SmartBotAI Chatbot SmartBot AI-Powered Customer Support Solution for Shopify Shopify App Store

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For example, AndrubisAndrubis [20], DroidBox [31], DroidScope [39], APK Analyzer [40], or APKScan [41] are some of the tools recently adopted and tested by research and development community. DroidBox is an open source package for dynamic analysis which cannot be used explicitly for large datasets because of its limited resultant parameters and deficiency to execute latest Android applications. In contrast, Andrubis provides an automated cloud based malware analysis platform which can generate reports with rich parameters (static and dynamic). Therefore, we have selected Andrubis (SaaS) sandbox in order to execute and collect network traces. Through an automated script we have uploaded the whole dataset of Drebin to Andrubis and obtained the dynamic analysis results in XML files.

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. According to [7], Obad botnet has the most sophisticated design as it can exploit several unexplored vulnerabilities in Android OS.

  • Exiting approaches employ static, dynamic or hybrid approaches with varying dataset sizes and focus on general malware detection; therefore, direct comparison is not feasible.
  • In addition to that, the service availability constraints of Andrubis are also present even when the service is unavailable, disrupted or malfunctioning.
  • The use of a bot in their case allows sites to be catalogued much faster and more scalably than humans could accomplish alone.
  • An AI-powered chatbot that will respond to all customer requests 24×7 – only faster.

They’re very good at what they do, but they’re unable to mimic conversational type language and their capabilities are basic. An AI-powered chatbot that will respond to all customer requests 24×7 – only faster. It offers personalized product recommendations and instant answers to frequently asked questions, making it easy for visitors to find what they need. Additionally, SmartBot empowers customers with self-service order inquiries, providing order tracking information without human assistance.

Smart robots can collaborate with humans, working along-side them and learning from their behavior. An early example of a smart robot is Baxter, produced by Rethink Robotics of Boston, Massachusetts. Baxter is an industrial android (humanoid robot) that can work right next to line employees on the factory floor, often working on highly repetitive tasks such as precision packing.

Moreover, Figs 8 and 9 derive the overall performance of classification algorithms when applied on Drebin dataset. 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.

what is smartbot

Training set consists of malicious samples not having C&C properties and well-known mobile botnet applications. As the system is specific for botnet detection, therefore we have selected features which are most relevant to a botnet life cycle which includes connection, infection and resilience. Consequently, training function computes the conditional and marginal probabilities in order to formulate algorithm for the final classification decision. As we described earlier, botnets initiate large number of services as compared to benign or malware applications.

We make comparison with respect to model efficacy, scalability and performance comparison. Furthermore in this section, we provide a case study that helps to demonstrate the usability of our framework. In the ever-evolving landscape of e-commerce, enhancing customer engagement and driving sales is essential. As a seasoned product manager with a decade of experience, I’m excited to introduce you to SmartBot — a powerful AI chatbot exclusively designed for Shopify merchants.

A short summary of the selected sample dataset is presented in Table 2. We labeled this sample dataset (either malware or botnet), which became the baseline for the dynamic feature selection and was used to train our neural network model. Ultimately, our framework employs the same sample set for learning the behavioral properties of botnet applications. After executing these applications in a sandbox, we collected the features that are most relevant to a botnet activity. The execution time for feature selection was 2 minutes, and the resultant schema was stored in a CSV file for further analysis using a Python script. For the evaluation, we selected the Drebin dataset because it is currently the largest malware dataset that is publicly available.

what is smartbot

In conclusion, SmartBot is the ideal AI chatbot for Shopify merchants looking to streamline customer interactions, boost sales, and ensure customer satisfaction — all without any need for human intervention. Its autonomous operation, continuous learning capabilities, and personalized engagement features make it a valuable asset for Shopify stores of all sizes. With SmartBot, you can create meaningful interactions with your customers, drive conversions, and achieve greater success in the competitive world of e-commerce. Welcome to effortless and effective customer engagement with SmartBot for Shopify.

what is smartbot

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. The other type of chatbot, or smartbot as we’ll call it, is data-driven and predictive chatbots.

For random sampling, we assigned 66% training data instances and 33% for test dataset. Although, we obtained similar results while choosing the best option between cross validation and random sampling, yet 10-fold cross validation generates smart bot 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.

The 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. On the average, each botnet application misused 14±2 digest operations, whereas only 12 malware samples misused 3±1 digest operation on the average. To measure the reliability of our classifier, we further applied random sampling method to our selected datasets.

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. 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.

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