depends on the code written in it. The latter is the clean-up code. Clean-up code removes traces from hackers after attacking the target system. This can make Shellcode and can provide performance from Shellcode in executing cleanly. Inside Shellcode, There are several functions referred to as root shell. It was the most widely used before some further developments. (Anley et al., 2007)(Niiranen, 2021) .  \nShellcode is developed with many tools that have their functions. Functions of the tools used to create Shellcode consist of tool","cbCaidMKWtvKbGPl","https://ap.wps.com/l/cbCaidMKWtvKbGPl","pdf",369219,1,12,"English","en",105,"# Introduction\n## Background and cyber security context\n## Malware and Shellcode overview\n## Shellcode structure and toolchain\n## Related work\n## Proposed approach and dataset\n# Methodology and experiments\n## Models: KNN, Decision Tree, Naïve Bayes\n## Training/testing with UNSW_NB15\n# Results and discussion\n## Classification performance and accuracy","[{\"question\":\"Why is cyber security important in this study?\",\"answer\":\"Cyber security is emphasized because malware and Shellcode can seriously disrupt or damage devices, and detection is not yet widely developed. The paper links low awareness and rising attack cases to the need for stronger detection techniques.\"},{\"question\":\"What dataset is used for training and testing?\",\"answer\":\"The experiments use the UNSW_NB15 dataset to derive the training and testing data for the malware classification tasks.\"},{\"question\":\"Which classifiers are implemented and how do they perform?\",\"answer\":\"K-Nearest Neighbour, Decision Tree, and Naïve Bayes are implemented. Their reported accuracy levels are 96.82%, 97.08%, and 63.43%, respectively.\"}]","Shellcode Classification with Machine Learning - Based on Binary Classification | PDF",1785683634,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"shellcode-classification-with-machine-learning-based-on-binary-classification","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/shellcode-classification-with-machine-learning-based-on-binary-classification/118443/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is cyber security important in this study?","Question",{"text":76,"@type":77},"Cyber security is emphasized because malware and Shellcode can seriously disrupt or damage devices, and detection is not yet widely developed. The paper links low awareness and rising attack cases to the need for stronger detection techniques.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset is used for training and testing?",{"text":81,"@type":77},"The experiments use the UNSW_NB15 dataset to derive the training and testing data for the malware classification tasks.",{"name":83,"@type":74,"acceptedAnswer":84},"Which classifiers are implemented and how do they perform?",{"text":85,"@type":77},"K-Nearest Neighbour, Decision Tree, and Naïve Bayes are implemented. 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