Artificial intelligence (AI) in Cybersecurity


In a world of advanced technology, the use of anti-malware software or login audit alone cannot be compared with the growing threats, and traditional tools are not enough to ensure network security. By increasing the number and complexity of cyberattacks, experts have been trying to design artificial intelligence-based solutions for a wide range of network security applications. Organizations need more resources and a solid infrastructure to resist any data leakage. To realize this advantage, they need to use AI, ML, and automation technology to defend against malicious intent to strengthen their company and company data.

AI can provide instant insights by collating threat intelligence from millions of research reports, blogs, and news reports, helping you deal with thousands of daily alerts and significantly speeding up your response.

How is Artificial intelligence technology applied to cybersecurity in the industry?

Four major scenarios of Artificial intelligence in cybersecurity

Artificial intelligence is effectively used in:

  • User access authentication
  • Network situation awareness
  • Dangerous behavior monitoring
  • Abnormal traffic identification

Use of AI in password protection and identity verification

Passwords have always been a fragile security control and are often the only link between cybercriminals and our identity. Developers are using AI to improve current biometric authentication and eliminate flaws, making it a robust application. One example is facial recognition technology, currently used on its iPhone X smartphone. AI software generates complex facial models by identifying key similarities and patterns.

(1) User access authentication

As the first line of defense for network security, the system needs to strengthen user access management, accurately identify various camouflage behaviors, and realize the detection of illegal or malicious objects. The system should ensure that users have been authenticated while keeping user data confidential to prevent the malicious collection of user information and other risk events. The current research focuses on AI in the field of user access.

AI Focus on user access authentication

How to match passwords and add other user characteristics to ensure two-factor authentication security is tricky that needs to be solved in pattern authentication. For example, current ATMs only use PIN codes for identity verification. This single-mode cannot guarantee the security of identity verification. Compared with model authentication, biometric authentication has uniqueness, non-replicability, heritability, and immutability, which has attracted wide attention.

The current biometric authentication field mainly uses fingerprint recognition and iris recognition, and Deep Learning methods such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) have been widely used in these two fields.

2: Network situation awareness

In the process of network use, the uneven flow of data exposes the location of the network, and the weak links of the network are sensed in advance, which provides a basis for network adjustment. Network situation awareness is required. In order to achieve this process, the situational awareness model is required to have a strong knowledge base that can quickly detect and match the network situation. At the same time, the model needs to have the ability to extract features. In addition, it should be possible to implement reasoning and give reliable perceptual results. Therefore, deep learning models such as Multi-entity Bayesian Network (MEBN), Fuzzy Neural Network (FNN), and Random Forest have promising applications in this field.

3: Dangerous behavior monitoring

New technologies such as big data and cloud computing continue to emerge, hackers’ attack methods are also constantly evolving. In order to achieve network security with accurate methods, it is necessary to monitor dangerous behaviors and their types in time. Many AI application methods in this field combine deep feature extraction with multi-layer, integrated support vector machines, using distributed DBN to reduce the dimensionality of large-scale network traffic data sets to find abnormal behavior.

Artificial intelligence-based super Parameter-optimized network intrusion detection method uses artificial neural network technology to detect botnet attacks.

4: Abnormal flow identification

Any network has a specific carrying capacity. Within the normal threshold range, the network can play an important role in providing users with high-quality services. Hackers will deliberately inject a large amount of illegal data into the network structure, making network nodes and links unable to bear and causing accidents, unable to provide services to users, and even leading to severe problems such as information loss. How to detect high-risk behaviors in cyberspace in time through network traffic analysis, provide an important basis for network situation awareness, and take effective measures to improve network response capabilities and maintain overall network security.

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