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DroidEnemy: Battling adversarial example attacks for Android Malware detection

Digital Communications and Networks 2022年第6期8卷 1040-1047页

作者： Neha Bala Aemun Ahmar Wenjia Li Fernanda Tovar Arpit Battu Prachi Bambarkar Department of Computer Science New York Institute of TechnologyNew YorkNY10023USA

In recent years,we have witnessed a surge in mobile devices such as smartphones,tablets,smart watches,etc.,most of which are based on the Android operating system.However,because these Android-based mobile devices are becoming increasingly popular,they are now the primary target of mobile malware,which could lead to both privacy leakage and property loss.To address the rapidly deteriorating security issues caused by mobile malware,various research efforts have been made to develop novel and effective detection mechanisms to identify and combat them.Nevertheless,in order to avoid being caught by these Malware detection mechanisms,malware authors are inclined to initiate adversarial example attacks by tampering with mobile applications.In this paper,several types of adversarial example attacks are investigated and a feasible approach is proposed to fight against them.First,we look at adversarial example attacks on the Android system and prior solutions that have been proposed to address these attacks.Then,we specifically focus on the data poisoning attack and evasion attack models,which may mutate various application features,such as API calls,permissions and the class label,to produce adversarial examples.Then,we propose and design a Malware detection approach that is resistant to adversarial examples.To observe and investigate how the Malware detection system is influenced by the adversarial example attacks,we conduct experiments on some real Android application datasets which are composed of both malware and benign applications.Experimental results clearly indicate that the performance of Android Malware detection is severely degraded when facing adversarial example attacks.

关键词： Security Malware detection Adversarial example attack Data poisoning attack Evasi on attack Machine learning Android

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A Novel Framework for Windows Malware detection Using a Deep Learning Approach

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Computers, Materials & Continua 2022年第7期72卷 461-479页

作者： Abdulbasit A.Darem Northern Border University Arar9280Saudi Arabia

Malicious software(malware)is one of the main cyber threats that organizations and Internet users are currently facing.malware is a software code developed by cybercriminals for damage purposes,such as corrupting the system and data as well as stealing sensitive data.The damage caused by malware is substantially increasing every day.There is a need to detect malware efficiently and automatically and remove threats quickly from the systems.Although there are various approaches to tackle malware problems,their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks.The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware.In this paper,a novel approach based on deep learning for detecting malware proposed.Furthermore,the proposed approach deploys novel feature selection,feature co-relation,and feature representations to significantly reduce the feature space.The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families.It achieved 96.01%accuracy and outperformed the existing techniques of Malware detection.

关键词： Malware detection malware analysis deep learning feature extraction feature selection cyber security

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware detection

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Computer Systems Science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and cybercrime.Such malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,etc.Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of money.To prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)algorithm.This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware detection(LBAAA-OMLMD)approach in Computer Networks.The presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the network.To accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality problems.Besides,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is applied.The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results.The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct measures.The comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： Computer networks machine learning security Malware detection feature selection ransomware

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An Android Malware detection System Based on Feature Fusion

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Chinese Journal of Electronics 2018年第6期27卷 1206-1213页

作者： LI Jian WANG Zheng WANG Tao TANG Jinghao YANG Yuguang ZHOU Yihua School of Computer Science Beijing University of Posts and Telecommunications School of Computer Science Beijing University of Technology

In order to improve the detection efficiency of Android malicious application, an Android Malware detection system based on feature fusion is proposed on three levels. Feature fusion especially emphasizes on ten categories, which combines static and dynamic features and includes 377 features for classification. In order to improve the accuracy of Malware detection, attribute subset selection and principle component analysis are used to reduce the dimensionality of fusion features. Random forest is used for classification. In the experiment, the dataset includes 43,822 benign applications and 8,454 malicious applications. The method can achieve 99.4% detection accuracy and 0.6% false positive rate. The experimental results show that the detection method can improve the Malware detection efficiency in Android platform.

关键词： Android security Malware detection Feature fusion Machine learning Information security

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An Effective Memory Analysis for Malware detection and Classification

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Computers, Materials & Continua 2021年第5期67卷 2301-2320页

作者： Rami Sihwail Khairuddin Omar Khairul Akram Zainol Ariffin Department of Information Science&Technology Universiti Kebangsaan MalaysiaSelangor43600Malaysia

The study of malware behaviors,over the last years,has received tremendous attention from researchers for the purpose of reducing malware risks.Most of the investigating experiments are performed using either static analysis or behavior analysis.However,recent studies have shown that both analyses are vulnerable to modern malware files that use several techniques to avoid analysis and detection.Therefore,extracted features could be meaningless and a distraction for malware analysts.However,the volatile memory can expose useful information about malware behaviors and characteristics.In addition,memory analysis is capable of detecting unconventional malware,such as in-memory and fileless malware.However,memory features have not been fully utilized yet.Therefore,this work aims to present a new Malware detection and classification approach that extracts memory-based features from memory images using memory forensic techniques.The extracted features can expose the malware’s real behaviors,such as interacting with the operating system,DLL and process injection,communicating with command and control site,and requesting higher privileges to perform specific tasks.We also applied feature engineering and converted the features to binary vectors before training and testing the classifiers.The experiments show that the proposed approach has a high classification accuracy rate of 98.5%and a false positive rate as low as 1.24%using the SVM classifier.The efficiency of the approach has been evaluated by comparing it with other related works.Also,a new memory-based dataset consisting of 2502 malware files and 966 benign samples forming 8898 features and belonging to six memory types has been created and published online for research purposes.

关键词： Cybersecurity feature selection machine learning malware dataset Malware detection memory analysis memory features

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Clustering-Aided Supervised Malware detection with Specialized Classifiers and Early Consensus

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计算机、材料和连续体（英文） 2023年第4期75卷 1235-1251页

作者： Murat Dener Sercan Gulburun Information Security Engineering Graduate School of Natural and Applied SciencesGazi UniversityAnkara06560Turkey

One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for malware Analysis(BODMAS),Elastic malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle Malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle Malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average pre

关键词： Malware detection ensemble learning classification clustering specialized classifier early consensus

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Malware detection in Android IoT Systems Using Deep Learning

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Computers, Materials & Continua 2023年第2期74卷 4399-4415页

作者： Muhammad Waqar Sabeeh Fareed Ajung Kim Saif Ur Rehman Malik Muhammad Imran Muhammad Usman Yaseen Department of Computer Science COMSATS UniversityIslamabad45550Pakistan Department of Optical Engineering Sejong UniversitySeoul143747Korea Cybernetica AS Tallinn12618Estonia

The Android Operating System(AOS)has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things(IoT).Due to the high popularity and reliability ofAOS for IoT,it is a target of many cyber-attacks which can cause compromise of privacy,financial loss,data integrity,unauthorized access,denial of services and so on.The Android-based IoT(AIoT)devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market.Recently,several detection preventive malwares are developed to conceal their malicious activities from analysis tools.Hence,conventional Malware detection techniques could not be applied and innovative countermeasures against such anti-detection malwares are indispensable to secure the AIoT.In this paper,we proposed the novel deep learning-based real-time multiclass Malware detection techniques for the AIoT using dynamic analysis.The results show that the proposed technique outperforms existing Malware detection techniques and achieves detection accuracy up to 99.87%.

关键词： Android cyber security deep learning internet of things Malware detection

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Variable-length sequential dynamic features-based Malware detection

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High Technology Letters 2016年第4期22卷 362-367页

作者：杜冬高 Li Gaochao Ma Yan Network and Information Center Institute of Network TechnologyBeijing University of Posts and Telecommunications National Computer Network Emergency Response Technical Team/Coordination Center of China Institute of Information Engineering Chinese Academy of Sciences Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications

In order to solve the problem that traditional signature-based Malware detection systems are inefficacious in detecting new malware,a practical Malware detection system is constructed to find out new malware. Application programming interface( API) call sequence is introduced to capture activities of a program in this system. After that,based on variable-length n-gram,API call order can be extracted from API call sequence as the malicious behavior feature of a software. Compared with traditional methods,which use fixed-length n-gram,the solution can find more new malware. The experimental results show that the presented approach improves the accuracy of Malware detection.

关键词： application programming interface(API) call order variable-length n-gram Malware detection

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Graph Convolutional Neural Network Based Malware detection in IoT-Cloud Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 2897-2914页

作者： Faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of cyberattacks.The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process.Furthermore,Android malware is increasing on a daily basis.So,precise Malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile devices.In this research article,an optimal Graph Convolutional Neural Network-based Malware detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud environment.The pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud platforms.The presented OGCNN-MDC model has three stages in total,such as data pre-processing,Malware detection and para-meter tuning.To detect and classify the malware,the GCNN model is exploited in this work.In order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关键词： Cybersecurity IoT cloud Malware detection graph convolution network

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Malware detection Using Decision Tree Based SVM Classifier for IoT

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Computers, Materials & Continua 2022年第7期72卷 713-726页

作者： Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K.Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The development in Information and Communication Technology has led to the evolution of new computing and communication environment.Technological revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart devices.One of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through internet.IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data.With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data.This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT.The proposed Machine Learning based Block Chain Technology Malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and blockchain.Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight.Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes.Furthermore,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy.ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

关键词： Blockchain Malware detection classification feature selection internet of medical things

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