What is smart robot?

SmartBot360’s AI is trained exclusively with real patient chats to improve understanding of healthcare interactions for accurate responses. Our AI uses a three-tier architecture to minimize dropoff and references four data sources to extract relevant answers. Co-create data-driven products and services that leverage on digital technologies. SwitchBot smartbots Bot is compatible with just about any rocker switch or button of any appliance found at home, enabling you to instantly transform old devices into something much smarter. An intelligent bot with chat interface for 40% increase in average response time. All you have to do is ask questions and get them answered instantly by our SmartBots.

SmartBot360’s AI uses data from four sources to have a more comprehensive AI that does not get confused. Aside from setting up the flow diagram, SmartBot360 users can also upload a FAQ sheet that contains keywords and answers, previous chat logs, and pages on their website. AI is important in healthcare chatbots because whenever a patient has an emergency or asks something similar to an existing question, it can answer or direct them to the appropriate page with the next steps to take. Most patients see a chatbot as a way to speak to a live agent or to have their questions answered in an interactive way. Patients expect immediate replies to their queries nowadays with chatbots being used in so many non-healthcare businesses. If they cannot easily navigate to a page with the answer to their question, there is a higher chance of them bouncing and going to another provider.

Normally, the validation in machine learning classifiers is performed in two different ways to assess accurate performance measures for classifiers. One method is called K- fold cross validation [68] and the other is known as random sampling validation [69]. Consequently, we determined that applications that belong to a specific botnet family demonstrate certain C&C communication patterns.

Thus, we proposed in [19] a static analysis approach to detect mobile botnet applications. The hybrid behavioral model proposed by [78] employs an SVM classifier for training and testing purposes and achieves 96.9% accuracy. For this model, a dataset of 3368 malicious applications was used for classification. Another work selected for comparison is [79], which is also based on static analysis.

Proposed Framework

Specifically, each malware application belonging to a particular family performs similar actions while executing remote commands[23,24], sharing information, and implementing request/response mechanisms. In our future work, we plan to devise a hybrid on-device analysis system for the detection of bot behavior using machine learning classifiers. 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.

During the specified running time we have collected the frequencies of feature vector called by those applications. Similarly, what is the total number of opened HTTP connections in order to establish C&C communication? Patients expect immediate replies to their requests nowadays with chatbots being used in so many non-healthcare businesses. A chatbot can either provide the answer through the chatbot or direct them to a page with an answer. 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. As all hidden nodes have collectively contributed in obtained output, they all have effect on generated error signals.

For this purpose, we have used element tree xml API of python [58,59] and regular expressions to build the said feature vector. We propose SMARTbot, a framework which learns to distinguish applications having C&C functionality from malicious corpus through dynamic analysis https://www.xcritical.in/ of Android applications. Our framework is purely based on machine learning techniques that can classify applications based on various features collected at runtime. Fig 2 shows the basic architecture of the SMARTbot framework together with the component hierarchy.

• In contrast, Andrubis provides an automated cloud based malware analysis platform which can generate reports with rich parameters (static and dynamic).
• We propose SMARTbot, a framework which learns to distinguish applications having C&C functionality from malicious corpus through dynamic analysis of Android applications.
• Similarly, Table 6 depicts the learning time comparison between 10-fold cross validation and random sampling.

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. 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. With integration across enterprise systems and self-learning (machine learning) from usage, Maya makes it simple for the field force to access critical business insights. With most commercial chatbots, failures are not handled well, but with Maya, any unanswered query gets logged as a ticket with our employee helpdesk. The unique differentiator is that Maya gets continuously trained on failed questions and is able to answer such questions going forward, thus making it an intuitive technology.

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A chatbot is a type of bot designed to interact with humans conversationally, based on its programming. They automate the process of interacting with your website vistors and social media followers in an attempt to create the best user experience. Ideally, this helps your site maintain the presence of a helping hand, even when you or your team can’t respond. 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. Existing commercial chatbot platforms rely on a set of rules to guide the goal-oriented conversation, but when patients go off-script, it usually leads to the bot not understanding, causing patients to drop-out.

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.

In many cases the ad events are generated when the applications are not being interacted i.e. by enabling various background services/processes. Therefore, the same results reflected in our observation shown in Fig 26 that botnet applications require more services to initiate as compared to malware ones. On average, botnet applications requested 58±10 background services, whereas on the average malware applications calls background services 15±2 times.

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. We have chosen simple logistic regression, NaiveBayes, RandomForest, SVM, MLP and J48 as our classification algorithms to build and test the generated classification model.

As we described earlier, botnets initiate large number of services as compared to benign or malware applications. Previous researchers [27] reported that Android malware usually request for more services, permissions and receiver components as compared to benign applications. This behavior is attributed to, the attempts of Android malware to hide malicious actions while inaudibly executing more background services. A recent report by Forensiq [75] states that mobile botnets are costing advertisers $1 billion in ad fraud by loading bulk of advertisements. The process is carried out through loading far more ads than any benign application would—more than 20 ads per minute.

After which, malware writers changed their motivation to stronger algorithms like, AES and Blowfish. In our analysis we observed that the Blowfish trend in the botnet applications was only 0.2% in all samples and no malware sample used this algorithm. 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.