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IEEE International Performance Computing and Communications Conference

作者： Chao Liu Jianan Li Min Yu Gang Li Bo Luo Kai Chen Jianguo Jiang Weiqing Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Information Technology Deakin University VIC Australia Department of Electrical Engineering and Computer Science University of Kansas Lawrence Kansas City USA

ISBN: (纸本)9781538668092;9781538668085

In android malware detection, sensitive data-flows provide more accurate information on the application's behavior than regular features such as signatures and permissions. Currently, android static taint analysis is widely adopted to identify sensitive data-flows because of its high code coverage and low false negative rate. However, existing static taint analysis tools cannot effectively analyze applications that adopt android reflection mechanism. Reflection mechanism can block the control-flows and data-flows of the application. When constructing a call graph, the call information will point directly to the system's reflection processing method, rather than the actual method invoked by the application. This significantly affects the accurate representation of the application's behavior. To address this issue, this paper proposes a unified android malware detection model based on reflective calls named URefFlow, in which the reflective call statement is replaced by the non-reflective call statement to make the reflective calls explicit by combining the parameters of the reflective calls into standard function calls. After extracting the complete sensitive data-flows with reflective calls from an application, we analyze the characteristics of these data-flows to determine whether the application is malicious. Evaluation results on thousands of applications show that URefFlow can achieve an impressive detection accuracy of 95.6% with a false positive rate of 0.8%. In addition, the proposed approach complements well with existing static stain analysis techniques.

关键词： Data-flow Reflection Standardization android malware detection

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MalWhiteout: Reducing Label Errors in android malware detection 22

MalWhiteout: Reducing Label Errors in Android Malware Detect...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Liu Wang Haoyu Wang Xiapu Luo Yulei Sui Huazhong University of Science and Technology China and Beijing University of Posts and Telecommunications China Huazhong University of Science and Technology China The Hong Kong Polytechnic University China University of Technology Sydney Australia

ISBN: (纸本)9781450394758

Machine learning based android malware detection has attracted a great deal of research work in recent years. A reliable malware dataset is critical to evaluate the effectiveness of malware detection approaches. Unfortunately, existing malware datasets used in our community are mainly labelled by leveraging existing anti-virus services (i.e., VirusTotal), which are prone to mislabelling. This, however, would lead to the inaccurate evaluation of the malware detection techniques. Removing label noises from android malware datasets can be quite challenging, especially at a large data scale. To address this problem, we propose an effective approach called MalWhiteout to reduce label errors in android malware datasets. Specifically, we creatively introduce Confident Learning (CL), an advanced noise estimation approach, to the domain of android malware detection. To combat false positives introduced by CL, we incorporate the idea of ensemble learning and inter-app relation to achieve a more robust capability in noise detection. We evaluate MalWhiteout on a curated large-scale and reliable benchmark dataset. Experimental results show that MalWhiteout is capable of detecting label noises with over 94% accuracy even at a high noise ratio (i.e., 30%) of the dataset. MalWhiteout outperforms the state-of-the-art approach in terms of both effectiveness (8% to 218% improvement) and efficiency (70 to 249 times faster) across different settings. By reducing label noises, we show that the performance of existing malware detection approaches can be improved.

关键词： confident learning android malware detection label noise

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TL-GNN: android malware detection Using Transfer Learning

Applied AI Letters

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Applied AI Letters 2024年第3期5卷

作者： Raza, Ali Qaisar, Zahid Hussain Aslam, Naeem Faheem, Muhammad Ashraf, Muhammad Waqar Chaudhry, Muhammad Naman Department of Computer Science NFC Institute of Engineering and Technology Multan Pakistan Department of Computing and Emerging Technologies Emerson University Multan Pakistan Department of Computing Science School of Technology and Innovations University of Vaasa Vaasa Finland Department of Computer Engineering Bahauddin Zakariya University Multan Pakistan

malware growth has accelerated due to the widespread use of android applications. android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state-of-the-art android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1-measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy. © 2024 The Authors. Applied AI Letters published by John Wiley & Sons Ltd.

关键词： android malware detection deep learning graph neural network malware classifier transfer learning

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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 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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android HIV: A Study of Repackaging malware for Evading Machine-Learning detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 987-1001页

作者： Chen, Xiao Li, Chaoran Wang, Derui Wen, Sheng Zhang, Jun Nepal, Surya Xiang, Yang Ren, Kui Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic 3122 Australia CSIRO Data61 Epping NSW 1710 Australia Univ Buffalo State Univ New York Dept Comp Sci & Engn Buffalo NY 14260 USA

Machine learning-based solutions have been successfully employed for the automatic detection of malware on android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features (e.g., requested permissions, API calls, etc.), and the perturbations can only be implemented by simply modifying application's manifest. While recent android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning-based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.

关键词： android malware detection adversarial machine learning

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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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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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A Multimodal malware detection Technique for android IoT Devices Using Various Features

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IEEE ACCESS 2019年 7卷 64411-64430页

作者： Kumar, Rajesh Zhang, Xiaosong Wang, Wenyong Khan, Riaz Ullah Kumar, Jay Sharif, Abubaker Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Sichuan Peoples R China Govt Coll Univ Faisalabad Dept Elect Engn Faisalabad 38000 Pakistan

Internet of things (IoT) is revolutionizing this world with its evolving applications in various aspects of life such as sensing, healthcare, remote monitoring, and so on. android devices and applications are working hand to hand to realize dreams of the IoT. Recently, there is a rapid increase in threats and malware attacks on android-based devices. Moreover, due to extensive exploitation of the android platform in the IoT devices creates a task challenging of securing such kind of malware activities. This paper presents a novel framework that combines the advantages of both machine learning techniques and blockchain technology to improve the malware detection for android IoT devices. The proposed technique is implemented using a sequential approach, which includes clustering, classification, and blockchain. Machine learning automatically extracts the malware information using clustering and classification technique and store the information into the blockchain. Thereby, all malware information stored in the blockchain history can be communicated through the network, and therefore any latest malware can be detected effectively. The implementation of the clustering technique includes calculation of weights for each feature set, the development of parametric study for optimization and simultaneously iterative reduction of unnecessary features having small weights. The classification algorithm is implemented to extract the various features of android malware using naive Bayes classifier. Moreover, the naive Bayes classifier is based on decision trees for extracting more important features to provide classification and regression for achieving high accuracy and robustness. Finally, our proposed framework uses the permissioned blockchain to store authentic information of extracted features in a distributed malware database blocks to increase the run-time detection of malware with more speed and accuracy, and further to announce malware information for all users.

关键词： android malware detection blockchain Internet of Things (IoT) clustering secure machine learning

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