Malware Detection Using Dual Siamese Network Model

This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber security.Traditional signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new malware.Recently,machine learning-based malware detection techniques,such as Convolutional Neural Networks(CNN)and Recurrent Neural Networks(RNN),have gained attention.While these methods demonstrate high performance by leveraging static and dynamic features,they are limited in detecting new malware or variants because they learn based on the characteristics of existing malware.To overcome these limitations,malware detection techniques employing One-Shot Learning and Few-Shot Learning have been introduced.Based on this,the Siamese Network,which can effectively learn from a small number of samples and perform predictions based on similarity rather than learning the characteristics of the input data,enables the detection of new malware or variants.We propose a dual Siamese network-based detection framework that utilizes byte images converted frommalware binary data to grayscale,and opcode frequency-based images generated after extracting opcodes and converting them into 2-gramfrequencies.The proposed framework integrates two independent Siamese network models,one learning from byte images and the other from opcode frequency-based images.The detection models trained on the different kinds of images generated separately apply the L1 distancemeasure to the output vectors themodels generate,calculate the similarity,and then apply different weights to each model.Our proposed framework achieved a malware detection accuracy of 95.9%and 99.83%in the experimentsusingdifferentmalware datasets.The experimental resultsdemonstrate that ourmalware detection model can effectively detect malware by utilizing two different types of features and employing the dual Siamese network-based model.

Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

DCEL:classifier fusion model for Android malware detection

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android malware.However,the current methods of Android malware detection need a lot of time in the feature engineering phase.Furthermore,these models have the defects of low detection rate,high complexity,and poor practicability,etc.We analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other attributes.We classify the software’s threat levels based on the correlation of features.Then,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware detection.First,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray image.Then,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single classifier.Experiments based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

MaliFuzz:Adversarial Malware Detection Model for Defending Against Fuzzing Attack

With the prevalence of machine learning in malware defense,hackers have tried to attack machine learning models to evade detection.It is generally difficult to explore the details of malware detection models,hackers can adopt fuzzing attack to manipulate the features of the malware closer to benign programs on the premise of retaining their functions.In this paper,attack and defense methods on malware detection models based on machine learning algorithms were studied.Firstly,we designed a fuzzing attack method by randomly modifying features to evade detection.The fuzzing attack can effectively descend the accuracy of machine learning model with single feature.Then an adversarial malware detection model MaliFuzz is proposed to defend fuzzing attack.Different from the ordinary single feature detection model,the combined features by static and dynamic analysis to improve the defense ability are used.The experiment results show that the adversarial malware detection model with combined features can deal with the attack.The methods designed in this paper have great significance in improving the security of malware detection models and have good application prospects.

Detection and Prevention of Malware in Android Mobile Devices: A Literature Review

Despite only being around for a few years, mobile devices have steadily risen to become the most extensively used computer devices. Given the number of people who rely on smartphones, which can install third-party apps, it has become an increasingly important issue for end-users and service providers to ensure that both the devices and the underlying network are secure. People will become more reliant on applications such as SMS, MMS, Internet Access, Online Transactions, and so on due to such features and capabilities. Thousands of devices ranging from low-cost phones to high-end luxury phones are powered by the Android operating system, which has dominated the smartphone marketplace. It is about making it possible for people from all socioeconomic backgrounds to get and use mobile devices in their daily activities. In response to this growing popularity, the number of new applications introduced to the Android market has skyrocketed. The recent appearance of a wide range of mobile malware has caught the attention of security professionals and scholars alike. In light of the ongoing expansion of the mobile phone industry, the likelihood of it being used in criminal activities will only continue to rise in the future. This article reviews the literature on malware detection and prevention in Android mobile devices, analyzes the existing literature on major studies and tasks, and covers articles, journals, and digital resources such as Internet security publications, scientific studies, and conferences.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Variable-length sequential dynamic features-based malware detection

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.

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Application Programming Interface(API)call feature analysis is the prominent method for dynamic android malware detection.Standard benchmark androidmalware API dataset includes featureswith high dimensionality.Not all features of the data are relevant,filtering unwanted features improves efficiency.This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance.In the first phase fuzzy benchmarking is used to select the top best features,and in the second phase meta-heuristic optimization algorithms viz.,Moth Flame Optimization(MFO),Multi-Verse Optimization(MVO)&Whale Optimization(WO)are run with Machine Learning(ML)wrappers to select the best from the rest.Five ML methods viz.,Decision Tree(DT),Random Forest(RF),K-NearestNeighbors(KNN),Naie Bayes(NB)&NearestCentroid(NC)are compared as wrappers.Several experiments are conducted and among them,the best post reduction accuracy of 98.34% is recorded with 95% elimination of features.The proposed novelmethod outperformed among the existing works on the same dataset.

Modern Mobile Malware Detection Framework Using Machine Learning and Random Forest Algorithm

With the high level of proliferation of connected mobile devices,the risk of intrusion becomes higher.Artificial Intelligence(AI)and Machine Learning(ML)algorithms started to feature in protection software and showed effective results.These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’malware,especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly.In this article,we present a framework for mobile malware detection based on a new dataset containing new categories of mobile malware.We focus on categories of malware that were not tested before by Machine Learning algorithms proven effective in malware detection.We carefully select an optimal number of features,do necessary preprocessing,and then apply Machine Learning algorithms to discover malicious code effectively.From our experiments,we have found that the Random Forest algorithm is the best-performing algorithm with such mobile malware with detection rates of around 99%.We compared our results from this work and found that they are aligned well with our previous work.We also compared our work with State-of-the-Art works of others and found that the results are very close and competitive.

A Survey on Visualization-Based Malware Detection

In computer security,the number of malware threats is increasing and causing damage to systems for individuals or organizations,necessitating a new detection technique capable of detecting a new variant of malware more efficiently than traditional anti-malware methods.Traditional antimalware software cannot detect new malware variants,and conventional techniques such as static analysis,dynamic analysis,and hybrid analysis are time-consuming and rely on domain experts.Visualization-based malware detection has recently gained popularity due to its accuracy,independence from domain experts,and faster detection time.Visualization-based malware detection uses the image representation of the malware binary and applies image processing techniques to the image.This paper aims to provide readers with a comprehensive understanding of malware detection and focuses on visualization-based malware detection.

An Effective Memory Analysis for Malware Detection and Classification

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.

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

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.

Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.

Android Malware Detection with Contrasting Permission Patterns

As the risk of malware is sharply increasing in Android platform,Android malware detection has become an important research topic.Existing works have demonstrated that required permissions of Android applications are valuable for malware analysis,but how to exploit those permission patterns for malware detection remains an open issue.In this paper,we introduce the contrasting permission patterns to characterize the essential differences between malwares and clean applications from the permission aspect Then a framework based on contrasting permission patterns is presented for Android malware detection.According to the proposed framework,an ensemble classifier,Enclamald,is further developed to detect whether an application is potentially malicious.Every contrasting permission pattern is acting as a weak classifier in Enclamald,and the weighted predictions of involved weak classifiers are aggregated to the final result.Experiments on real-world applications validate that the proposed Enclamald classifier outperforms commonly used classifiers for Android Malware Detection.

Towards improving detection performance for malware with a correntropy-based deep learning method

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT paradigm.Currently,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so far.However,there are also some problems.For example,considering the noise and outliers in the existing datasets of malware,some methods are not robust enough.Therefore,the accuracy of malware classification still needs to be improved.Aiming at this issue,we propose a novel method that combines the correntropy and the deep learning model.In our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of outliers.We present the detailed design process of our method.Furthermore,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

Wide dynamic detection range of methane gas based on enhanced cavity absorption spectroscopy

Integrated cavity output spectroscopy(ICOS) is an effective technique in trace gase detection.The strong absorption due to the long optical path of this method makes it challenging in the application scenes that have large gas concentration fluctuation,especially when the gas concentration is high.In this paper,we demonstrate an extension of the dynamic range of ICOS by using a detuned laser combined with an off-axis integrating cavity.With this,we improve the upper limit of the dynamic detection range from 0.1%(1000 ppm) to 20% of the gas concentration.This method provides a way of using ICOS in the applications with unpredictable gas concentrations such as gas leak detection,ocean acidification,carbon sequestration,etc.

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

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.

An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification

Machine learning(ML)is often used to solve the problem of malware detection and classification,and various machine learning approaches are adapted to the problem of malware classification;still acquiring poor performance by the way of feature selection,and classification.To address the problem,an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier“AFC-XG Boost”is presented in this paper.The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set.The model turns the XG Boost classifier in several stages to optimize performance.At preprocessing stage,the data set given has been noise removed,normalized and tamper removed using Feature Base Optimizer“FBO”algorithm.The FBO would normalize the data points,as well as perform noise removal according to the feature values and their base information.Similarly,the performance of standard XG Boost has been optimized by adapting the selection using Class Based Principle Component Analysis“CBPCA”algorithm,which performs the selection according to the fitness of any feature for different classes.Based on the selected features,the method generates a regression tree for each feature considered.Based on the generated trees,the method performs classification by computing the tree-level ensemble similarity‘TLES’and the class-level ensemble similarity‘CLES’.Using both methods calculates the value of the class match similarity‘CMS’based on which the malware has been classified.The proposed approach achieves 97% accuracy in malware detection and classification with the less time complexity of 34 s for 75000 samples.

An LSTM-Based Malware Detection Using Transfer Learning

Mobile malware occupies a considerable proportion of cyberattacks.With the update of mobile device operating systems and the development of software technology,more and more new malware keep appearing.The emergence of new malware makes the identification accuracy of existing methods lower and lower.There is an urgent need for more effective malware detection models.In this paper,we propose a new approach to mobile malware detection that is able to detect newly-emerged malware instances.Firstly,we build and train the LSTM-based model on original benign and malware samples investigated by both static and dynamic analysis techniques.Then,we build a generative adversarial network to generate augmented examples,which can emulate the characteristics of newly-emerged malware.At last,we use the augmented examples to retrain the 4th and 5th layers of the LSTM network and the last fully connected layer so that it can discriminate against newly-emerged malware.Actual experiments show that our malware detection achieved a classification accuracy of 99.94%when tested on augmented samples and 86.5%with the samples of newly-emerged malware on real data.

Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.