ABC: Android Botnet Classification Using Feature Selection and Classification Algorithms

Abdullah, Zubaile; Saudi, Madihah Mohd; Anuar, Nor Badrul

doi:10.1166/asl.2017.8994

Cited by 17 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In one study, the results indicated that the random forest approach had 0.972% precision and 0.96% recall. In [ 33 ], machine learning approaches were proposed for detecting Android botnets. The ISCX dataset consisted of 1635 benign and 1635 attacks.…”

Section: Background Of Studymentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

View full text Add to dashboard Cite

With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment.

show abstract

Section: Background Of Studymentioning

confidence: 99%

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Alkahtani

Aldhyani

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Random Forest was found to have the best results yielding 97.3% accuracy, 0.987 recall, and 0.985 precision. The authors of [13] also utilized only the 'requested permissions' as features and applied Information Gain to reduce the features and select the most significant requested permissions. They evaluated their approach using Decision Trees, Naive Bayes, and Random Forest.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned earlier, the ISCX Android botnet dataset from [9] was utilized for the experiments in this paper. This dataset contains 1929 botnet apps and has been employed in previous works including [6][7][8][10][11][12][13]22]. Table 3 shows the distribution of samples within the 14 different botnet families present in the dataset.…”

Section: Dataset Used For the Investigationmentioning

confidence: 99%

Deep Learning Techniques for Android Botnet Detection

Yerima

Alzaylaee

Shajan³

et al. 2021

Electronics

View full text Add to dashboard Cite

Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers.

show abstract

“…They proposed a mobile botnet detection by inspecting abnormal network flow through a virtual private network (VPN) with a 94.6% detection rate. In [25] developed Android Botnet Classification (ABC) classification on Android mobile botnet based only on permission with 94.6% detection rate. A Prototype of Android Botnet Identification System (ABIS) extracts feature set permissions and API calls from Android apps to identify the botnet and its family [26].…”

Section: Related Workmentioning

confidence: 99%

Mobile Botnet Classification by using Hybrid Analysis

Yusof¹,

Saudi²,

Ridzuan³

2018

IJET

View full text Add to dashboard Cite

The popularity and adoption of Android smartphones has attracted malware authors to spread the malware to smartphone users. The malware on smartphone comes in various forms such as Trojans, viruses, worms and mobile botnet. However, mobile botnet or Android botnet are more dangerous since they pose serious threats by stealing user credential information, distributing spam and sending distributed denial of service (DDoS) attacks. Mobile botnet is defined as a collection of compromised mobile smartphones and controlled by a botmaster through a command and control (C&C) channel to serve a malicious purpose. Current research is still lacking in terms of their low detection rate due to their selected features. It is expected that a hybrid analysis could improve the detection rate. Therefore, machine learning methods and hybrid analysis which combines static and dynamic analyses were used to analyse and classify system calls, permission and API calls. The objective of this paper is to leverage machine learning techniques to classify the Android applications (apps) as botnet or benign. The experiment used malware dataset from the Drebin for the training and mobile applications from Google Play Store for testing. The results showed that Random Forest Algorithm achieved the highest accuracy rate of 97.9%. In future, more significant approach by using different feature selection such as intent, string and system component will be further explored for a better detection and accuracy rate.

show abstract

ABC: Android Botnet Classification Using Feature Selection and Classification Algorithms

Cited by 17 publications

References 0 publications

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

Deep Learning Techniques for Android Botnet Detection

Mobile Botnet Classification by using Hybrid Analysis

Contact Info

Product

Resources

About