scholarly journals Deep-Droid: Deep Learning for Android Malware Detection

2020 ◽  
Vol 9 (12) ◽  
pp. 122-125
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Learning Approaches ◽  
Android Malware ◽  
Detection Systems ◽  
Learning Framework ◽  
The Past ◽  
Android Malware Detection ◽  
Android Os

Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. Machine learning approaches have been showing promising results in classifying malware where most of the method are shallow learners like Random Forest (RF) in recent years. In this paper, we propose Deep-Droid as a deep learning framework, for detection Android malware. Hence, our Deep-Droid model is a deep learner that outperforms exiting cutting-edge machine learning approaches. All experiments performed on two datasets (Drebin-215 & Malgenome-215) to assess our Deep-Droid model. The results of experiments show the effectiveness and robustness of Deep-Droid. Our Deep-Droid model achieved accuracy over 98.5%.

scholarly journals Android Malware Detection through Machine Learning Techniques: A Review

2020 ◽  
Vol 16 (02) ◽  
pp. 14
Author(s):  
Abikoye Oluwakemi Christiana ◽  
Benjamin Aruwa Gyunka ◽  
Akande Noah
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Social Engineering ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Android Os ◽  
Learning Techniques ◽  
The Individual

<p class="0abstract">The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares. The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown attacks. This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.</p>

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

Towards Deep Learning-Based Approach for Detecting Android Malware

2021 ◽  
pp. 2193-2219
Author(s):  
Jarrett Booz ◽  
Josh McGiff ◽  
William G. Hatcher ◽  
Wei Yu ◽  
James Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Environment ◽  
Malware Detection ◽  
Extensive Study ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Mobile Malware Detection ◽  
Optimal Settings

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, an extensive study is conducted on various hyper-parameters to determine optimal configurations, and then a performance evaluation is carried out on those configurations to compare and maximize detection accuracy in our target networks. The results achieve a detection accuracy of approximately 95%, with an approximate F1 score of 93%. In addition, the evaluation is extended to include other machine learning frameworks, specifically comparing Microsoft Cognitive Toolkit (CNTK) and Theano with TensorFlow. The future needs are discussed in the realm of machine learning for mobile malware detection, including adversarial training, scalability, and the evaluation of additional data and features.

Towards Deep Learning-Based Approach for Detecting Android Malware

2019 ◽  
Vol 7 (4) ◽  
pp. 1-24 ◽  
Author(s):  
Jarrett Booz ◽  
Josh McGiff ◽  
William G. Hatcher ◽  
Wei Yu ◽  
James Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Environment ◽  
Malware Detection ◽  
Extensive Study ◽  
Detection Accuracy ◽  
Android Malware ◽  
Android Malware Detection ◽  
Mobile Malware Detection ◽  
Optimal Settings

In this article, the authors implement a deep learning environment and fine-tune parameters to determine the optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, the authors demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, an extensive study is conducted on various hyper-parameters to determine optimal configurations, and then a performance evaluation is carried out on those configurations to compare and maximize detection accuracy in our target networks. The results achieve a detection accuracy of approximately 95%, with an approximate F1 score of 93%. In addition, the evaluation is extended to include other machine learning frameworks, specifically comparing Microsoft Cognitive Toolkit (CNTK) and Theano with TensorFlow. The future needs are discussed in the realm of machine learning for mobile malware detection, including adversarial training, scalability, and the evaluation of additional data and features.

scholarly journals On the Feasibility of Adversarial Sample Creation Using the Android System API

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 433
Author(s):  
Fabrizio Cara ◽  
Michele Scalas ◽  
Giorgio Giacinto ◽  
Davide Maiorca
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Random Noise ◽  
Malware Detection ◽  
Android Malware ◽  
Static And Dynamic Analysis ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Expert Analysis ◽  
The Impact

Due to its popularity, the Android operating system is a critical target for malware attacks. Multiple security efforts have been made on the design of malware detection systems to identify potentially harmful applications. In this sense, machine learning-based systems, leveraging both static and dynamic analysis, have been increasingly adopted to discriminate between legitimate and malicious samples due to their capability of identifying novel variants of malware samples. At the same time, attackers have been developing several techniques to evade such systems, such as the generation of evasive apps, i.e., carefully-perturbed samples that can be classified as legitimate by the classifiers. Previous work has shown the vulnerability of detection systems to evasion attacks, including those designed for Android malware detection. However, most works neglected to bring the evasive attacks onto the so-called problem space, i.e., by generating concrete Android adversarial samples, which requires preserving the app’s semantics and being realistic for human expert analysis. In this work, we aim to understand the feasibility of generating adversarial samples specifically through the injection of system API calls, which are typical discriminating characteristics for malware detectors. We perform our analysis on a state-of-the-art ransomware detector that employs the occurrence of system API calls as features of its machine learning algorithm. In particular, we discuss the constraints that are necessary to generate real samples, and we use techniques inherited from interpretability to assess the impact of specific API calls to evasion. We assess the vulnerability of such a detector against mimicry and random noise attacks. Finally, we propose a basic implementation to generate concrete and working adversarial samples. The attained results suggest that injecting system API calls could be a viable strategy for attackers to generate concrete adversarial samples. However, we point out the low suitability of mimicry attacks and the necessity to build more sophisticated evasion attacks.

scholarly journals On defending against label flipping attacks on malware detection systems

2020 ◽  
Vol 32 (18) ◽  
pp. 14781-14800 ◽  
Author(s):  
Rahim Taheri ◽  
Reza Javidan ◽  
Mohammad Shojafar ◽  
Zahra Pooranian ◽  
Ali Miri ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Malware Detection ◽  
Detection Problem ◽  
Classification Rate ◽  
Android Malware ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Noise Rate ◽  
Internet Of Thing

Abstract Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature.

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