Original article was published by Jarvis+ on Artificial Intelligence on Medium
What Impact Does Artificial Intelligence Have on Network Security?
In the first half of 2020, the globalized telecommuting caused by the epidemic further magnified the threat from “human loopholes”, and the market’s demand for security awareness services also began to grow rapidly.
Artificial intelligence (AI) may bring new possibilities for network security threat protection.
In the network security defense system of modern enterprises, personnel are the “vulnerabilities” that hackers are easiest to break through and exploit, and employees are still the biggest security threat to network attacks.
According to a report by Tessian, 33% of employees in the United States and the United Kingdom made security mistakes at work, posing cyber security or data security threats to themselves or their company.
When asked what type of mistakes they made, a quarter of employees admitted to clicking on links in phishing emails at work.
In addition to clicking on malicious links, 58% of employees also admitted to sending work emails to the wrong recipients, and 17% of them were sent to the wrong outsiders.
This simple mistake can have serious consequences for individuals and companies, and they must report the incident to regulators and their customers. In fact, one in five respondents said that their company lost customers due to sending wrong emails, while 12% of employees lost their jobs.
Cyber security is very important because it covers all our sensitive data, personally identifiable information (PII), protected health information (PHI), personal information, intellectual property, data, government and industry information systems, etc., and protects these data Free from theft and vandalism. As the entire world becomes more and more digital, cybercrime has now become one of the greater threats to all businesses and government organizations worldwide.
In recent years, many research results have emerged in artificial intelligence technologies such as Agent systems, neural networks, consultant systems, and machine learning in network security defense. In general, artificial intelligence currently focuses on network security intrusion detection, malware detection, and situation analysis.
Analysis and other fields.
Intrusion detection technology
Intrusion detection technology uses various methods to collect, filter, and process data such as abnormal network traffic, and automatically generate security reports for users, such as DDoS detection and botnet detection. At present, neural networks, distributed Agent systems, and advisory systems are all important artificial intelligence intrusion detection technologies. This is also the most commonly used artificial intelligence technology in the field of network security. Traditional intrusion detection technology has shortcomings in detection speed, detection range and architecture. In order to make up for these shortcomings, the intelligent intrusion detection system uses artificial intelligence technology such as fuzzy information recognition, rule generation expert systems, data mining and artificial neural networks to improve the efficiency of intrusion detection, and can resist viruses from all parties to the greatest extent Potential threats caused by invasion.
Intelligent interception system
Nowadays, computer networks are developing rapidly, and e-mail is widely used to facilitate people’s work and business activities. For some criminals, use this feature to inject viruses into them. When the mail is delivered, the virus will be introduced into the network. When the mail or link is opened, the virus will be injected into the computer, affecting the operation of the computer, resulting in loss of information or information. damage.
In response to this situation, the application of artificial intelligence in the anti-spam system can not only protect the security of user data, but the most important thing is to detect and scan user emails and perform intelligent identification, timely discover sensitive information in them, and take effective preventive measures. Prevent malicious emails and protect users from spam harassment.
Intelligent firewall system
Firewalls have been widely used as network security equipment. The firewall delineates a protection scope and assumes that the firewall is the only exit, and then the firewall decides whether to pass or block incoming and outgoing packets. Traditional firewalls have a major theoretical assumption-if the firewall refuses the passage of certain data packets, it must be safe because these packets have been discarded. But in fact, the firewall does not guarantee the safety of the data packet that is allowed to pass. The firewall cannot determine the difference between a normal data packet and a malicious data packet, but requires the administrator to ensure that the packet is safe. The recognition technology cited by the intelligent firewall can analyze and process the corresponding data well by itself. At the same time, it can subtly integrate the proxy technology and the filtering technology. It can not only reduce the computer’s calculation of the data, but also broaden the monitoring range, effectively Block data streams that are harmful to the network, so as to better protect the security of the network environment.
Predictive malware defense technology uses machine learning and statistical models to find the characteristics of malware families, predict the evolution direction, and defend in advance. At present, with the continuous increase of viruses and malware and the emergence of ransomware, enterprises have an urgent need for malware protection, and a number of related product systems using artificial intelligence technology have emerged on the market. In September 2016, security company SparkCognition created DeepArmor, an AI-driven “cognitive” antivirus system that can accurately detect and delete malicious files and protect the network from unknown network security threats.