Video surveillance networking applications generally feature large scale, wide distribution, complex structure, and strict monitoring requirements. It can be said that there is currently a vast video surveillance network in China, especially large-scale networks represented by the “Sharp Eyes Project.” While the market continues to heat up rapidly, the underlying security risks cannot be ignored.
In order to ensure that video surveillance networking platforms are not damaged and that monitoring data is not leaked or tampered with, such systems must also have corresponding overall security protection capabilities during construction. As data security receives widespread attention, continuously strengthening cybersecurity protection through strong technical capabilities is key to development.
At present, the industry mainly protects network security from different system layers, typically including endpoint, transmission, cloud, and chip.
“Endpoint” refers to terminal devices. Through systems, firewalls, and other measures on the devices, key user information leakage is prevented, usually mainly through software.
“Transmission” refers to the data transmission channel. Data is encrypted during transmission, so even if leakage occurs, it cannot be used without decryption. This is usually implemented by built-in encryption modules in communication modules or through encryption algorithms.
“Cloud” refers to cloud storage. Since data needs to be uploaded to the cloud, the demand for data security is more prominent. Most cloud service providers focus on protecting data on the cloud side to prevent leakage and loss.
“Chip” refers to encryption chips, especially the underlying encryption IP of chips. This area is still in an early development stage domestically.
From an application perspective, the main security factors that need attention in network video surveillance systems include operating system security, user information security, application software security, and network security.
Currently, mainstream network security protection technologies in the industry include SSL security authentication technology, MPLS VPN, firewall systems, etc. SSL (Secure Sockets Layer) is a secure network communication protocol that uses a combination of public key and private key technologies.
It provides data encryption, server authentication, message integrity, and optional client authentication for TCP/IP connections. It is mainly used to improve data security between applications by encrypting transmitted data and ensuring that the data is not altered during transmission, thus ensuring data integrity.
MPLS VPN adds a fixed-length label to each IP packet and forwards packets based on the label value. MPLS is essentially a tunneling technology, making it easy and efficient to establish VPN tunnels.
MPLS VPN uses route isolation, address isolation, and information hiding to provide protection against attacks and label spoofing.
Firewall products generally ensure system security through packet filtering technology, proxy service technology, and network address translation technology.
In addition to the above conventional security technologies, in recent years, with the implementation of artificial intelligence technologies, AI applications in cybersecurity mainly include machine learning, natural language processing, and contextual awareness computing. Security applications mainly target application security, endpoint security, cloud security, and network security.
Main applications include data loss prevention, unified threat management, encryption, identity and access management, risk and compliance management, antivirus/malware protection, intrusion detection/prevention systems, distributed denial-of-service mitigation, security information and event management, threat intelligence, and fraud detection.
In fact, using artificial intelligence technology to address security challenges in the security industry has become a trend, such as identity authentication and anomaly detection. In recent years, unsupervised learning has increasingly been used for anomaly detection.
Data dimensionality reduction can reduce the dimensionality of data vectors, remove redundant information, improve recognition accuracy, and further discover the intrinsic features of data. Association rule learning can identify potential correlations between behavior sets and abnormal states by learning from source data. Clustering algorithms based on probability distributions and frequency can ultimately distinguish between normal and abnormal behaviors.
These machine learning algorithms have made anomaly detection applications in cybersecurity more mature.
In addition, based on the characteristics of decentralization, data immutability, and permanent traceability, blockchain can disruptively solve some key security issues faced by current security product development and bring broader possibilities to classic security application scenarios such as smart homes, intelligent transportation, and smart cities.
Video surveillance security belongs to the field of Internet of Things (IoT) security. IoT security currently generally faces issues such as non-unified communication protocols, lack of mandatory security development standards, and limited computing resources. Therefore, how to solve access security is an important technical direction, and industry competition will focus on processing speed and quality.
Furthermore, with the increasing maturity of AI technology applications, how to mine unknown security risks from video data and form the ability to perceive and judge unknown risks will become a key focus of the industry.