Report: Cybersecurity in Q4 2017

The 21st century is undoubtedly the age of technology. Computers, laptops, smartphones, tablets, IoT devices, cars and also people and companies are connected to the network. It is estimated that their number will exceed 50 billion by 2020. Such a solution facilitates work and everyday life, but at the same time raises threats on a previously unknown scale. Modern cybersecurity is facing many…

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Imagine that you live in the world without any protection. But there is a place where all information concerning you is stored together with the balance of your bank account. Everyone can access this place and change its data, pay in or pay out money from its account but also... modify other people's data. Even in the perfect world, there will be a person who will want to steal your…

The cryptocurrency market is very attractive not only to investors and private persons but also to cybercriminals. Virtual currencies enable a quite rapid manner of earning a lot. In July, there was information that Ethereum virtual currency of the value of USD 34 mln was stolen. Ethereum - an alternative for Bitcoin? Ethereum cryptocurrency (ETH) is an alternative for Bitcoin. Ethereum is…

Q2 2017, as far as cybersecurity is concerned, compared to the previous years, is not optimistic. How do the statistics of conducted DDoS attacks look like in Q2 2017? Kaspersky Lab report “IT threat evolution Q2 2017” demonstrates that there are more cyberattacks directed at China and USA. The longest DDoS attack in Q2 2017 lasted 277 hours what means 131% increase compared to the…

The extended duration time of attacks and their frequency demonstrate an increasing tendency. The last few months showed that DDoS attacks were more frequently used as a tool of a political fight which was driven by money. What direction cyberattacks are heading? What does the report prepared by Kaspersky "IT threat evolution Q2 2017" emphasise? Cryptocurrency market renaissance Money has…

This article is part of a series. Part I can be found here and part II here . The full code of this tutorial was posted on Github . In the previous part we continued work on our binary classifier. We showed how to use scikit-learn's Pipeline class to combine feature calculation and prediction into one simple step. In this part we will improve performance of our model by adding new…

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