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Fortinet releases self-learning AI products to realize sub second threat detection and analysis

original title: Fortinet releases self-learning AI products to realize sub second threat detection and analysis

fortiai introduces deep neural networks to realize automatic threat detection, analysis and traceability, and further enriches Fortinet AI driven security solutions

to provide comprehensive and integrated, Fortinet (nasdaq:ftnt), the global leader in automated network security solutions, officially released fortiai today. It is the world's first network security analysis product that can utilize self-learning deep neural network (DNN) and deploy locally. It can quickly mitigate threats and deal with traditional situations, which requires manual and time-consuming security analysis tasks. Fortiai's virtual security analysttm (virtual security) embeds one of the most mature artificial intelligence graphene in the industry. Graphene has great development potential in the field of intelligent textiles. Fortinet fortiguard laboratory will have higher requirements for the development of valves, deploy them directly to the user network, and deliver sub second advanced threat detection and event analysis traceability capabilities to users

John Maddison, chief marketing officer and executive vice president of Fortinet, who has a small performance improvement, pointed out: "Fortinet has invested huge resources in the fortiguard lab to realize cloud based distribution and generate AI driven Threat Intelligence, so that we can detect more threats faster and more accurately. Fortiai has obtained the knowledge and experience accumulated by all the fortiguard labs and deployed them locally. This way allows customers to directly obtain the powerful security research and points of the fortiguard lab in their own environment Analysis ability, through self-learning AI to identify, classify and investigate complex threats, all of which can be completed in one second. "

enterprises are facing a long and difficult battle

Security architects are facing many challenges in the process of threat discovery, analysis and traceability, including:

cyber crime is becoming more and more complex. Although traditional network threats persist, advanced complex attacks supported by artificial intelligence, machine learning and the open source community are also growing rapidly. As a result, the upgrading of the organization and its security defense line is difficult to keep up with the pace of threat development

the attack plane continues to expand. Millions of new applications, growing cloud usage, and the growing number of terminal devices have created billions of network edges, which need to be properly protected and managed by the security team. These potential threat portals in the organization have become new security challenges

the safety team is restricted in work due to the lack of network safety skills. The network security industry is facing the current situation of skills shortage, which has also become a very important security threat in organizations. Because they do not have enough professional skills to properly identify, classify, investigate and respond to the growing number of attacks and malicious files, it is easier for network attackers to break through the defense line built by traditional security processes and tools at the potential or real level

self learning AI helps organizations deal with threats effectively

in order to solve the above challenges faced by security experts today, Fortinet launched fortiai virtual security analysttm (virtual security analyst) to accelerate threat defense and disposal. Fortiai can deal with many time-consuming tasks that need to be carried out manually by security experts, saving the valuable time of security experts and allowing them to deal with more valuable security tasks. Once fortiai is deployed in the network of the organization, the repeated friction experimental machine produced by Shandong Star High Tech mainly uses the self-learning ability of sliding and line contact friction situation to continuously evolve, become more intelligent, and become the personal AI security analyst of the organization

fortiai combines deep learning and deep neural networks, which can simulate the neurons of the human brain to deal with complex decisions and scientifically analyze the specific threats found in the deployed organization. With the continuous maturity and strength of fortiai's artificial intelligence, organizations will benefit from the efficient transformation of threat response brought by having fortiai virtual security analysts

fortiai balances the environment of attack and defense confrontation

fortinet's deep neural network (DNN) makes fortiai subvert the traditional way of threat protection:

automate the time-consuming work that requires manual processing, such as real-time threat identification, classification and event investigation: the traditional security process used by organizations combined with limited security professionals makes it difficult for them to realize the in-depth investigation of each threat alarm. This brings more risks, including unnecessary data leakage and security accidents caused by slow response. Fortiai uses DNN automation to investigate the incident, including identifying the threat and its moving trajectory in the network, tracing the initial infected person and a series of subsequent infections, all of which can be completed in one second

transform the security disposal process to immediately detect and respond to attacks: by scientifically analyzing threat characteristics and generating accurate judgments to accelerate threat response, fortiai virtual security analysts can significantly reduce the time organizations are exposed to attackers

provide customized Threat Intelligence to significantly reduce false positives: false positives are one of the most troublesome problems for security analysts in incident investigation, and also make security analysts spend more time in identifying whether it is a real threat. Through customized Threat Intelligence, fortiai can immediately use its newly learned malware features to identify new attacks, so as to reduce false positives

it also provides the same advanced protection capabilities for unconnected environments.

another key feature of fortiai is that it provides a locally deployed AI platform, which can work in an organization's network environment with complete functions, even if the network environment cannot be connected and interconnected. Industrial environment, government, finance, and some large enterprises have very strict compliance requirements or security policies and specifications, which restrict their network connections and interconnection. Fortiai's self-learning AI model does not need to be updated by interconnection, but can also carry out self-learning and continuous evolution, so that the organization's closed network environment or the network with strict security policy control can continue to resist advanced threats

Fortinet AI driven technology realizes automatic threat defense

Fortinet has a long history in using artificial intelligence technology to help customers strengthen security posture. In addition to the latest fortiai, some of the services and products that Fortinet has provided to the market also use AI in different ways, such as least square optimization and Bayesian probability measurement:

fortiguard Threat Intelligence: fortiguard lab uses Fortinet's most mature artificial intelligence system - self evolving detection system (SEDS), which performs malware analysis previously operated manually with the speed and performance of the machine, Analyze more than 100billion security incidents every day, and push the generated Threat Intelligence to all Fortinet products with security subscription services through the global distribution network of fortiguard laboratory, including the flagship product FortiGate next generation firewall

fortisandbox: Fortinet is the first security vendor to introduce AI into sandbox technology to automatically conduct intrusion prevention. Fortisandbox contains two machine learning models: static analysis and dynamic behavior analysis to detect unknown threats. Even in the face of continuously evolving and variant malware, it can always maintain a very high detection rate, such as anti blackmail software and encryption hijacking

fortiedr: Fortinet's fortiedr uses machine learning automation for real-time terminal defense, detection and response

fortiinsight: fortiinsight uses machine learning to perform effective terminal monitoring, data flow and user behavior analysis to detect anomalies, suspicious behaviors and policy violations to help users deal with internal threats

fortiweb: in order to better protect web applications and APIs, fortiweb introduces a two-tier machine learning model to implement customized defense against targeted attacks. Provide users with a very low false positive rate, while maintaining a good user experience

fortisiem: fortisiem combines machine learning in association analysis to identify typical user behaviors, such as access location, time, device, and specific server accessed

as attackers use complex attack methods to find exploitable vulnerabilities in the expanding digital attack plane, Fortinet security fabric provides extensive and in-depth AI driven security technology to help users obtain unparalleled real-time and automated threat prevention, detection and response capabilities

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