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Reviewer Integration and Performance Measurement for Malware Detection [article]

Brad Miller, Alex Kantchelian, Michael Carl Tschantz, Sadia Afroz, Rekha Bachwani, Riyaz Faizullabhoy, Ling Huang, Vaishaal Shankar, Tony Wu, George Yiu, Anthony D. Joseph, J. D. Tygar
2016 arXiv   pre-print
We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource.  ...  We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points.  ...  Detection System Evaluation In this section we evaluate our malware detection system and the impact of reviewer integration.  ... 
arXiv:1510.07338v2 fatcat:kr6r3uocrjgulcfwme4gxenyi4

Reviewer Integration and Performance Measurement for Malware Detection [chapter]

Brad Miller, Alex Kantchelian, Michael Carl Tschantz, Sadia Afroz, Rekha Bachwani, Riyaz Faizullabhoy, Ling Huang, Vaishaal Shankar, Tony Wu, George Yiu, Anthony D. Joseph, J. D. Tygar
2016 Lecture Notes in Computer Science  
We present and evaluate a large-scale malware detection system integrating machine learning with expert reviewers, treating reviewers as a limited labeling resource.  ...  We find that using training labels obtained well after samples appear, and thus unavailable in practice for current training data, inflates measured detection by almost 20 percentage points.  ...  Detection System Evaluation In this section we evaluate our malware detection system and the impact of reviewer integration.  ... 
doi:10.1007/978-3-319-40667-1_7 fatcat:fwmxfmjtgneblbsa3zcbkcc4pe

Sisyfos: A Modular and Extendable Open Malware Analysis Platform

Dimitrios Serpanos, Panagiotis Michalopoulos, Georgios Xenos, Vasilios Ieronymakis
2021 Applied Sciences  
We present the structure and implementation of Sisyfos, which accommodates analysis for Windows, Linux and Android malware.  ...  Sisyfos is a modular and extensible platform for malware analysis; it addresses multiple operating systems, including critical infrastructure ones.  ...  constitute fundamental infrastructure for the detection and mitigation of malware.  ... 
doi:10.3390/app11072980 fatcat:kvk6iffmdvd3xalvrjfcoxpzxa

Dynamic Analysis for IoT Malware Detection with Convolution Neural Network model

Jueun Jeon, Jong Hyuk Park, Young-Sik Jeong
2020 IEEE Access  
This paper proposes a dynamic analysis for IoT malware detection (DAIMD) to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently.  ...  DAIMD performs dynamic analysis on IoT malware in a nested cloud environment to extract behaviors related to memory, network, virtual file system, process, and system call.  ...  Section 2 reviews existing research methods for analyzing and detecting IoT malware. Section 3 describes the DAIMD proposed in this paper.  ... 
doi:10.1109/access.2020.2995887 fatcat:sjch2uh54ja2xapedtkwddwgiq

Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification

Altyeb Taha, Omar Barukab, Sharaf Malebary
2021 Mathematics  
This makes effective detection of Android malware apps a difficult problem and important issue.  ...  The proposed approach utilizes the Choquet fuzzy integral as an aggregation function for the purpose of combining and integrating the classification results of several classifiers such as XGBoost, Random  ...  Section 2 presents the related work on Android malware detection. Section 3 presents the fuzzy measures and Choquet integral.  ... 
doi:10.3390/math9222880 fatcat:svmv3ppkifgg3eqfohxp3h462q

Identifying Malware Fraud Detection in Web Application using Content Integrity Verification

SONTELA KURUBA DINESH, SONTELA KURUBA DINESH, C. GOVARDHAN
2019 International Journal of Recent Trends in Engineering and Research  
, to detect both malware and apps subjected to search rank fraud.  ...  FairPlay also helped the discovery of more than 1,000 reviews,reported for 193 apps that reveala new type of" coercive" review campaign: users areharassed into writing positive reviews,and install and  ...  The RFmodule exploits this observation through a two step approach:(i) detect and filter out fraudulent reviews, then (ii) identify malware and fraud indicative feedback from there maining reviews.  ... 
doi:10.23883/ijrter.2019.5079.zjrff fatcat:hz2ayjnndzaafdm4qemjqtmk4e

Editorial for Special Issue Detecting Attack and Incident Zone System

Christoforos Ntantogian
2021 Information  
Attackers who have a strong motivation to succeed in their nefarious goals are often able to breach the security of their targets and cause havoc [...]  ...  The work in [4] , "A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection", is a review paper that schematizes contemporary machine learning-based mobile malware detection  ...  Let us place in this context the papers of this Special Issue, which were accepted, after a careful peer-review process, for publication in the Special Issue, "Detecting Attack and Incident Zone System  ... 
doi:10.3390/info12090382 fatcat:mlg2qykmjjh4jnkdrdozuunzpu

Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis

Rami Sihwail, Khairuddin Omar, Khairul Akram Zainol Ariffin, Sanad Al Afghani
2019 Applied Sciences  
The need to detect malware before it harms computers, mobile phones and other electronic devices has caught the attention of researchers and the anti-malware industry for many years.  ...  This paper proposes an integrated malware detection approach that applies memory forensics to extract malicious artifacts from memory and combines them to features extracted during the execution of malware  ...  Acknowledgments: The authors acknowledge the Deanship of Scientific Research at King Faisal University for the financial support under Nasher Track (Grant No. 186256).  ... 
doi:10.3390/app9183680 fatcat:cqon6qrfqzbstifljt5mcqevhe

SMASH: A Malware Detection Method Based on Multi-feature Ensemble Learning

Yusheng Dai, Hui Li, Yekui Qian, Ruipeng Yang, Min Zheng
2019 IEEE Access  
and hardware performance counters.  ...  The existing malware dynamic detection methods are vulnerable to evasion attacks. For this situation, we propose a malware dynamic detection method based on mufti-feature ensemble learning.  ...  ACKNOWLEDGMENT The authors would like to thank the editor and the anonymous referees for their constructive comments.  ... 
doi:10.1109/access.2019.2934012 fatcat:dckk42yap5hkvbjqig7jeekn7e

A Comprehensive Review on Malware Detection Approaches

Omer Aslan, Refik Samet
2020 IEEE Access  
On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches  ...  This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches.  ...  Narayanan et al. proposed a MKLDROID, a unified framework for Android that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localization  ... 
doi:10.1109/access.2019.2963724 fatcat:ecckbq7ylzbepgl5az5qfupyxi

Comprehensive Analysis of IoT Malware Evasion Techniques

A. Al-Marghilani
2021 Engineering, Technology & Applied Science Research  
The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security.  ...  This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches.  ...  Mitigations for the identified threat incidents are yet to be integrated and automated.  ... 
doi:10.48084/etasr.4296 fatcat:hyfkdspwizce3cyeu6erygpqai

A Framework for Generating Malware Threat Intelligence

Ekta Gandotra, Divya Bansal, Sanjeev Sofat
2017 Scalable Computing : Practice and Experience  
This paper purposes a design of a framework for generating Malware Threat Intelligence that can analyze, identify and predict the malware threats and can act as an Early Warning System (EWS).  ...  Malware writers are making use of obfuscation techniques like insertion of dead code, subroutine reordering, instruction substitution etc. for creating polymorphic and metamorphic malware [4] .  ...  So, a hybrid technique integrating both static attributes and dynamic behaviors is required for better malware detection and classification.  ... 
doi:10.12694/scpe.v18i3.1300 fatcat:dhrm6hm33jaz3jcdkwged4g2cu

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Hadeel Alrubayyi, Gokop Goteng, Mona Jaber, James Kelly
2021 Journal of Sensor and Actuator Networks  
In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks.  ...  Most of these algorithms imitate the human's body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge.  ...  For this reason, we dedicate this review paper to investigating and analyzing the AIS methods in detecting malware files in the IoT.  ... 
doi:10.3390/jsan10040061 doaj:67bbb1ca47c840ab9b269638b4ee8d98 fatcat:nnihfntrprf6va4whrzj6edssy

Network Malware Detection using Soft Computing and Machine Learning Techniques

2019 International Journal of Engineering and Advanced Technology  
In this paper, we analyze usefulness of Soft Computing and Machine Learning Techniques for network malware detection.  ...  Malware detection is an important area for research in effective and secure functioning of computer networks. Research efforts are required to protect the systems from various security attacks.  ...  LITERATURE REVIEW There are various techniques and approaches available for network malware detection system in literature which is shown in below section.  ... 
doi:10.35940/ijeat.a1654.129219 fatcat:kohk2rx6draa5cgw2en4swdyiu

Defending medical information systems against malicious software

David E Gobuty
2004 Excerpta Medica: International Congress Series  
This white paper informs both vendors (manufacturers and integrators of MedIS) and users (for example, hospitals and medical practices) about possible malware attacks and suggests ways to protect against  ...  To this aim this white paper informs both vendors (manufacturers and integrators of MedIS) and users (for example, hospitals and medical practices) about possible malware attacks and suggests ways to protect  ... 
doi:10.1016/j.ics.2004.03.046 fatcat:gorcxb2c5bekta3eyzu74krvtm
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