AI in cyber security

The use of Artificial Intelligence has helped IT professionals to predict and react to cyber attacks quicker than ever before. AI seems to have taken over everything from ordering food at a restaurant, speech recognition and task automation. One of the areas where AI has touched is cybersecurity. Experts argue that it has opened the doors for defenders and attackers to reach their goals.

Most cybersecurity solutions use signature-based or rule-based methodology that requires institutional knowledge and human intervention. These systems need regular updates that take up time and force analyst to only examine one part of the enterprise. AI goes further to augment the human element and make cybersecurity more productive.

AI is the science of training machines or systems to emulate human intelligence through continuous learning. While humans will remain fundamental, a system’s ability to learn more about the environment it will protect is critical. It helps the machines search for anomalies and handle tasks in user behaviour.

How AI has improved cybersecurity

A colossal mistake happened in April 2018 when Facebook had to notify 90 million users that their personal data may have been shared without their knowledge. In 2017, thousands of cameras were used to produce a DDoS attack that decreased a website’s function or took them down with unending requests. Renowned brands involved in the attack include Netflix, Amazon and Twitter.

Prevent cyberattack through machine learning

AI uses machine learning to enhance its intelligence. To improve cybersecurity, it fills the gap of lack of skills to prevent cyber attacks. Once it detects malicious software on the network, it speeds up the incident response. Some AI bots block access to websites completely. By preventing such actions, AI improves the security of an organisation or individual on the internet.

Analyse data more efficiently

AI needs data to analyse if it must work efficiently. Both streaming or stored data are valuable in the cyber environment. AI identifies the right data that gets the best result. It is capable of gaining a more comprehensive understanding of cyber threats and determining the best practices to mitigate threats.

Empowering through the combination of technical and human methods

Security companies are using machine learning in various industries. For instance, Artificial Intelligence helps to break down the difficulty of an automatic complex process for detecting attacks and reacting appropriately. The challenge is to deliver measurable results in processes such as anticipation, detection and analysis of attacks.

The automation feature of machine learning allows AI to harness the combined power of technical and human methods engaged in cyber defence. The tools and methodologies used to solve security issues will be more reassuring.

Improving security tools

The different processes that spot attacks and perform tasks against those attacks can be improved with predictive AI. For instance, an innovative area of data deception tech uses AI to spot activities that fit a specific pattern of attack. Attacks usually occur in an abnormal pattern and AI uses decoys to trap attackers.

Organisations use AI to improve cybersecurity and offer more robust protection against complex hacking attempts. The number of threat vectors is cumbersome for traditional systems to handle due to their sophistication.

AI improves incident monitoring, which is important to the speed of detection. The response is critical to limiting damages. It also sends out automated responses to attacks without human input. Humans will review or make adjustments but AI is more equipped to produce the best responses and results.

AI in cybersecurity: The Risk

While AI can improve cybersecurity, companies have to worry about the threat of hackers who’ll use it to launch sophisticated hacking attacks. It’s no wonder that cybersecurity firms are deploying AI in their services. Tech companies such as Google are adding machine learning to strengthen their cloud computing data centres.

Criminal cyber gangs, state-sponsored attackers and ideological hackers use AI to launch warfare. Experts at Openhost hosting believe these criminals use AI to develop mutating malware to avoid detection and pitch the AI against itself.

More AI solutions more opportunities for hackers

Speed is the edge of the cyber defence. Al is allowing businesses to detect complex threats that weren’t possible in the past. Conversely, cybercriminals are adopting the same technology. It’s fighting fire with fire.

Attacks are going to be more personalised with a higher likelihood of success. Even with AI, detecting malicious code and removing it from your network will be harder. For instance, hackers are using AI to accelerate polymorphic malware that causes code to constantly change, making it undetectable.

A few ways hackers use AI include

  • Bypassing facial security
  • Deceiving autonomous vehicles to misinterpret speed limit and stop signals
  • Fooling sentiment analysis of hotels, movie reviews and more
  • Bypassing spam filters
  • Fake voice commands
  • Misclassifying system based medical predictions
  • Getting past anomaly detection engines

Research demonstrated that small disturbances of an image lead to misclassification. Instead of a bicycle, the system recognises a car. First, the attacker calculates a dependency matrix showing adjustment of the output for every input. Next, they change the most influential pixels of the modified image to misclassify the result using optimised brute force.

Greater results with fewer efforts

We’ve seen AI-based malware such as Trickbot plague organisations recently. Trickbot is a malicious code that enters a network like Homer’s Trojan Horse to infect systems automatically. It’s difficult to detect because the malware authors make changes on the fly.

According to Information Age, AI could be configured to learn specific tools and defence mechanism that it runs up against to improve its ability to breach them in future. Hackers could create special viruses that host this AI and generate malware to bypass advanced security in place.

Cybersecurity vendors must consider the vulnerabilities of AI when designing detection and classification system. A few actionable steps to take in the meantime include

  • Rather than engaging human analysts in low-level repetitive tasks, engage them in critical decision-making
  • Watch out for an unusually high number of false negatives and false positives
  • Upgrade your software regularly
  • Initiate an active risk programme. Ideally, it must include the development and verification of analytics.

Conclusion

The application of AI to finance, environmental tracking and retail also expand the surface of attack. While the benefits far outweigh potential downsides, a robust defence strategy is required to protect an organisation’s data from sophisticated AI-based hacking attempts.

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