Automate Customer Interactions and Deliver Personalized Customer Experiences

As for as DNS response record is concern, 95% of the botnet applications receive (average of 2.7) DNS server replies, whereas only 48% malware samples receive (average 0.9) DNS response. Fig 14 shows the response generated by DNS server also known as DNS_TYPE_A_Requests. Similarly, the total number of unsuccessful DNS queries is presented in Fig 15. On the average, the DNS server’s responses for botnet applications are more than those for malware samples.

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. 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.

They’re very good at what they do, but they’re unable to mimic conversational type language and their capabilities are basic. 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. Save time by collecting patient information prior to their appointment, or recommend services based on assessment replies and goals.

It clearly shows that, 92% of botnet dataset established TCP connection, whereas only 33% malware do so. Moreover, the average connections established by each botnet and malware application are 10 and 2.5 respectively. In order to get insight into the HTTP traffic, we also observed the GET requests initiated by both datasets. It can be seen from the Figs 21 and 22 that 40% of the botnet applications use GET command for communication, however, 23% of the malware samples use this feature to communicate externally via HTTP. Fig 13 shows the comparison of the top 1746 botnet and malware applications with respect to DNS requests.

Bots are not robots and they don’t symbolize the end of marketing or sales as we know it. You might have noticed these terms and others like them increasingly pop across your screen. While they each represent technological advances, it’s important to know the meaning of each and how they differ to ensure you remain informed on how they can impact you.

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.

Smart robots have the capacity for not only manual labor but cognitive tasks. Erik Brynjolfsson and Andrew McAfee, authors of “The Second Machine Age,” maintain that technological advances have led global culture to an inflection point rivaling that brought about by the industiral revolution. When thinking about bots, however, it’s important to maintain perspective. They can serve a variety of purposes and what they ultimately accomplish is dependent on the humans that control them. Search engines like Google extensively use bots, often known as web crawlers, to analyze content and index the web. The use of a bot in their case allows sites to be catalogued much faster and more scalably than humans could accomplish alone.

• The reason to choose ANN’s backpropagation modeling is that, during our initial classification results, MLP outperform in terms of accuracy, precision and recall rates when applies to sample dataset.
• 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.
• Overall 96% of botnet applications perform DNS requests, in opposite only 51% of malware samples requested DNS queries.
• Moreover, a similar trend was observed for unsuccessful DNS queries generated by the botnet applications, i.e it is higher than malware applications.
• 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.
• Smart robots have the capacity for not only manual labor but cognitive tasks.

Consequently, training function computes the conditional and marginal probabilities in order to formulate algorithm for the final classification decision. Mobile botnet applications rely not only on permissions but also on different API functions, such as Connect, openConnection, execute, URL and Socket etc. The results in Fig 24 affirm that botnets are more interested in using these network methods than malware.

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. 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.

It clearly indicates that the botnet applications request more DNS queries than the malware applications. On the average, each botnet application requests 4.4 DNS queries, whereas on the average each malware initiates 3.1 times DNS requests. Overall 96% of botnet applications perform DNS requests, in opposite only 51% of malware samples requested DNS queries. Another important factor to smartbots assess the botnet intuition is to determine the frequency of failed DNS queries. This also affirms our classifiers’ accuracy that botnet dataset has higher failure rate with respect to DNS queries, while malware has lower rate of failed DNS requests. Consequently, 80% of the botnet applications have failed DNS requests, while only 28% of the malware samples have failed DNS requests.

The process is carried out through loading far more ads than any benign application would—more than 20 ads per minute. 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.

Moreover, a similar trend was observed for unsuccessful DNS queries generated by the botnet applications, i.e it is higher than malware applications. Figs 4–7 and show the accuracy rates (in percent), precision, recall, F-measure for Drebin dataset using 10-fold cross validation. 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.

Digital ledger transactions are recorded with an immutable cryptographic signature called a Hash. 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. 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. An intelligent bot with chat interface for 40% increase in average response time. With an Alexa-like interface, do more than just a typical bot interaction.