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How Artificial Intelligence is Changing Cyber Security Landscape and Preventing Cyber Attacks

The world is changing at a rapid pace and the future is bright for organizations that embrace technology. Digitization means that everything is moving quickly in various sectors such as entertainment, new products, and business. The customer gets what they want instantly because the service provider has everything he or she needs to deliver products or services. 

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While there are lots of benefits and conveniences in this digital era, there are also a couple of negatives. One of the most important and destructive threats linked to it is the risk of your private information. The last decade has seen a host of identity theft cases, data breaches, and loss of money. Cyberattacks tend to be pervasive and affect individuals, government bodies, and businesses. Today, cybercriminals can access their targets at any location in the world at any time. 

Therefore, the need for cybersecurity has never been essential than now. A cyberattack is a cybercriminals’ attempt to access, damage, or alter a target’s network or computer without authorization. It is intentional, systematic, and calculated to affect computer systems and disrupt operations and organizations that rely on them.

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Artificial Intelligence can negatively affect cybersecurity

There’s a high probability that attackers can weaponize artificial intelligence and use it to expand their attacks. One of the greatest concerns is that hackers can automate cyberattacks using artificial intelligence on a huge scale. Our enemies rely on human resources to organize their attacks. Cybersecurity and cybercrime will eventually change when they learn to use machine learning and AI to do their work. IT students can save time by using AI to connect with Best Essay Tips writers, and protect their projects.

Another important issue is we can use machine learning and artificial intelligence to complement human resource shortage and save cybersecurity costs. And our enemies can use it similarly too. The resources and finances needed to coordinate and launch such attacks will go down quickly – a lower investment for cyber attackers and a big threat to cybersecurity.

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Also, AI advancement can give birth to other types of cyber threats. Artificial Intelligence can take advantage of the vulnerability of the system faster and better than a human being. Attackers can use AI to disguise attacks easily that the victim might never know that their device or network has been affected.

Artificial intelligence has three implications to the threat landscape. And they include the threats and attacks of today’s augmentation, the creation of new threats, and the variation of existing threats.

Uses of AI in Cybersecurity

In recent years, there has been a great amount of interest in exploring the use of AI to improve cybersecurity practices. The majority of machine learning cases rely on supervised machine learning strategies where best essay writing services analysts use existing data to train the machine learning app. Unsupervised machine learning strategies are in the experimentation phase. With these facts in mind, here are a few examples where Artificial Intelligence is being used to improve cybersecurity:

  1. Detecting intrusion

Machine learning helps in detecting and defending intrusions. And it goes beyond simple rules that rely on logic. Once a specific set of behaviors is learned by the machine, based on factors such as frequency of queries, the number of access attempts, and the amount of data in each query, outliers get flagged automatically as suspicious without any human intervention.

  1. Detecting malware

In most cases, new malware is created by cyber attackers. Once they have achieved their goals, the process of creating subsequent variants becomes automatic. Integrating machine learning techniques with signature-based systems can help in identifying future variants and versions of malware to prevent the spread.

  1. Discovery of areas of vulnerability

This is a new application area where human resources or developers scan huge amounts of code and automate the process of identifying vulnerabilities using machine learning before cyber attackers do.

  1. Detecting fraud

Fraudulent activities and transactions can be noticed and prevented by identifying deviations and detecting patterns from the expected line of behavior. Anomaly detection is one of the best machine learning applications that sift through huge amounts of event logs. As you’ve probably noted, the use cases are not new because they are things that IT experts have been doing for years. The only difference is, artificial intelligence is being used in these cases to make them secure and robust. In this way, enterprises can reduce the time it takes to identify and respond to threats by extending AI approaches.

  1. Enhancing threat intelligence

Combining machine learning and threat intelligence techniques has helped in improving detection rates at professional writing services and reducing the number of threats.

  1. Password protection and authentication

When it comes to security, passwords have always been a fragile control. And in most cases, they are the only barrier between our accounts and cybercriminals. Let’s face it. Most people are lazy with their passwords. And this results in the usage of one password across several accounts, using one password for ages and saving them as draft messages on our technological devices. 

Biometric authentication has been a good alternative to passwords. However, it is not convenient as hackers can bypass it quickly. For instance, a face recognition system can make it difficult for you to access your device or account especially when you change your hairstyle or wear glasses. Attackers can bypass it by using your images from Instagram or Facebook.

Developers are enhancing biometric authentication using AI to eliminate imperfections and make the system reliable. A good example is Apple’s face recognition technology that’s available on iPhone X devices. The technology processes the facial features of users using neural engines and infra-red sensors. 

The AI software creates a complex model of the user’s face by identifying patterns and correlations. Using this technology, Apple claims that there’s no chance of confusing the device with a new face. The software architecture works in different conditions and compensates for changes like growing facial hair, a new hairstyle, or wearing glasses.

Conclusion

The evolution of technology has made it easier for attackers to enhance their techniques, methods, and tools to exploit organizations and individuals. While artificial intelligence is useful, it is also a double-edged sword. Choosing your technology carefully will help you avoid a crisis.

Author Bio:

Ashley Simmons is a professional journalist and editor at essay writing service uk and best essay writing service uk. She has been working in a newspaper in Salt Lake City for 4 years. She is also a content writing expert in such topics as psychology, modern education, business and marketing innovations. She is a master in her craft.

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