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A Novel Approach for Predicting the Malware Attacks

Ekta Rokkathapa, Soumen Kanrar
2019 International Journal of Computer Applications  
Malware means malicious software. Detecting malware over a system is malware analysis. It consists of two parts static analysis and dynamic analysis.  ...  In this paper, we have proposed a framework for malware analysis based on semi automated malware detection usually machine learning which is based on dynamic malware detection .  ...  We have proposed a learning process for multiple classifier f1fk trained for different monitoring length T1Tk. Malware detection system learns in real time for the best classifer.  ... 
doi:10.5120/ijca2019918585 fatcat:sprkqyhjw5ezllaai2h3nn66zq

Android Malware Detection through Machine Learning Techniques: A Review

Abikoye Oluwakemi Christiana, Benjamin Aruwa Gyunka, Akande Noah
2020 International Journal of Online and Biomedical Engineering (iJOE)  
Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based.  ...  The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown  ...  This system was able to perform both statistical and dynamical analysis to automatically detect suspicious applications.  ... 
doi:10.3991/ijoe.v16i02.11549 fatcat:hurc3snq6zgzzmrbnhvzafxvxu

Differentiating malware from cleanware using behavioural analysis

Ronghua Tian, Rafiqul Islam, Lynn Batten, Steve Versteeg
2010 2010 5th International Conference on Malicious and Unwanted Software  
We also propose, as an alternative to the traditional method of manually identifying malware files, an automated classification system using runtime features of malware files.  ...  Our experimental results, based on a dataset of 1368 malware and 456 cleanware files, provide an accuracy of over 97% in distinguishing malware from cleanware.  ...  Our contributions to the field of malware detection based on behavioural feature analysis are fourfold.  ... 
doi:10.1109/malware.2010.5665796 dblp:conf/malware/TianIBV10 fatcat:akomhuffnvfrvfgfbfisum7kcy

Malware Classification Based on the Behavior Analysis and Back Propagation Neural Network

Zhi-Peng Pan, Chao Feng, Chao-Jing Tang, T. Gong, T. Yang, J. Xu
2016 ITM Web of Conferences  
In this paper, we propose an automated malware classification approach based on the behavior analysis.  ...  (BP) Neural Network model.The experimental results demonstrate that our classification technique is able to classify the malware variants effectively and detect malware accurately.  ...  What we need is the ability to automatically categorize of the malware and detect the malware by its behavior.  ... 
doi:10.1051/itmconf/20160702001 fatcat:zvqj2t4zrrfjldljdy5retlfza

Malware Detection, Supportive Software Agents and Its Classification Schemes

Adebayo Olawale Surajudeen
2012 International journal of network security and its applications  
Over time, the task of curbing the emergence of malware and its dastard activities has been identified in terms of analysis, detection and containment of malware.  ...  The research also describes tools that classify malware dataset using a rule-based classification scheme and machine learning algorithms to detect the malicious program from normal program through pattern  ...  The techniques of malware detection can also be classified as Signature-based malware detection, Specification-based malware detection and Behaviour-based detection.  ... 
doi:10.5121/ijnsa.2012.4603 fatcat:ep3a3uydffdnrmhybtoqhuplfq

Use of Data Visualisation for Zero-Day Malware Detection

Sitalakshmi Venkatraman, Mamoun Alazab
2018 Security and Communication Networks  
The aim of the paper is twofold: (1) to provide a comprehensive overview of the existing visualisation techniques for detecting suspicious behaviour of systems and (2) to design a novel visualisation using  ...  similarity matrix method for establishing malware classification accurately.  ...  There are limitations with texture-based image analysis of malware. Such an approach cannot be applied to analyse behaviour patterns for detecting obfuscated code changes.  ... 
doi:10.1155/2018/1728303 fatcat:fct3to3a6rc2hhlcswdu7kx3dy

Behavioural Analysis of Android Malware using Machine Learning

Lokesh Vaishanav
2017 International Journal Of Engineering And Computer Science  
It is posing benevolence challenges and difficulties to detect such malwares as signature based detection techniques available today are becoming inefficient in sensing new and anonymous malware.  ...  This paper summarizes the evolution of malware detection techniques based on machine learning algorithms focused on the android OS.  ...  classifiers, MADAM (Multi-Level Anomaly Detector for Android Malware, (HOSBAD) Host-based Anomaly Detection System, and MAMA (Manifest Analysis for Malware detection in Android) and last various machine  ... 
doi:10.18535/ijecs/v6i5.32 fatcat:3gneyfuy2fethm5dw6moltc6ce

Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors

Praveen Hugar, Mayur Pershad, T. Sathvika, Ganesh Bhukya
2022 International Journal of Innovative Research in Engineering & Management  
As a result, for the malware classification job, high-level abstractions and representations are automatically derived.  ...  For cyber-criminals to achieve their nefarious objectives and purposes, a variety of viruses has been widely deployed.  ...  Automatic and effective malware detection and classification systems, which play critical roles in preserving the security of operating systems and networks, are critical in combating large-scale malware  ... 
doi:10.55524/ijirem.2022.9.3.6 fatcat:z7tj4i2savbhbaxlumydwwck2u

Classification of Malware with MIST and N-Gram Features Using Machine Learning

Udayakumar Nandagopal, Vellore Institute of Technology, Subbulakshmi Thirumalaivelu, Vellore Institute of Technology
2021 International Journal of Intelligent Engineering and Systems  
In this work, inside the cuckoo sandbox malwares were executed for observing behavioural accomplishments of the each and every Malicious Application.  ...  But signature based technique failed to detect the new and unknown malwares, so signature based method failed to provide the better result.  ...  Much research has been done in the past for classification of malware based on dynamic and behavioural analysis.  ... 
doi:10.22266/ijies2021.0430.29 fatcat:gmc4gu6cmjfwvfv2jk7sl2fffm

MalClassifier: Malware family classification using network flow sequence behaviour

Bushra A. AlAhmadi, Ivan Martinovic
2018 2018 APWG Symposium on Electronic Crime Research (eCrime)  
Considering the limitations of existing malware analysis and classification methods, we present MalClassifier, a novel privacy-preserving system for the automatic analysis and classification of malware  ...  By mining and extracting the distinctive n-flows for each malware family, it automatically generates network flow sequence behaviour profiles.  ...  Host-based Malware Analysis and Family Classification.  ... 
doi:10.1109/ecrime.2018.8376209 dblp:conf/ecrime/AlAhmadiM18 fatcat:ozjd7wu2bja47jwjms3b7fnyoi

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Suhaib Jasim Hamdi, Naaman Omar, Adel AL-zebari, Karwan Jameel Merceedi, Abdulraheem Jamil Ahmed, Nareen O. M. Salim, Sheren Sadiq Hasan, Shakir Fattah Kak, Ibrahim Mahmood Ibrahim, Hajar Maseeh Yasin, Azar Abid Salih
2021 Asian Journal of Research in Computer Science  
So, there is a need for smart malware detection systems to reduce malicious activities risk.  ...  More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available.  ...  Also proposed a new classification method based on the findings of a longitudinal analysis on Android applications focusing on their complex behaviour.  ... 
doi:10.9734/ajrcos/2021/v11i330266 fatcat:fcaiwoexh5hk7dce6triq3eu5y

An Analysis of Malware Classification Technique by using Machine Learning

P. S., P. Venkateswara
2019 International Journal of Computer Applications  
s Feature fusion for effective Malware Family Classification system, Liu et al.'s Automatic Malware classification and detection system, Bashari et al.'  ...  s Malware classification and detection system using ANN. Ashu Sharma et al.'s Classification of advanced Malware system. Finally, we have done a comparative analysis of all the abovementioned methods.  ...  s Automatic Malware classification and Detection system used  ... 
doi:10.5120/ijca2019918355 fatcat:67kplmtkqjhibnf6d5x6yoawc4

The Android malware detection systems between hope and reality

Khaled Bakour, Halil Murat Ünver, Razan Ghanem
2019 SN Applied Sciences  
The achieved study includes more than 200 papers that have different goals such as apps' behaviour analysis, automatic user interface triggers or packer/unpacker frameworks development.  ...  The widespread use of Android-based smartphones made it an important target for malicious applications' developers.  ...  Furthermore, we used general terms such as behaviour analysis, anomaly detection, signature-based detection and apps classification.  ... 
doi:10.1007/s42452-019-1124-x fatcat:jzbb6ruykrcw3nuwps4qb4fuze

On Robust Malware Classifiers by Verifying Unwanted Behaviours [chapter]

Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik
2016 Lecture Notes in Computer Science  
Our approach integrates several methods: formal methods, machine learning and text mining techniques. It is the first to automatically generate unwanted behaviours for Android malware detection.  ...  We show that by taking the verification results against unwanted behaviours as input features, the classification performance of detecting new malware is improved dramatically.  ...  that using semantics-based features like unwanted behaviours dramatically improves the classification performance of new malware detection; − supply a static analysis tool to construct behaviour automata  ... 
doi:10.1007/978-3-319-33693-0_21 fatcat:vh26x3s6ija3difpypmazmenby

Malicious Code Invariance Based On Deep Learning

2021 International Journal of Information Technology Infrastructure  
The malicious code identification helps to identify the affected malware on the system. Malicious code theft data from our system and it yields high security issues in real time.  ...  The malicious code detection is critical task for in the field of security.  ...  Their work was based on the deep belief network (DBN) for automatic malware signature generation and classification.  ... 
doi:10.30534/ijiti/2021/011032021 fatcat:gslsmvwa7vezjhqqqetsttpfqa
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