This post is also available in: עברית (Hebrew)
Deep Instinct, the first company to apply end-to-end Deep Learning to cybersecurity, recently released its biennial Threat Landscape Report.
The team of researchers responsible for the report analyzed and tracked a variety of cyber attacks to formulate a model that would predict what the next cyber attack would be about, figure out what the attackers are motivated by, and outline the various steps the organization could take to mitigate it. As noted already last year in the report, there is an increasing trend of attackers who use artificial intelligence or machine learning techniques to pose a new threat to businesses of all sizes.
There has been a dramatic increase in the number of cyber attacks since the outbreak of Corona plague. There has been a substantial increase in specific attack vectors, with a 170% jump in the use of Office droppers (hidden software that streams malicious code into the system), as well as a 125% jump in all cyber threats combined.
Moreover, the report found a change in cyber-attacker capabilities, as they began using newer languages like Python over older programming languages (like C or C ++). It is easier to learn these languages and harder to detect threats based on them.
Supply chain attacks, cloud attacks that became common with the shift to a hybrid model, and more were reviewed in the report.