Review of Hybrid Analysis Technique for Malware Detection

Yunus, Yus Kamalrul Bin Mohamed; Ngah, Syahrulanuar Bin

doi:10.1088/1757-899x/769/1/012075

Cited by 10 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A signature is a group of characters that may be used to recognize certain viruses is a file hash or a hash of bytes. [8] Consequently, this technique has a low false positive (FP) rate. But in this type we cannot detect new species or species that change their signature.…”

Section: Detection Methodsmentioning

confidence: 99%

A Survey on Malware Attacks Analysis and Detected

M Jameel,

M. T. Jawhar

2023

IRJIET

View full text Add to dashboard Cite

Malware is one of the biggest problems modern internet users face. Private data and pricey computing resources are seriously threatened by the rise in malware attacks. Anti-malware businesses rely on signatures, which do in fact involve regular expressions and strings, to find malware and its related families. Recent malware attacks in recent years have demonstrated that signature-based techniques are error-prone and easily avoided by sophisticated malware programs. This essay provides an introductory overview of malware and analysis techniques used, as well as detection techniques used by researchers.

show abstract

Section: Detection Methodsmentioning

confidence: 99%

A Survey on Malware Attacks Analysis and Detected

M Jameel,

M. T. Jawhar

2023

IRJIET

View full text Add to dashboard Cite

show abstract

“…Malware merupakan suatu istilah yang merujuk pada suatu perangkat lunak yang dapat merusak data, informasi, atau sistem komputer. Malware dapat dimanfaatkan untuk kejahatan dengan memasuki sistem tanpa otorisasi administrator komputer atau pengguna sistem tersebut seperti menyerang jaringan, merusak infrastruktur vital, membahayakan komputer dan perangkat pintar serta mencuri data sensitif [1] [2].…”

Section: Pendahuluanunclassified

Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware

Muhamad Malik Matin

2023

JURNAL MULTIMEDIA NETWORKING INFORMATICS

View full text Add to dashboard Cite

Random forest merpuakan algoritma machine learning yang populer digunakan untuk klasifikasi. Dalam mendeteksi malware, Random forest dapat membantu mengidentifikasi malware dengan akurasi yang baik. Namun, untuk meningkatkan performa model, diperlukan proses hyperparameter tuning. GridsearchCV adalah metode hyperparameter tuning yang memungkinkan pengguna untuk melakukan pemindaian pada sejumlah hyperparameter yang dipilih. Dalam paper ini, kami melakukan eksperimen dengan menggunakan GridsearchCV untuk melakukan hyperparameter tuning pada Random forest untuk tugas deteksi malware. Hasil eksperimen menunjukkan bahwa dengan melakukan hyperparameter tuning dapat meningkatkan akurasi model dalam mengidentifikasi malware

show abstract

“…On the other hand, dynamic analysis methods have higher time and memory exhaustion with a higher detection ratio besides being better resistant to code obfuscation algorithms [20]. For that particular reason, researchers employ a hybrid approach by merging both static and dynamic analysis to solve the malware problem while maintaining reasonable computational resources [21]- [23].…”

Section: Introductionmentioning

confidence: 99%