Moreover, a similar trend was observed for unsuccessful DNS queries generated by the botnet applications, i.e it is higher than malware applications. 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. https://www.xcritical.com/ Thus, HTTP attacks generated by GET requests are simpler to create, and can more effectively scales in a botnet scenario [54]. Fig 20 shows the number of HTTP connections opened/established by botnet dataset. It clearly shows that, 92% of botnet dataset established TCP connection, whereas only 33% malware do so.
From the Fig 8, it can be concluded that the simple logistic regression performs the best in terms of accurately classifying the Drebin dataset with 99% using the selected feature vector. Similarly, simple logistic regression has the highest recall rate of 100% from its counterpart classifiers while having the minimum FNR of 0. However, the TPR of MLP is slightly improved than simple logistic regression (0.97) which is 0.99. Moreover, the FNR for Naive Bayes, SVM, J48, and RF are 13%, 12%, 3% and 2% respectively. In order to learn runtime behavior of botnet applications we have chosen 36 malicious applications that belong to 49 different malware families [21]. While Maya started as a chat BOT that answers redundant questions, over a period of time, the scope of Maya grew.
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Similarly, Table 6 depicts the learning time comparison between 10-fold cross validation and random sampling. Training time is ranging from 1.9957s to 3.5611s in 10-fold cross validation whereas random sampling requires 0.036s to 8.076s to train the model. Additionally, testing time required by 10-fold cross validation ranges from 0.0321s to 0.0691s which is better than existing machine learning based mobile malware detection solution, Mobile-Sandbox [76]. Likewise, the time taken to process testing classifier model during random sampling is 0.018s to 3.90s.
- SmartBot360’s AI is trained exclusively with real patient chats to improve understanding of healthcare interactions for accurate responses.
- Smartphones are connected to the Internet, and the C&C functionality for Android botnets is constantly controlled through the network.
- 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.
- The two common types of mobile malware analysis approaches include static or code-based and dynamic or runtime execution analyses.
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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.
In addition to that, the service availability constraints of Andrubis are also present even when the service is unavailable, disrupted or malfunctioning. Second, the use of sandboxing technique is another limitation; various approaches [82] have been introduced by the researchers to determine if the execution platform is a sandbox machine or a real device. For instance, Obad botnet tries to evade execution on several sandboxes using anti-decompilation or anti-emulation approaches. It does so by checking the value of Android.os.build.MODEL, if the value indicates the existence of emulator, the application stops execution immediately [7,83]. From the Fig 25 we can conclude that, the most commonly observed opened network connections for botnet dataset occurred on port 80 (HTTP, 92% of samples), port 443 (HTTPS, 69%), port 123 (NTP, 44%) and port 13 (Daytime, 9%).
Features
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. Message Digest (MD5) is a widely accepted standard for enforcing message integrity during the network communication.
However, recently researcher have found some serious security concerns in the form of collision attacks [72] and replay attacks [73]. Therefore, recent studies [74] not encourage users from adopting this option. The https://www.xcritical.com/smartbots/ results regarding MD5 misusage by botnet and malware applications are shown in Figs 11 and 12 respectively. We observed high spikes when digest operations were misused in a large number of botnet applications.
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SOCi SmartBot combines advanced machine learning and natural language processing to deliver a localized and conversational chatbot experience — across every location. Our data driven test studio allows you to simulate typical behavior of real humans interacting with the Conversational AI assistant and perform every type of testing. SmartBot360 combines the best of both worlds, by allowing your organization to create and maintain simple or complex AI chatbots in a DIY fashion, and only request expert consultation when needed. Patients can type their questions and get an immediate answer, leave a message, or escalate to live chat. Whether it’s creating or optimizing a chatbot, our healthcare chatbot experts can work with you to set up a chatbot according to your goals. Improve the support experience of new and existing patients while reducing call center load & wait times.
All the experiments are performed in a powerful feature of Weka workbench [66] known as Weka Experimental [67]. It has a GUI explorer built-in for experimenting machine learning algorithms on big datasets, and robust enough to produce a large number of experimental results needed for evaluation and comparison. Normally, the validation in machine learning classifiers is performed in two different ways to assess accurate performance measures for classifiers.