Today is June 14th, so I am 14 days into summer school; 7 more days left, and we are all already feeling saddened by the idea of leaving Kharagpur soon. In India, an IIT is a dream for 90% of the 12th graders who join IIT coaching classes. The competition is high so not everyone gets in. I’m one of those who didn’t get in. So when I saw there was an ACM Summer School opportunity at the largest and oldest IIT in India, obviously I grabbed it. By sheer luck, I was selected to actually attend the school. Over the course of 21 days, we have been tasked to learn about machine learning and natural language processing. Continue reading
In my previous blog post, I described some of my findings regarding malicious mobile apps. In summary, I observed that there are POSIX abstractions, which are popular only for malicious apps. The findings were derived from a study that I did with some colleagues on POSIX (Portable Operating System Interface) abstractions. Recall that, a part of our study involved the examination of the POSIX calls that are used by both benign Android applications (~1 million) coming from the Google Play Store, and malicious Android applications (about 1260 of them) taken from a well-known dataset, which you can download from here.
We performed a further analysis on these results to check if we can create a more robust filter to detect malicious apps, than the simple filter described in my previous post (recall that this filter was based on the three most unpopular abstractions among benign applications and at the same time popular among malicious ones). Our attempt involved the following: we fed a set of benign apps (the 500 most popular apps of the Google Store) and the aforementioned dataset of the malicious apps, to an SVM (Support Vector Machine), a binary classifier that builds a model based on given features (abstractions in our case) to separate the two cases. In this way the classifier can classify a new app as malicious or not. By using the model on the same set of apps that we examined in the previous case, 1283 apps were identified as suspicious. Based on the antiviruses provided by the VirusTotal website again, we found that from these apps, 232 (18%) are potentially malicious. Even if the approach seems less robust than the previous one, Figure 1, illustrates that there are more cases of apps that were indicated as malicious by more than one antivirus. Table 1, presents applications that were filtered out by the SVM model, and were identified as malicious by more than 15 antiviruses.
Through our experiments, we came across a number of Android apps that included obfuscated libraries (991 apps in total). Given the fact that obfuscation techniques have been extensively encountered while analyzing Android malware, we decided to examine all the apps that contained such libraries by using the 54 antiviruses of the VirusTotal website. Surprisingly, almost half of the apps (481 in total — 48.53%) were classified as suspicious. An interesting observation is that the majority of these apps were indicated as potentially malicious by a large number of antiviruses — see Figure 2. Table 2, presents indicative apps that were identified as malicious by more than 22 antiviruses.
As it is clear, a malware detector cannot be based solely on observations like the aforementioned ones. However, such findings could be useful for the development of complex filters that can help find malicious software.
Here are the three winners of our The Time is Write 2.0 competition! You can read the three articles below, but first: congrats to Dipika Rajesh, Aditi Balaji and Pratyush Singh.
The Time is Write is an article writing competition that encourages all the aspiring writers to lay out their thoughts in writin and to share them on a global platform. This year, participants had to write a short article on the topic “Your Dream Software: Revolutionize the future”, about what their idea of a perfect software might be in order to revolutionize a particular field.
Before we begin, let us talk about how Mike (a fictional character) spends a typical morning. Mike begins his day by searching for breakfast recipes on Google Now (https://en.wikipedia.org/wiki/Google_Now). After a filling breakfast, Mike starts getting ready for work. He asks Siri (http://www.apple.com/in/ios/siri/) to tell him the weather and traffic conditions for his drive to work. Finally, as Mike gets ready to leave the house, he asks Alexa (https://en.wikipedia.org/wiki/Amazon_Alexa) to dim the lights and thermostat. It is not even 10 a.m. yet, but Mike like many of us has already used three intelligent personal assistant applications using Natural Language Processing (NLP). We will unravel the mysteries of building intelligent personal assistants with a simple example to build such an assistant quite easily using NLP.