A new method for assigning appropriate labels to create a 28 Standard Android Botnet Dataset (28-SABD)

Moodi, Mahdi; Ghazvini, Mahdieh

doi:10.1007/s12652-018-1140-5

Cited by 17 publications

(6 citation statements)

References 46 publications

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“…[9] NB, BN, & DT 98.00 [10] Not ML method 99.00 [11] ANN, SVM, k-NN, NB, & GBM 97.00 [12] k-NN, DT, & RF 70.00 [13] ANN, SVM, NB, DT, RF, LR. & BNet 99.47 [14] NB & DT 97.00 [15] DT 99.00 [16] K-means 82.10 [17] DT 97.00 [18] RF 86.41 [19] ANN, SVM, & NB 93.90 [44] Not ML method 99.82 [20] k-NN & RF 91.10 [21] ANN 97.87 [22] DT 90.40 [23] DT 99.46 [24] ANN, SVM, & k-NN 99.00 [45] Not ML method 95.50 [46] Not ML method 99.68 [25] SVM & ANN 94.00 [47] Not ML method 99.70 [26] DT & ANN 99.20 [48] Not ML method 99.00 [27] KNN, SVM, DT, RF, & ANN 99.00 [28] SVM 99.15 [29] k-NN 94.00 [49] Not ML method 96.20 [50] Not ML method 99.35 [51] Not ML method 92.92 [52] Not ML method 98.70 [53] Not ML method 97.00 [54] Not ML method 98.70 [55] Not ML method *100 [56] Not ML method 99.94 [57] Not ML method 99.60 [58] Not ML method 98.60 [59] Not ML method 97.20 [30] k-NN, NB, DT, RF, & SVM 91.80 [31] ANN 99.60 This research LR, LR, DT, NB, k-NN, RF, GBM, SVM, K-means, K-medians, mini batch, HC, ANN, DBSCAN, GMM, LAC, AP, and ensemble learning…”

Section: Resultsmentioning

confidence: 99%

Using Ensemble Learning Technique for Detecting Botnet on IoT

Rezaei

2021

SN COMPUT. SCI.

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Despite the growing reputation and the ubiquitous nature of the IoT, it poses significant challenges as it is also considered a convenient platform for cyberattacks. The connection of various devices without fixed security help attackers in allowing botnet to run high crash DDoS attacks against a range of internet services. The botnet is one of the main security challenges that have the most impact on IoT for several reasons. It allows the private network devices to be infected by malicious software and controlled as a group without the owners' awareness. Botnets are often utilized for sending spam, stealing data, and performing DDoS attacks. As there are many areas of security that utilize machine learning (ML) technology, utilizing ML to detect botnet appears to be realistic and workable. While several studies have been conducted using ML for detecting botnet, they are either not highly accurate or only compatible with specific types of botnet or devices. This study aims to create an ensemble learning model using the best ML methods among supervised learning, unsupervised learning, and regression learning to optimize the accuracy of botnet detection on IoT and minimize the number of features requested. After several examinations in different combinations of ML algorithms, this study managed to achieve 100% accuracy of botnet detection as well as minimizing the number of features to only 20.

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Section: Resultsmentioning

confidence: 99%

Using Ensemble Learning Technique for Detecting Botnet on IoT

Rezaei

2021

SN COMPUT. SCI.

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“…The experiment results show that overall 99.3% accuracy and 99.1% F1-score can be achieved. In [17], the authors have created a 28 Standard Android Botnet Dataset (28-SABD), an Android botnet malware dataset including 14 families of Android botnet malware traffic. They used the ensemble K-Nearest Neighbors (KNN) technique to improve overall detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics

Lo¹,

Layeghy²,

Sarhan³

et al. 2022

Preprint

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In this paper, we proposed XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed model is mainly composed of a botnet detector and an explainer for automatic forensics. The XG-BoT detector can effectively detect malicious botnet nodes under large-scale networks. Specifically, it utilizes a grouped reversible residual connection with a graph isomorphism network to learn expressive node representations from the botnet communication graphs. The explainer in XG-BoT can perform automatic network forensics by highlighting suspicious network flows and related botnet nodes. We evaluated XG-BoT on real-world, large-scale botnet network graphs. Overall, XG-BoT is able to outperform the state-of-theart in terms of evaluation metrics. In addition, we show that the XG-BoT explainer can generate useful explanations based on GNNExplainer for automatic network forensics.

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“…This is a new dataset specific to the Android botnet created by [12]. This dataset presents a new dataset based on the ISCX dataset that includes features derived via dynamic analysis in the form of vectors of zeros and ones.…”

Section: ) 28-sabdmentioning

confidence: 99%

“…For example in [28], the authors have employed the tPacketCapture pro tool to collect network visitors of software from the Gnome dataset. Another study by [12] analyzed the network traffic from botnet apps derived from the ISCX dataset using four actual machines and a BlueStack emulator. Network traffic has been shown to be the most trustworthy characteristic according to several studies such as [18] and [29].…”

Section: Dynamic Analysismentioning

confidence: 99%

Applications of Artificial Intelligence to Detect Android Botnets: A Survey

Almuhaideb

Alynanbaawi

2022

IEEE Access

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From the growing popularity of Android smart devices, and especially with the recent advances brought on by the COVID-19 pandemic on digital adoption and transformation, the importance of protecting these devices has grown, as they carry very sensitive data. Malicious attacks are targeting Android since it is open source and has the highest adoption rate among mobile platforms. Botnet attacks are one of the most often forgotten types of attacks. In addition, there is a lack of review papers that can clarify the state of knowledge and indicate research gaps in detecting android botnets. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of Android Botnet detection. This study attempts to provide a comprehensive overview of the deployed AI apps for future academics interested in performing Android Botnet Detection studies. We focused on the applications of artificial intelligence and its two prominent subdomains, machine learning (ML) and deep learning (DL) techniques. The study presents available Android Botnet datasets suitable for detection using ML and DL algorithms. Moreover, this study provides an overview of the methodologies and tools utilized in APK analysis. The paper also serves as a comprehensive taxonomy of Android Botnet detection methods and highlights a number of challenges encountered while analyzing Android Botnet detection techniques. The research gaps indicated an absence of hybrid analysis research in the area, as well as a lack of an up-to-date dataset and a time-series dataset. The findings of this paper show valuable prospective directions for future research and development opportunities.INDEX TERMS Android security, android attacks, android botnets, android botnet detection, artificial intelligence (AI), machine learning (ML), deep learning (DL).

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A new method for assigning appropriate labels to create a 28 Standard Android Botnet Dataset (28-SABD)

Cited by 17 publications

References 46 publications

Using Ensemble Learning Technique for Detecting Botnet on IoT

Using Ensemble Learning Technique for Detecting Botnet on IoT

XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics

Applications of Artificial Intelligence to Detect Android Botnets: A Survey

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