Fortinet Predicts Organizations Will Employ More Automation To Combat Threats

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Fortinet unveiled predictions from the FortiGuard Labs team about the threat landscape for 2019 and beyond. These predictions reveal methods and techniques that Fortinet researchers anticipate cybercriminals will employ in the near future, along with important strategy changes that will help organizations defend against these oncoming attacks. For a more detailed view of the predictions and key takeaways for CISOs,visit the blog.

Cyberattacks Will Become Smarter and More Sophisticated: For many criminal organizations, attack techniques are evaluated not only in terms of their effectiveness, but in the overhead required to develop, modify, and implement them. As a result, many of their attack strategies can be interrupted by addressing the economic model employed by cybercriminals. Strategic changes to people, processes, and technologies can force some cybercriminal organizations to rethink the financial value of targeting certain organizations. One way that organizations are doing this is by adopting new technologies and strategies such as machine learning and automation to take on tedious and time-consuming activities that normally require a high degree of human supervision and intervention.

These newer defensive strategies are likely to impact cybercriminal strategies, causing them to shift attack methods and accelerate their own development efforts. In an effort to adapt to the increased use of machine learning and automation, we predict that the cybercriminal community is likely to adopt the following strategies, which the cybersecurity industry as a whole, will need to closely follow.

Artificial Intelligence Fuzzing (AIF) and Vulnerabilities: Fuzzing has traditionally been a sophisticated technique used in lab environments by professional threat researchers to discover vulnerabilities in hardware and software interfaces and applications. They do this by injecting invalid, unexpected, or semi-random data into an interface or program and then monitoring for events such as crashes, undocumented jumps to debug routines, failing code assertions, and potential memory leaks. Historically, this technique has been limited to a handful of highly skilled engineers working in lab environments.

However, as machine learning models are applied to this process we predict that this technique will not only become more efficient and tailored, but available to a wider range of less technical individuals. As cybercriminals begin to leverage machine learning to develop automated fuzzing programs they will be able to accelerate the process of discovering zero-day vulnerabilities, which will lead to an increase in zero-day attacks targeting different programs and platforms.

Michael Joseph,Director System Engineering, India & SAARC at Fortinet, said, “We are seeing significant advances in cybercriminal tools and services which leverage automation and the precursors of AI. Organizations need to rethink their strategy to better anticipate threats and to combat the economic motivations forcing cybercriminals back to the drawing board. Rather than engaging in a perpetual arms race, organizations need to embrace automation and AI to shrink the windows from intrusion-to-detection and from detection-to-containment. This can be achieved by integrating security elements into a cohesive security fabric that dynamically shares threat information for broad protection and visibility across every network segment from IoT to multi-clouds.”

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