Combining the unique strengths of humans and machines for better security outcomes
Today’s security landscape is changing very fast. The number of cyberattacks each day has risen from a mere 500 to an estimated 200,000-500,000. The volume of threats and information that must be processed is greater than humans alone can manage. We need the speed of machines to process, adapt, and scale.
But we need humans too, to match and outmatch the wits and ingenuity of the human attackers on the other side of that code. In short, we need teams of humans and machines, learning and informing each other—and working as one.
Layers of artificial intelligence for each level of task
As humans have defined and refined advanced analytics, data scientists and technologists have recognized that there is an evolution of complexity towards more predictive and cognitive forms of computing. These levels, as depicted here, build one upon another toward the goal of better and faster intelligence.
Evolving machine learning for a better threat defense
Recent research highlights the need for machine learning for advanced detection capabilities. McAfee is evolving its machine learning cybersecurity technology to even more complex analytics called deep learning and artificial intelligence. Deep learning is the machine learning-based analytics approach that uses many layers of mathematical neurons—much like the human brain. It provides reasoning and a feed-forward or backward convolution of decision-making. Artificial intelligence adds complexity to deep learning, appending reasoning, suggested actions, and problem solving, often working in an n-dimensional space (like the brain). Machine learning, deep learning, and artificial intelligence are mathematically more complex as the computation becomes more brain- and human-like.
Putting the human-machine team to work
Machine learning and deep learning are pervasive within the McAfee product portfolio. McAfee Endpoint Security suites feature the machine learning capabilities of Real Protect. Our suite of advanced threat analysis solutions, McAfee Advanced Threat Defense and McAfee Cloud Threat Detection, use deep neural networks to provide advanced malware behavior analysis. Learn more about our offerings featuring machine learning and deep learning capabilities below.
Our dynamic endpoint threat defense solution applies state-of-the-art machine learning techniques to identify malicious code, combat emerging threats, and radically simplify security operations. McAfee Endpoint Security suites, part of this solution, include Real Protect technology that combines pre-execution static analysis and post-execution behavioral analysis to stop more malware than any signature-based or static-only solution.Learn More >
McAfee Advanced Threat Defense provides in-depth static code analysis that enhances behavioral malware analysis and sandboxing capabilities to detect hidden, evasive threats. It also looks for malicious indicators that have been identified through machine learning via McAfee’s cloud-based deep neural network. This unparalleled analysis generates both summary reports that help you understand the scope of an attack and prioritize actions, and highly detailed reports with analyst-grade data on malware.Learn More >
McAfee Investigator helps analysts close more cases faster with higher confidence that they’ve determined the root cause. This cloud-based service provides triaged alerts that trigger expert-led exploration of relevant SIEM and real-time endpoint data. Data can come from everywhere, including endpoints and SIEM solutions, replacing silos with contextual visibility into IOCs, tactics, techniques, procedures, and relationships.Learn More >
Advanced Threat Detection Powered by the Cloud
Learn more about how McAfee Cloud Threat Detection leverages machine learning to uncover malicious indicators, identify advanced malware, and automate protection.Watch Video
Learn more about machine learning and artificial intelligence
This white paper draws an analogy between machine learning and analysis of the behaviors and characteristics of autonomous cars in various scenarios.