The Impact of AI and Machine Learning on Cybersecurity | Everything You Need to Know

• In this article you will learn:
• 1.Introduction to an AI and Machine Learning on a Cybersecurity.
• 2.AI and Cyber Security.
• 3.Main Challenges Cybersecurity Faces Today.
• 4.How AI Improves a Cybersecurity.
• 5.Drawbacks and Limitations of Using AI for a Cybersecurity.
• 6.Conclusion.

Introduction to an AI and Machine Learning on a Cybersecurity:

Experts are agree that a Artificial Intelligence (AI) and Machine Learning (ML) have both the negative and positive effects on a cyber security. AI algorithms use a training data to learn how to respond to various situations. As they progress, they learn by a copying and adding additional information. This article reviews a positive and negative effects of AI on cyber security.

AI and Cyber Security:

• Cyber security is one of many uses of an artificial intelligence. A report by a Norton showed that the global cost of a typical data breach recovery is $3.86 million.
• The report also states that a companies need an average of 196 days to recover from the any data breach. For this reason, organizations should invest a more in AI to avoid wastage of a time and financial losses.
• AI machine learning and a threat intelligence can recognize a patterns in data to enable security systems to learn from a past experience. In addition AI and machine learning enable the companies to reduce an incident response times and adhere to a security best practises.

Main Challenges Cybersecurity Faces Today:

Despite advances ina cyber security, attacks are becoming more and more dangerous. The main challenges of a cyber security include:

Geographically distant IT systems— a Geographical distance makes a manual tracking of incidents more complex . a Cyber security experts need to bridge a gap in infrastructure to successfully monitor the incidents across sectors.

Manual Threat Hunting – can be a costly and time-consuming, resulting in a more going unnoticed.

Reactive nature of cyber security – Companies can solve a problems only when they have already been happened. Predicting threats before they occur is the major challenge for a security experts.

Hackers often hide and change their IP addresses— Hackers use a different programs such as Virtual Private Networks (VPNs), proxy servers, a Tor browser and more. These programs are help hackers to remain for anonymous and undetected.

How AI Improves a Cybersecurity:

Threat hunting:

• Traditional security techniques use a signatures or indicators of the compromise to identify threats. This technique may work well for a threats already encountered, but they are not be effective for threats that have not yet discovered.
• The signature based on technology can detect about 90% of a threats. Replacing traditional techniques with an AI can increase the detection rate to 95%, but will get an explosion of false positives.
• The best solution would be to combine the both traditional methods and AI. This can result in 100% detection rate and reduce a false positives.
• Companies can also use the AI to enhance a threat hunting process by integrating behavioral analysis. For example, can leverage AI models to develop profiles of the each application within an organization’s network by a processing high amounts of an endpoint data.

Vulnerability management:

• There were a 20,362 new vulnerabilities reported in a 2019 a 17.8% increase over 2018. An Organisations are struggling to be prioritise and manage the massive amount of a new vulnerabilities they encounter on daily basis.
• Traditional vulnerability management methods are wait for hackers to exploit a high-risk vulnerabilities before deactivating them.
• While traditional vulnerability databases are the critical to managing and containing known vulnerabilities, AI and machine learning techniques such as a User and Event Behavioural Analytics (UEBA) can analyze a baseline behavior of user accounts, endpoints, servers and identify anomalous behavior.
• Can identify what may be indicate a zero-day unknown attack. This can help keep to organisations secure even before vulnerabilities are officially reported and also patched.

Data centers:

AI can optimise and monitor more essential data center processes like backup power, cooling filters, power consumption, internal temperature and bandwidth usage. AI’s computational powers and a continuous monitoring capabilities provide the insight into what values ​​will improve an effectiveness and security of a hardware and infrastructure.

Network security:

There are two time-intensive aspects of a traditional network security, creating security policies and understanding the organization’s network topography:

• Policies—Security policies are identify which network connections are be legitimate and which should monitor for a malicious behavior. Can use these policies to effectively implement zero-trust model. The real challenge lies in formulating and maintaining policies in view of large number of networks.
• Topography—Most organizations do not have a precise naming conventions for the applications and workloads. As a result, security teams spend a lot of time determining what type of a workload a given application is be concerned with.
• Companies can leverage AI to improve the network security by learning a network traffic patterns and recommending functional set of workloads and also security policy.

Drawbacks and Limitations of Using AI for a Cybersecurity:

Resources— Companies need to invest the lot of time and money into resources are computing power, memory, and data to build and maintain AI systems.

Data set- AI model is trained with a learning data set. Security teams need to get their hands on many various data sets of malicious code, malware code, and anomalies. Some companies are do not have a resources and time to obtain all these accurate data sets.

Hackers- also use a AI-attackers’ tests and improve their malware to made it resistant to AI-based security tools. Hackers are learn from existing AI tools to develop a more advanced attacks and attack traditional security systems or an even AI-boosted systems.

Neural Fuzzing – Fuzzing is a process of testing large amounts of a random input data within software to identify its weaknesses. Neural Fuzzing leverages AI to quickly test a large amounts of random input. However, a fuzzing has a creative side as a well. Hackers can learn about weaknesses of a target system by gathering information with the power of a neural networks. A Microsoft developed a method for implementing this approach to improve software, resulting in a more secure code that is harder to use.

Conclusion:

Artificial intelligence and machine learning can improve the security, as well as make it simpler for a cybercriminals to systems without any human intervention. This can cause a lot of loss to the any company. It is highly recommended to get a some sort of protection against cybercriminals if need to minimize losses and stay in a business.