Not all Artificial Intelligence is created equal. As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Dr. Igor, Chief Scientist and CTO at MixMode explains: Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning).
I know how terrible healthcare records theft can be. I myself have been the victim of a data theft by hackers who stole my deceased father's medical files, running up more than $300,000 in false charges. I am still disputing on-going bills that have been accruing for the last 15 years. This event led me on the path to finding a solution so others would not suffer the consequences that I continue to be impacted by, but hospitals and other healthcare providers must be willing to make the change. The writing is on the wall.
Machine learning-powered tools promise to transform threat detection and threat hunting capabilities in security operations centers (SOCs). Industry experts see ML as helping SOCs automate and improve analysis of event and incident data gathered from enterprise security devices and myriad other network-connected systems. The massive and growing volumes of data from these devices in recent years have made it increasingly difficult for security operations (SecOps) teams to detect, triage, prioritize, and respond to threats--resulting in heightened risk exposure. Traditional security information and event management (SIEM) systems and other alerting mechanisms that use static rules and thresholds, while effective against known threats, have run into challenges with new, low, and targeted attacks. In a survey that Crowd Research Partners conducted last year, more than half of the respondents (55%) cited their inability to detect advanced threats as the biggest challenge for SOCs.
Second in a series of two articles about the history of signature-based detections and how the methodology has evolved to identify different types of cybersecurity threats. Many security vendors are now applying increasingly sophisticated machine learning elements into their cloud-based analysis and classification systems, and into their products. All of these techniques have already proven their value in Internet search, targeted advertising and social networking business arenas. For example, supervised learning models lie at the heart of ensuring that the best and most applicable results are returned when searching for the phrase "never going to give you up." In the information security world, supervised learning models are a natural progression of the one, two, and multi-dimensional signature systems discussed in my earlier article.
Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research. AI has made some inroads in the cybersecurity sector and several AI vendors claim to have launched products that use AI to help safeguard against cyber threats. At Emerj, we've seen many cybersecurity vendors offering AI and machine learning-based products to help identify and deal with cyber threats. Even the Pentagon created the Joint Artificial Intelligence Center (JAIC) to upgrade to AI-enabled capabilities in their cybersecurity efforts.