Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.

An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware.These malicious applications have become a serious concern to the security of Android systems.To address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples.However,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification results.The current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training time.The proposed approach consists of two main steps.First,a feature selection method based on the Attention mechanism is used to select the most important features.Then,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the malware.The feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural network.The role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification process.Experimental evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.

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.

An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware variants.On the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based classifiers.Commercial antivirus systems,in particular,have been identified as vulnerable to such AEs.This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers.Our attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack research.By directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack studies.Being a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based classifiers.Also,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality verification.Through thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware detectors.Furthermore,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense model.Through several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation techniques.In conclusion,our defense model alleviates the limitation of the most promising defense method,adversarial training,which is only effective against the AEs that are included in the training classifiers.

An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

Various organizations store data online rather than on physical servers.As the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also increases.Different researchers worked on different algorithms to protect cloud data from replay attacks.None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack.This study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay attacks.The program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original text.In the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the backend.This mechanism has the benefit of enhancing the detectability of replay attacks.Nevertheless,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy is.At the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

MoBShield:A Novel XML Approach for Securing Mobile Banking

Mobile banking security has witnessed significant R&D attention from both financial institutions and academia.This is due to the growing number of mobile baking applications and their reachability and usefulness to society.However,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking information.Related literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security functionality.In this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training datasets.The dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission requests.Further,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s behaviour.Performance evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false positives.Specifically,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art approaches.These promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking applications.Consequently,MoBShield has the potential to significantly enhance the security and trustworthiness of mobile banking platforms,mitigating the risks posed by cyber threats and ensuring a safer user experience.

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.

Covalent Bond Based Android Malware Detection Using Permission and System Call Pairs

The prevalence of smartphones is deeply embedded in modern society,impacting various aspects of our lives.Their versatility and functionalities have fundamentally changed how we communicate,work,seek entertainment,and access information.Among the many smartphones available,those operating on the Android platform dominate,being the most widely used type.This widespread adoption of the Android OS has significantly contributed to increased malware attacks targeting the Android ecosystem in recent years.Therefore,there is an urgent need to develop new methods for detecting Android malware.The literature contains numerous works related to Android malware detection.As far as our understanding extends,we are the first ones to identify dangerous combinations of permissions and system calls to uncover malicious behavior in Android applications.We introduce a novel methodology that pairs permissions and system calls to distinguish between benign and malicious samples.This approach combines the advantages of static and dynamic analysis,offering a more comprehensive understanding of an application’s behavior.We establish covalent bonds between permissions and system calls to assess their combined impact.We introduce a novel technique to determine these pairs’Covalent Bond Strength Score.Each pair is assigned two scores,one for malicious behavior and another for benign behavior.These scores serve as the basis for classifying applications as benign or malicious.By correlating permissions with system calls,the study enables a detailed examination of how an app utilizes its requested permissions,aiding in differentiating legitimate and potentially harmful actions.This comprehensive analysis provides a robust framework for Android malware detection,marking a significant contribution to the field.The results of our experiments demonstrate a remarkable overall accuracy of 97.5%,surpassing various state-of-the-art detection techniques proposed in the current literature.

Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.

A Low Complexity ML-Based Methods for Malware Classification

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware identification.Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware classification.This technique optimizes the model’s performance and reduces computational requirements.The proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature vector.Through the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate classification.The evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced features.This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time.The method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and prediction.The new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and efficiency.The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained devices.Novelty and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

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.

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.

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.

Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

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.

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.

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.

Malware Attacks Detection in IoT Using Recurrent Neural Network(RNN)

IoT(Internet of Things)devices are being used more and more in a variety of businesses and for a variety of tasks,such as environmental data collection in both civilian and military situations.They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power.In this study,we investigate the possibility of detecting IoT malware using recurrent neural networks(RNNs).RNNis used in the proposed method to investigate the execution operation codes of ARM-based Internet of Things apps(OpCodes).To train our algorithms,we employ a dataset of IoT applications that includes 281 malicious and 270 benign pieces of software.The trained model is then put to the test using 100 brand-new IoT malware samples across three separate LSTM settings.Model exposure was not previously conducted on these samples.Detecting newly crafted malware samples with 2-layer neurons had the highest accuracy(98.18%)in the 10-fold cross validation experiment.A comparison of the LSTMtechnique to other machine learning classifiers shows that it yields the best results.

An Agent Based Model for Ransomware Detection and Mitigation in a Cloud System

The increasing trend toward dematerialization and digitalization has prompted a surge in the adoption of IT service providers, offering cost-effective alternatives to traditional local services. Consequently, cloud services have become prevalent across various industries. While these services offer undeniable benefits, they face significant threats, particularly concerning the sensitivity of the data they handle. Many existing mathematical models struggle to accurately depict the complex scenarios of cloud systems. In response to this challenge, this paper proposes a behavioral model for ransomware propagation within such environments. In this model, each component of the environment is defined as an agent responsible for monitoring the propagation of malware. Given the distinct characteristics and criticality of these agents, the impact of malware can vary significantly. Scenario attacks are constructed based on real-world vulnerabilities documented in the Common Vulnerabilities and Exposures (CVEs) through the National Vulnerability Database. Defender actions are guided by an Intrusion Detection System (IDS) guideline. This research aims to provide a comprehensive framework for understanding and addressing ransomware threats in cloud systems. By leveraging an agent- based approach and real-world vulnerability data, our model offers valuable insights into detection and mitigation strategies for safeguarding sensitive cloud-based assets.

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.