In recent times have seen quite a few breakthroughs with regards to the use of machine learning algorithms for enhancing the operations of business beyond the thoughts and capacity of the human mind. While benefits of this have been achieved and are evident across sectors, the same benefits are enjoyed by the dark side of cyberspace which is responsible for brewing myriad of sophisticated cyber threats.
Hackers are always looking to improve their Return on Investments when it comes to cybercrime and they keep finding sophisticated and innovative ways to do that. Machines have always been used to better humans at a strategy that has been very well documented by video games in which humans are pitted against machines. In such a scenario we deduce that machines blossom into anticipating the moves of its adversaries and construct a plan to foil their plan within minutes. The same can be applied to enterprise security and by enlisting the power of machine learning, organizations can create their own offensive cyber strategy.
To overcome specific challenges, most growth in the use of algorithms in the commercial environment relies on unsupervised machine learning which is often simplified as Artificial Intelligence. Machine learning finds its presence in everything, right from autonomous vehicles to personal voice assistants to recommendation engines on e-commerce websites. In hindsight, traditional data has looked to summarize information and produce trends. When it comes to cybersecurity, the end goal is to gather historical information on attacks and signatures, with the long term goal being gathering new insights by anticipating new behavior that is beyond the operational norms.
Birth of the offensive AI
It is pretty obvious that due to the sophistication in technology, hacker groups gain the most when they target larger organizations. It could be a coordinated effort against a large organization or a group of large organizations but with the help of machine learning the scale of the attack is growing and with a similar level of effort, co-ordinated attacks now reach hundreds and thousands of organizations.
In current day scenario within the attack framework attackers are implementing machine learning on various levels which includes, reconnaissance, intrusion, c2 establishment, privilege escalation, and lateral movement while utilizing existing knowledge bases to its full extent. Such technology gives an added advantage to the attackers in deciding on what data to be held as a hostage.
In addition to creating awareness in businesses about the advancements of machine learning, CSIO’s need to address the following issues as well.
• How can organizations defend against automated attacks?
• What kinds of AI-powered attacks are occurring in the world today?
• What machine algorithms are used to executing these attacks?
To read more, please check eScan Blog
