A Survey on Machine Learning Approach to Detect Malware
نویسندگان
چکیده
منابع مشابه
Malware Detection and Evasion with Machine Learning Techniques: A Survey
Malware has become a powerful and sophisticated tool used by malicious users to compromise and harm systems, and its evasion ability has improved considerably, getting to the point of becoming completely undetectable. On the other hand, machine learning has evolved tremendously in last years and it has become a standard in many IT solutions including the data processing field. Likewise, cryptog...
متن کاملAn Approach to Malware Analysis and Reporting by Machine Learning
Now-a-days cyber security is major concern information and related security areas to protect data from threat. In this context, we frame a methodology by using machine learning to detect various types of Malwares by using machine learning methods. In the present work, we use machine learning approach to the various static and dynamic analysis techniques to discuss different algorithms in cyber ...
متن کاملUsing a Personalized Machine Learning Approach to Detect Stolen Phones
We the undersigned committee hereby approve the attached thesis, " Using a Personalized Machine Learning Approach to Detect Stolen Phones " by Huizhong Hu. With the increasing number of smartphone penetration, mining smartphone data that make smartphone smarter became a top research area, there are a lot of event data which we can use to predict behavior or detect anomalies. The privacy disclos...
متن کاملMalware and Machine Learning
Malware analysts use Machine Learning to aid in the fight against the unstemmed tide of new malware encountered on a daily, even hourly, basis. The marriage of these two fields (malware and machine learning) is a match made in heaven: malware contains inherent patterns and similarities due to code and code pattern reuse bymalware authors; machine learning operates by discovering inherent patter...
متن کاملEvading Machine Learning Malware Detection
Machine learning is a popular approach to signatureless malware detection because it can generalize to never-beforeseen malware families and polymorphic strains. This has resulted in its practical use for either primary detection engines or supplementary heuristic detections by anti-malware vendors. Recent work in adversarial machine learning has shown that models are susceptible to gradient-ba...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Turkish Journal of Computer and Mathematics Education (TURCOMAT)
سال: 2021
ISSN: 1309-4653
DOI: 10.17762/turcomat.v12i2.1961