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Cognitively Inspired Three-Way Decision Making and Bi-Level Evolutionary Optimization for Mobile Cybersecurity Threats detection: A Case Study on android malware

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COGNITIVE COMPUTATION 2024年第6期16卷 3200-3227页

作者： Jerbi, Manel Dagdia, Zaineb Chelly Bechikh, Slim Ben Said, Lamjed Univ Tunis ISG CS Dept SMART Lab Tunis Tunisia Univ Luxembourg Fac Sci Technol & Med L-4364 Esch Sur Alzette Luxembourg Univ Tunis LARODEC ISG Tunis Tunisia Univ Paris Saclay DAVID UVSQ Paris France

Malicious apps use a variety of methods to spread infections, take over computers and/or IoT devices, and steal sensitive data. Several detection techniques have been proposed to counter these attacks. Despite the promising results of recent malware detection strategies, particularly those addressing evolving threats, inefficiencies persist due to potential inconsistency in both the generated malicious malware and the pre-specified detection rules, as well as their crisp decision-making process. In this paper, we propose to address these issues by (i) considering the detection rules generation process as a Bi-Level Optimization Problem, where a competition between two levels (an upper level and a lower one) produces a set of effective detection rules capable of detecting new variants of existing and even unseen malware patterns. This bi-level strategy is subtly inspired by natural evolutionary processes, where organisms adapt and evolve through continuous interaction and competition within their environments. Furthermore, (ii) we leverage the fundamentals of Rough Set Theory, which reflects cognitive decision-making processes, to assess the true nature of artificially generated malicious patterns. This involves retaining only the consistent malicious patterns and detection rules and categorizing these rules into a three-way decision framework comprising accept, abstain, and reject options. Our novel malware detection technique outperforms several state-of-the-art methods on various android malware datasets, accurately predicting new apps with a 96.76% accuracy rate. Moreover, our approach is versatile and effective in detecting patterns applicable to a variety of cybersecurity threats.

关键词： android malware detection Bi-level optimization Rough set theory Artificial malicious patterns Evolutionary algorithms Three-way decision making

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Novel Multi-Classification Dynamic detection Model for android malware Based on Improved Zebra Optimization Algorithm and LightGBM

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SENSORS 2024年第18期24卷 5975页

作者： Zhou, Shuncheng Li, Honghui Fu, Xueliang Han, Daoqi He, Xin Inner Mongolia Agr Univ Coll Comp & Informat Engn Hohhot 010018 Peoples R China

With the increasing popularity of android smartphones, malware targeting the android platform is showing explosive growth. Currently, mainstream detection methods use static analysis methods to extract features of the software and apply machine learning algorithms for detection. However, static analysis methods can be less effective when faced with android malware that employs sophisticated obfuscation techniques such as altering code structure. In order to effectively detect android malware and improve the detection accuracy, this paper proposes a dynamic detection model for android malware based on the combination of an Improved Zebra Optimization Algorithm (IZOA) and Light Gradient Boosting Machine (LightGBM) model, called IZOA-LightGBM. By introducing elite opposition-based learning and firefly perturbation strategies, IZOA enhances the convergence speed and search capability of the traditional zebra optimization algorithm. Then, the IZOA is employed to optimize the LightGBM model hyperparameters for the dynamic detection of android malware multi-classification. The results from experiments indicate that the overall accuracy of the proposed IZOA-LightGBM model on the CICMalDroid-2020, CCCS-CIC-AndMal-2020, and CIC-AAGM-2017 datasets is 99.75%, 98.86%, and 97.95%, respectively, which are higher than the other comparative models.

关键词： android malware detection improved zebra optimization algorithm LightGBM hyperparameter optimization

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CloudIntellMal: An advanced cloud based intelligent malware detection framework to analyze android applications

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartA期119卷

作者： Mishra, Preeti Jain, Tanmay Aggarwal, Palak Paul, Gunjan Gupta, Brij B. Attar, Razaz Waheeb Gaurav, Akshat Doon Univ Sch Technol Dept Comp Sci Dehra Dun India Reverie Language Technol Ltd Bangalore India Cognizant Ghaziabad India Amazon Bangalore India Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan Kyung Hee Univ 26 Kyungheedae Ro Seoul South Korea Symbiosis Int Univ Symbiosis Ctr Informat Technol SCIT Pune India Univ Petr & Energy Studies UPES Ctr Interdisciplinary Res Dehra Dun India Princess Nourah bint Abdulrahman Univ Coll Business Adm Management Dept POB 84428 Riyadh 11671 Saudi Arabia Ronin Inst Montclair NJ USA

Serverless computing has become very popular in recent times which facilitates a greater abstraction in virtualization technology. With the rapid development in serverless computing, a new paradigm has been evolved to design and develop android applications. However, the android platform has become one of the most vulnerable targets due to the increase in the number of users and hence raises a strong security concern to develop an advanced security framework. In this paper, a serverless computing based intelligent malware detection framework, called CloudIntellMal is proposed to detect malware targeting android-based applications. The framework incorporates some popular key cloud services such as Amazon Web Services (AWS) Elastic Cloud Compute (EC2) and Simple Storage Service (S3) to store and pre-process the logs taken from the end user devices. An efficient feature extraction algorithm is designed that employs 'Bag of n-grams' to develop feature vectors. The framework runs the machine learning algorithm in the cloud infrastructure to learn malicious patterns. The decision model is loaded in EC2 at the time of detection phase to classify the monitored apps. A prototype of the approach has been developed and validated using Drebin dataset and results seem to be promising.

关键词： android malware detection Static analysis Amazon EC2 Amazon S3 malware detection Artificial Intelligence

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Two Phase Static Analysis Technique for android malware detection 15

Two Phase Static Analysis Technique for Android Malware Dete...

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3rd International Symposium on Women in Computing and Informatics (WCI)

作者： Kate, Priyadarshani M. Dhavale, Sunita V. Def Inst Adv Technol Dept Comp Engn Pune 411025 Maharashtra India

ISBN: (纸本)9781450333610

The growing popularity of android based smart phones has greatly fuelled the spread of android malware. Further, these malwares are evolving rapidly to escape from traditional signature -based detection methods. Hence, there is a serious need to develop effective android malware detection techniques. In this paper, we propose two phase static android malware analysis scheme using bloom filters. The Phase I involves two different bloom filters that classify a given sample into malware or benign class based on permission feature set only. The evaded malicious samples from Phase I are further analyzed by Phase II consisting Na ve Bayes Classifier using permission and code based mixed feature set. Inclusion of Phase I classification makes the technique computationally less intensive;while addition of the Phase II classification improves the overall accuracy of the proposed model. Experimental results indicate both detection accuracy and computational efficiency of the proposed technique.

关键词： Bloom Filter Machine learning android malware detection

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An adaptive semi-supervised deep learning-based framework for the detection of android malware

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 5141-5157页

作者： Wajahat, Ahsan He, Jingsha Zhu, Nafei Mahmood, Tariq Nazir, Ahsan Pathan, Muhammad Salman Qureshi, Sirajuddin Ullah, Faheem Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Lasbela Univ Agr Water & Marine Sci Dept Comp Sci Lasebla Pakistan Univ Educ Fac Informat Sci Vehari Campus Vehari Pakistan Sci Maynooth Univ Dept Comp Maynooth Ireland

Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting android mobile devices. android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time android malware detection.

关键词： android malware detection deep learning CNN LSTM Drebin dataset

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Unified detection of Obfuscated and Native android malware

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Computers, Materials & Continua 2022年第2期70卷 3099-3116页

作者： Pagnchakneat C.Ouk Wooguil Pak Department of Computer Engineering Keimyung UniversityDaegu42601Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsanGyeongbuk38541Korea

The android operating system has become a leading smartphone platform for mobile and other smart devices,which in turn has led to a diversity of malware *** amount of research on android malware detection has increased significantly in recent years and many detection systems have been *** these efforts,however,most systems can be thwarted by sophisticated androidmalware adopting obfuscation or native code to avoid discovery by anti-virus *** this paper,we propose a new static analysis technique to address the problems of obfuscating and native malware *** proposed system provides a unified technique for extracting features from applications and native libraries using a selection algorithm that can extract a small set of unique and effective features for detecting malware applications rapidly and with a high detection *** using large android malware detection datasets obtained from various sources confirmed that the proposed approach achieves very promising results in terms of improved accuracy,low false positive rate,and high detection rate.

关键词： android malware detection native code obfuscation unified feature extraction

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A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in android apps

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EGYPTIAN INFORMATICS JOURNAL 2024年 28卷

作者： Chimeleze, Collins Jamil, Norziana Alturki, Nazik Zain, Zuhaira Muhammad Univ Tenaga Nas Inst Informat & Comp Energy Kajang Malaysia United Arab Emirates Univ Coll IT Dept Informat Syst & Secur POB 15551 Abu Dhabi U Arab Emirates Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia

The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article to propose a new clustering method-fuzzy C-mean simulated annealing (FCMSA)-to enhance malware detection through machine learning. The FCMSA clustering technique improves performance by minimizing vulnerabilities, reducing outliers, and optimizing large datasets. The proposed technique selects high-quality clusters from android app permissions and, using lightGBM, classifies android malware. Experimental results show that the proposed FCMSA-GBM technique achieves superior accuracy (99.21%) and precision (99.70%) compared to other prevalent cluster-based android malware detection techniques, while also lowering error rates and execution time.

关键词： android malware detection Fuzzy c means clustering Simulated annealing Gradient boosting machine

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detection approaches for android malware: Taxonomy and review analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartF期238卷

作者： Manzil, Hashida Haidros Rahima Naik, S. Manohar Cent Univ Kerala Dept Comp Sci Periye Kerala India

The main objective of this review is to present an in-depth study of android malware detection approaches. This article provides a comprehensive survey of 150 studies on android malware detection from 2010 to 2022. Two broader categories like traditional signature-based and behavior-based approaches are discussed throughout the review process. The behavior-based detection approaches are further categorized in to static, dynamic, and hybrid analysis methods. The survey has conducted in different dimensions including detection approaches, datasets used, features, sustainability of the solutions, etc. Although researchers have proposed detection tools and techniques to develop efficient countermeasures against android malware, there is a scarcity of a concise review for research practitioners in this subject area. The survey shows there is a great deal of interest in machine learning-based detection methods among the research community. The review not only provides an authentic assessment of the malware detection capabilities of different approaches but also presents observations and suggestions regarding various aspects of the android malware ecosystem. These observations and suggestions are intended to assist researchers in enhancing further research towards the subject domain.

关键词： android malware detection Behavior based analysis Signature based analysis malware analysis

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An Early detection of android malware Using System Calls based Machine Learning Model 22

An Early Detection of Android Malware Using System Calls bas...

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17th International Conference on Availability, Reliability and Security (ARES)

作者： Zhang, Xinrun Mathur, Akshay Zhao, Lei Rahmat, Safia Niyaz, Quamar Javaid, Ahmad Yang, Xiaoli Purdue Univ Northwest Hammond IN 46323 USA Univ Toledo 2801 W Bancroft St Toledo OH 43606 USA

ISBN: (纸本)9781450396707

Several host intrusion detection systems (HIDSs) based on system call analysis have been proposed in the past to detect intrusions and malware using relevant datasets. Machine learning (ML) techniques have been applied on those datasets to improve the performances of HIDSs. However, the emphasis given on their real-world deployment is limited. To address this issue, we propose a framework for system call processing for benign and malware android apps with an ability of early detection of malware. We extracted and analyzed system call traces for benign and malware apps, and processed their system call traces with N-gram and TF-IDF models. Six ML algorithms - Decision Trees, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machines, and Multi-layer Perceptron - were trained for the malware detection system. The experimental results demonstrate that our android malware detection system (AMDS), using traces of 3000 system calls, is capable of early detection with an average accuracy of 99.34%. We also implemented an android app based on a client-server architecture for the proposed AMDS to demonstrate its deployment for malware detection in real-time.

关键词： android malware detection smartphone security system call analysis

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A Lightweight On-Device detection Method for android malware

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第9期51卷 5600-5611页

作者： Yuan, Wei Jiang, Yuan Li, Heng Cai, Minghui Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Tencent Shenzhen Peoples R China

android malware poses severe threats to users, hence raising an urgent demand for malware detection. In-cloud android malware detection often suffers privacy leakage and communication overheads. Therefore, this article focuses on on-device android malware detection. At present, on-device malware detectors are usually trained on servers and then transplanted to mobile devices (e.g., smartphones). In practice, on-device training is particularly important due to the demand for offline updates. Because mobile devices are limited in resource, however, on-device training is hard to implement, especially for those high-complexity malware detectors. To overcome this challenge, we design a lightweight on-device android malware detector, based on the recently proposed broad learning method. Our detector mainly uses one-shot computation for model training. Hence it can be fully or incrementally trained directly on mobile devices. As far as detection accuracy is concerned, our detector outperforms the shallow learning-based models, including support vector machine (SVM) and AdaBoost, and approaches the deep learning-based models multilayer perceptron (MLP) and convolutional neural network (CNN). Moreover, our detector is more robust to adversarial examples than the existing detectors, and its robustness can be further improved through on-device model retraining. Finally, its advantages are confirmed by extensive experiments, and its practicality is demonstrated through runtime evaluation on smartphones.

关键词： malware Training Detectors Feature extraction Servers Smart phones android malware detection broad learning information security machine learning

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