Artificial Intelligence for Smarter Cybersecurity

November 13, 2020

Organizations continue to embrace the Internet of Things (IoT), the cloud, and mobile technology. This has influenced considerable changes in the threat landscape and created more vulnerability points.

Cybercriminals are leveraging these new vulnerability points to develop and launch sophisticated, high-volume, multi-dimensional attacks. Such attacks mean that data is at risk, and organizations must analyze potentially malicious files.

Using artificial intelligence software, organizations can process large volumes of threat data and adequately prevent and respond to breaches and hacks. This article will discuss how machine learning and artificial intelligence improve cybersecurity in four important areas: network security, data centers, threat hunting, and vulnerability management.

Network Security

Network security mainly focuses on the understanding of network topography and creating a security policy of an organization. Securities policies help distinguish legitimate network connections from illegitimate ones.

But drafting and maintaining policies for multiple networks may be a challenge. Also, some organizations don’t know how to follow security policies properly. Others may not have a precise naming convention for workloads and applications.

This means that network security teams have to attach significantly different workloads to given applications. These activities are time-consuming, and the use of artificial intelligence technology can come in handy.

AI can expedite these processes by learning and observing traffic patterns and suggesting security policies. This way, organizations save on time, resources, and effort. The organization can allocate these two other areas of technological advancement or development.

Data Centers

Cyber attackers target data centers to get critical data. Cybersecurity has improved over the years with advancements in technology. Being aware of this, cybercriminals regularly develop more advanced malware strains to ensure successful cyber-attacks on organizational networks.

Organizations can deploy artificial intelligence technology in their data centers to enhance data security. To ensure data security, AI learns normal network behavior. It then can detect cyber threats by monitoring for any variations from regular network behavior.

Particularly, supervised Machine Learning is crucial in identifying new malware posing a threat to data centers. Malware are constantly developed to bypass traditional systems, relying on signature recognition. The good news is that modern machine learning systems that use Recurrent Neural Networks (RRN) help detect this malware.

Machine learning and RNN use linguistic analysis to provide a more accurate result. Malware-generated domains typically have strange consonants/vowel ratios, which helps RNNs to identify and isolate the issue(s).

The application of AI in a data center can help identify security loopholes and detect malware in data center systems. AI-based cybersecurity can analyze or screen outgoing and incoming data security threats.

Threat Hunting

The asymmetric nature of cybersecurity is an inherent problem that seems to encourage malicious acts of attackers. With this, cyber-attackers only need to be successful once by exploiting just a single weakness while the defenders must be successful every time. Preventing threats requires more than just ensuring data security. It also requires combing for threats on an entire information technology stack.

Here, AI is essential to identify abnormalities, outliers, and patterns in all data with no need to apply fixed rules. Human analysts depend on this output to investigate and act. This is often referred to as threat hunting in security language. It involves narrowing down threats by collaborating security analytics and machine intelligence, and advanced human cognition.

Artificial intelligence uses artificial neural network-based techniques for cyber-threat detection. Artificial Neural Network (ANN) identify patterns, learn system characteristics, and compare recent user activity with normal behavior. This way, we can recognize malicious events with more consistency. Ideally, system administrators leverage this technology to protect their organization against cyber-threats.

Vulnerability Management

Every year, there are many reported vulnerabilities to cybersecurity. It’s extremely difficult to manage these unique vulnerabilities with traditional technology or human resources. Modern approaches to vulnerability management are data-driven, and their corrective actions target specific vulnerabilities depending on their exploitability, environment, and exploit pulse.

They involve the automation of cybersecurity with artificial intelligence to categorize, list, and rate cyber-threats/risks. Once AI systems identify vulnerable points, they provide immediate strategic solutions which may include blocking cyberattacks.

In most cases, systems that use machine learning and artificial intelligence don’t wait for online attackers to exploit existing vulnerabilities. Rather, these systems find potential vulnerabilities in information systems.

They rely on multiple factors such as hackers’ patterns, the reputation of the hacker, and the hacker’s conversations on the dark web. AI-ML systems analyze these factors and use the insights to assess how and when threats might attack vulnerable targets.

Negative Effects of AI on Cybersecurity

Cybersecurity experts have embraced AI because of its significance in the industry. Yet, aside from being a solution, AI can pose a considerable threat to businesses.

Artificial intelligence relies on supervised learning. With this, algorithms label data sets depending on their nature. This could be clean data, malware, or some other tag. If cybercriminals get access to the security firm, they can alter those labels to suit their interests or needs.

Besides, advanced hacking campaigns can use machine learning technology to manipulate routine tasks that rely on that artificial intelligence technology.

AI can identify irregularities and abnormalities in a network, they deduce patterns from data, and analyze user behaviors.

With this information, identifying cyber vulnerabilities becomes easier. However, cyber attackers can use the same information to create malicious cyber programs that imitate legitimate AI-based algorithms, so the information might be susceptible to cyber crime.

The Future of AI and Cybersecurity

The future application of artificial intelligence to improve cybersecurity looks promising. Security teams expect AI to enhance and respond better to cyber-attacks. Human analysts are designing better defense strategies, and AI will enforce the best methods to ensure cybersecurity. Response time to threats will improve, and human experts will have more free time to handle more complicated investigations.

Artificial intelligence’s natural language can predict and understand the origin of cyberattacks. We’ll see this principle put into effect by scanning data across the internet.

Next-generation firewalls might have a powerful in-built machine learning technology. This will help find patterns in network packets and blocking them automatically if flagged as a threat.

Conclusion

Artificial intelligence promotes and enhances cybersecurity. Lately, cybersecurity teams have increased their use of AI to augment their efforts of ensuring information security. Cyber-attacks are very dynamic, and AI is imperative in providing much-needed analysis and threat identification.

Attackers also take advantage of AI to find loopholes in cybersecurity and plan their attacks. Using AI in cybersecurity is expected to advance in the future, thus ensuring that systems can respond better to attacks.

Sources & Additional Resources


Peer Review Contributions by: Lalithnarayan C


About the author

Eric Kahuha

Eric is a data scientist interested in using scientific methods, algorithms, and processes to extract insights from both structural and unstructured data. Enjoys converting raw data into meaningful information and contributing to data science topical issues.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.