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Similarly, the highest F-measure of simple logistic regression having 0.99 values other than the obtained results of Naive Bayes, SVM, MLP, J48 and RF. Whereas, the attained F-measure for Naive Bayes, SVM, MLP, J48 and RF are 0.87, 0.93, 0.96, 0.97 and 0.98 respectively. Among the selected applications, 62% represent known botnets, whereas the remaining 38% belong to other malware families without the enabled C&C feature.

However, the largest size for any model is 1.6MB which is of the simple logistic regression model. In contrast to MLP and Naive Bayes model sizes in [77], our model size is reasonable enough to reside on user device. It is an off-devise analysis system and its results strongly rely on the output of already developed cloud based sandbox service known as Andrubis. However, we aim to identify bot application binaries, whereas the said approach is used for mobile malware detection in general. A GET request is meant to retrieve static contents like images, binaries etc. while POST requests are used in server side programming to dynamically retrieve the resources. Thus, HTTP attacks generated by GET requests are simpler to create, and can more effectively scales in a botnet scenario [54].

Data Leak Statistics

In the past few years, several mobile botnets, such as NotCompatible.C, Zues botnet, DroidDream, BMaster, and TigerBot, have evolved to hinder the performance of smartphone devices. A recent report [1] stated that a variant of the existing malware NotCompatible called NotCompatible.C, which has remote administration capabilities, targets Android devices. The report mentioned that NotCompatible.C is the most dangerous mobile malware with traditional PC-based botnet capabilities ever introduced.

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Therefore, we have opted the ANN’s backpropagation modeling to classify Drebin dataset. As part of dynamic analysis component, we need to extract only those features which are most appropriate for an application to initiate a botnet attack. For this purpose, we bind a program with the API provided by [20], execute each malicious binary in an automated fashion on publically available cloud based sandbox and collect run-time smartbots execution traces of each application. This service executes program instructions through a modified Dalvik VM deployed virtual machine introspection (VMI) for system-level inspection. In addition to that, a rich external stimulation is implemented to capture maximum program behavior and to increase code coverage [30]. Average running time of each application is 3 to 5 minutes depending upon the instruction set.

Furthermore in this section, we provide a case study that helps to demonstrate the usability of our framework. This can also be used to develop entirely new apps to use in Google’s AppSheet platform. Just give a text prompt input for the kind of app you want, and Duet can help make it for you.

What’s up at SmartBots

Compared with other sophisticated botnets (e.g., Obad, DroidDream, and Geinimi), NotCompatible.C discriminates itself by having a P2P C&C architecture and by employing numerous evasion techniques. Moreover, it offers cross-platform compatibility by sharing its C&C system with Windows bots. Other advancements in botnets include Zeus botnet [2], which affects Android, Symbian, Blackberry, and Windows users, unlike DroidDream botnet [3], which is particularly designed only for Android devices. IKee.B [4] botnet, which scans the IP addresses of target victims, is designed for iPhones, whereas BMaster [5] and TigerBot [6] particularly aim to disrupt Android-based devices.

Although all ML classifiers produced relatively good accuracy rates i.e higher than 90% however, simple logistic regression outperforms the other tested classifiers. It correctly classifies 99.49% of Drebin dataset using the selected features to distinguish botnet applications. In difference, Naive Bayes, SVM, MLP, J48 and RF achieve accuracy rate of 91%, 96%, 97%, 98% and 99% respectively. Table 5 also reveals that the precision values support the accuracy rates of the machine learning classifiers in establishing an effective model. The precision value for the SVM is 1.00 while the precision values for Naive Bayes, MLP, simple logistic regression, J48, and RF are 0.87, 0.94, 0.99, 0.98, and 0.99 respectively.

• Process more than 20,000 conversations in a month without human intervention.
• They automate the process of interacting with your website vistors and social media followers in an attempt to create the best user experience.
• Given the race among mobile botnet authors, various off-the-shelf mobile malware tools [8] that can perform specific malevolent actions on the behalf of attackers have been introduced.
• This service executes program instructions through a modified Dalvik VM deployed virtual machine introspection (VMI) for system-level inspection.
• A short description of these algorithms is presented in the next subsection.
• As a result, according to [50,51], we can differentiate botnet and regular DNS queries by investigating (a) botnet structures (b) botnet synchronization and (c) bots response time.

A permission analysis component of VetDroid extracts all permissions and highlights the connections between them. As a result, the system generates a function call graph through which malicious applications are identified. DroidBox is a sandbox for behavioral analysis, proposed by Lantz [31], which can effectively analyze Android applications.

Although, we obtained similar results while choosing the best option between cross validation and random sampling, yet 10-fold cross validation generates slightly better results as compared to random sampling. The results in Table 6 affirm the viability of the simple logistic regression classifier as a basis for effective botnet application detection within the specified feature domain. Ultimately, this will become our final choice for classifier building in production environments. Mobile application developers use cryptographic operations which include message authentication codes and block ciphers to secure communication and data. From the Fig 10 we can observe that, the most common cryptographic algorithms observed during the dynamic analysis of botnets were AES (20%), DES (12%), AES/ECB/ZEROBYTEPADDING (5%), and DES/CBC/PKCS5Padding (3%). According to [71], DES was the predominantly used cryptographic algorithm in 2010 (98%); however, its usage reduced to 1.53% in 2013.

With, these permissions, the bind method of Java ServerSocket class is called in order to communicate outside by opening a socket. Moreover, the application listens for RECEIVE_BOOT_COMPLETED permission to start any background activity in order to trace the smartphone location and to listen to a C&C server. Finally, Backward Pass is performed to update weights throughout the network. Backward Pass is initialized at output layer and carried out by propagating error signals backwards from output layer to each hidden layer until input layer.

Another work selected for comparison is [79], which is also based on static analysis. It uses Permission and API calls as the feature vector and evaluates the results with various machine learning approaches such as SVM, Bagging and C4.5. For comparison, we selected the best results obtained by the model using the SVM classifier and achieved 96.69% accuracy. In addition, authors in [77] proposed an Android malware detection system using Bayesian algorithm with static feature set including permissions and API calls. The authors conducted experiments on 1000 malware samples with various module constructions and achieved 98% accuracy for 15M-based classifier model.

SMARTbot uses the dynamic feature space and selects the features which show the behavior of mobile applications in terms of botnet actions, as presented in Table 3. To measure the reliability of our classifier, we further applied random sampling method to our selected datasets. For random sampling, we assigned 66% training data instances and 33% for test dataset.