"The corporate security department often lacks technical staff and is not enough to deal with a variety of threats."
The number of reports on cybercrime continues to increase. However, this does not include those who have not been reported and have not been discovered. In order to cope with the deteriorating crime situation, monitoring methods have been continuously improved and upgraded, and almost changed their face.
As the saying goes, “One thing is falling,†cybercrime is on the rise, and the methods of cracking down on cybercrime are constantly being upgraded. Without compromising accuracy and efficiency, letting computers automatically crack down on cybercrime can strengthen people's role in security operations.
The wave of automation is a technological advancement. The autonomous learning of intelligent software enables it to identify and handle emergencies, while security experts can handle more complex and important cases.
But security experts are not so good. The black market's freely available malware, botnets, and DDoS products provide an opportunity for organized criminal gangs. However, there are even greater challenges ahead: whether it is the identification and control of internal threats, or equipment strategy, management, or the increasingly interconnected nature of the Internet of Things, all of which increase the difficulty of the work of security experts. It makes the commercial security protection work in the real world or in the network complicated.
What makes this problem even more tricky is the shortage of technical talent: The company’s security department often lacks technical staff and is not enough to deal with a variety of threats. The unprecedented skill gap among applicants is that these agencies often fail to recruit suitable candidates.
Self-awareness and self-defense of the machine
The current security situation is very serious and security measures must evolve and upgrade. Criminals are more and more savvy in their criminal tactics. Therefore, security teams should also constantly improve their capabilities to protect the security of new hybrid networks and key asset data.
Traditional systems are well-suited to respond to past unconventional cyber attacks. However, in the digital era today, the traditional methods have very limited ability to deal with cyber attacks. The traditional system is a rigid system that is "yes" or "not". Based on a set of fixed control procedures, only network attacks that meet the characteristics of program identification can be intercepted.
DARPA put forward the idea of ​​automatic defense, trying to achieve this goal in the network security challenge competition, sent a generation of machines with algorithms that can realize the function of autonomous discovery, identification of network attacks and real-time patching of software vulnerabilities. The success of this challenge shows that with the development of machine self-awareness, the existing advantages of cybercriminals will no longer exist in the near future.
When a company faces multiple attacks at the same time, risk assessment can be used to reduce the risk. Essentially, each threat is comprehensively scored based on environmental factors and the priority of protection of the security agency.
Fundamentally, risk assessment allows security agencies to prioritize incidents that have a significant impact on business security. Whether discovered or not discovered, the number of cyberattacks is growing. Risk assessment will continue to provide guidance and guarantees for commercial security. However, such passive protection measures are certainly important, but if you really want to solve cybercrime the only way is to expand the scope of surveillance and resolve it before the other party commits an attack. This is exactly what the current security agencies are trying to overcome.
Application of machine learning
Security experts have added machine learning techniques to traditional protection systems. By analyzing the data, the attack pattern is identified and the machine language is used to modify the attack program. This is also a means by which many commercial companies conduct big data analysis. Amazon, for example, predicts consumer spending habits through machine learning using a specific algorithm.
Obviously this move is very effective, attracting major companies to follow suit, and they will all use similar equipment to increase profits. In the field of network security, the machine learning protection system uses anomaly detection. The system will preset a normal model. If the data entering the system is inconsistent with this model, it will be identified as abnormal data.
It is worth noting that this normal reference model is not static. The more data that is added, the clearer the system's definition of the normal model will be, and this constantly updated normal model will be used to proofread the system environment in real time to ensure its accuracy. This means that if an unrecognized threat enters the system, it will be identified because it is inconsistent with the normal model preset by the system.
In the digital age, all strategies are evolving. Deep learning enhances machine learning. This protection method can accurately identify the trigger mechanism, events and results with the help of the deep learning core engine database. Under the blessing of this technology, the computer is more accurate in the identification and classification of malicious files, whether it is a variant of a known program or an unknown malicious program.
Automatically monitor cyber attacks and block real-time protection for commercial security. In the field of security, deep learning is a breakthrough compared to traditional machine learning in the monitoring of new malicious programs on any device, platform or operating system.
Information security experts have struggled with this for years, and in order to better identify different cyber attack patterns, they used the latest technology to compete with them. In a recent attempt, they used narrow artificial intelligence to aid analysis and take action.
Artificial neurons transmit much faster than biological neurons, which determines that they can make decisions faster than humans. Artificial intelligence reduces the time wasted on false alarms. As the stay time is reduced, important business data is effectively protected. In the future, artificial intelligence will ease the burden on the security work team and allow them more time to deal with more complex tasks that the machine cannot handle.
Currently, the narrow version of artificial intelligence-artificial intelligence can only deal with a specific problem in a well-defined environment - it is still in the early stages of security operations. The limitation of this kind of artificial intelligence is that it cannot respond according to the environment. It can only respond to the preset ways. However, more advanced artificial intelligence that can be used in some specific situations has emerged, can identify network attack patterns, and take measures to automatically classify them.
The next year is a crucial year. Major companies and technology companies will deepen the integration of deep learning and machine learning. However, artificial intelligence still cannot enter millions of households because it still needs huge database, training mode and large data processing capabilities.
The hybrid approach of combining automation with machine learning controlled by people or people can not only ease the current status of the company's security talent shortage, but also provide better protection than pure labor or pure machines. Automation technology will gradually mature in the next few years. Many companies have used automation and rapid decision-making as one of the means to increase efficiency and expand the company. The next step should be to apply this technology to the information security industry.
The means of cyber attacks are endless, and the methods of identification, defense, and control are constantly maturing. Automatic control of cyber attacks may be earlier than most people think.
Via information-age
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