• September 29, 2022
• By admin

Adoption of AI-backed Technologies by Hackers Targeting Healthcare

What Is Machine Learning and How Does It Apply to Cybersecurity?

Machine learning (ML) is AI’s brain—a type of algorithm that allows computers to analyse data, learn from previous experiences, and make decisions in a manner similar to human behaviour.

In cybersecurity, machine learning algorithms can detect and analyse security incidents automatically. Some can even respond to threats automatically. Machine learning is already used in many modern security tools, such as threat intelligence.

There are numerous machine learning algorithms, but the majority of them perform one of the following functions:

Regression—

detects correlations between different datasets and explains how they are related Regression can be used to predict operating system system calls and then identify anomalies by comparing the prediction to an actual call.

Clustering—

identifies similarities between datasets and groups them based on shared characteristics Clustering operates on new data without regard for previous examples.

Classification—

Classification algorithms learn from prior observations and attempt to apply what they have learned to new, previously unseen data. Classification is the process of taking artefacts and categorising them under one of several labels. Classify a binary file as legitimate software, adware, ransomware, or spyware, for example.

AI’s Impact on Cybersecurity:

While artificial intelligence can improve security, it can also provide cybercriminals with unrestricted access to systems. The following list summarises the good news about AI’s impact on cybersecurity.

Management of vulnerabilities:

Organizations are struggling to manage and prioritise the large number of new vulnerabilities that they discover on a daily basis. Conventional vulnerability management techniques only respond to incidents that have already been exploited by hackers.

AI and machine learning techniques can help vulnerability databases improve their vulnerability management capabilities. Furthermore, when powered by AI, tools such as user and event behaviour analytics (UEBA) can analyse user behaviour on servers and endpoints and detect anomalies that may indicate an unknown attack. This can help organisations protect themselves even before vulnerabilities are officially reported and patched.

Threat detection:

Traditional security tools identify threats by using signatures or attack indicators. This technique is effective for detecting previously discovered threats. Signature-based tools, on the other hand, cannot detect threats that have not yet been discovered. In fact, they can only detect about 90% of threats.

Traditional detection techniques can be improved by AI up to 95 percent. The issue is that multiple false positives can occur. A combination of AI and traditional methods would be ideal. This fusion of the traditional and the innovative can increase detection rates by up to 100 percent, reducing false positives.

By incorporating behaviour analysis, AI can also improve threat hunting. For example, by analysing data, you can create profiles for each application on your organization’s network.

Network safety:

Traditional network security techniques are divided into two categories: creating security policies and understanding the network environment. Here are some things to think about:

• Policies—

Security policies can aid in the differentiation of legitimate and malicious network connections. Policies can also be used to impose a zero-trust model. Creating and maintaining policies for a large number of networks, on the other hand, can be difficult.

• Environment—

Most organisations lack precise application and workload naming conventions. As a result, security teams must spend a significant amount of time determining which workloads belong to which application.

AI can improve network security by learning network traffic patterns and recommending security policies as well as functional workload grouping.

Data storage facilities:

Artificial intelligence can monitor and optimise critical data centre processes such as power consumption, backup power, internal temperatures, bandwidth usage, and cooling filters. AI reveals which values can improve the security and efficiency of data centre infrastructure.

AI can help you save money on maintenance. AI can generate alerts that notify you when hardware failures occur. AI-based alerts allow you to repair your equipment before it sustains further damage. After implementing AI technology in their data centres in 2016, Google reported a 15% reduction in power consumption and a 40% reduction in cooling costs.

Real-World AI Applications in Cybersecurity:

Machine learning can scan large amounts of data quickly and analyse it statistically. Modern businesses generate massive amounts of data, so it’s no surprise that technology is such a useful tool.

• Screening for security:

Immigration officers and customs officers can detect people who are lying about their intentions through security screening. However, the screening procedure is prone to errors. Furthermore, human-based screening can lead to errors because people get tired and easily distracted.

The US Department of Homeland Security has created a system called AVATAR that analyses people’s body gestures and facial expressions. AVATAR uses AI and Big Data to detect subtle variations in facial expressions and body gestures that may indicate suspicion.

A screen with a virtual face that asks questions is part of the system. It tracks changes in their responses as well as differences in voice tone. The collected data is compared to elements that suggest that someone is lying. Passengers who appear suspicious are flagged for further investigation.

• Crime prevention and security:

The New York Police Department has been using the Computer Statistics (CompStat) AI system since 1995. CompStat is an early form of AI that incorporates organisational management and philosophy but is dependent on various software tools. The system was the first tool used for “predictive policing,” and since then, many police stations across the United States have used CompStat to investigate crimes.

AI-based crime analysis tools, such as Armorway, based in California, use AI and game theory to predict terrorist threats. Armorway is also used by the Coast Guard for port security in Los Angeles, Boston, and New York.

• Investigate mobile endpoints:

Google is analysing mobile endpoint threats with AI. This analysis can be used by organisations to protect the growing number of personal mobile devices.

Zimperium and MobileIron have announced a partnership to assist organisations in adopting mobile anti-malware solutions that incorporate artificial intelligence. The combination of Zimperium’s AI-based threat detection and MobileIron’s compliance and security engine can address issues such as network, device, and application threats.

Skycure, Lookout, and Wandera are some other vendors that provide mobile security solutions. To detect potential threats, each vendor employs its own AI algorithm.

• AI-assisted threat detection:

ED&F Man Holdings, a commodities trader, was involved in a security incident several years ago. According to an independent assessment, the company’s cybersecurity processes and tools needed to be improved.

Cognito, Vectra’s AI-based threat detection and response platform, was chosen. Cognito collects, stores, and enriches network metadata with unique security insights. It detects and prioritises attacks in real time using this metadata and machine learning techniques.

Cognito assisted ED&F Man Holdings in detecting and blocking multiple man-in-the-middle attacks, as well as putting an end to an Asian cryptomining scheme. Cognito also discovered command-and-control malware that had been hiding for several years.

• Sophisticated cyber-attack detection:

The Energy Saving Trust is an organisation that aims to cut carbon emissions in the United Kingdom by 80% by 2050. The company was looking for a cutting-edge cyber security technology to supplement its overall cyber defence strategy. This includes protecting the company’s critical assets from sophisticated cyber-attacks, such as intellectual property and sensitive client data.

Following careful consideration, the company decided to concentrate on Darktrace’s Enterprise Immune System. Machine learning technology underpins Darktrace’s platform. To learn specific patterns, the platform models the behaviours of every device, user, and network. Darktrace automatically detects unusual behaviour and notifies the company in real time.

Energy Saving Trust was able to detect numerous anomalous activities as soon as they occurred and alert the security team to conduct additional investigations, all while mitigating any risk posed before significant damage was done.

• Threat Response Time Reduction:

A global bank was subjected to sophisticated cyber threats and sophisticated attacks. The bank’s threat detection and response needed to be improved. The existing solution was incapable of detecting and mitigating new generations of threats.

Paladon’s AI-based Managed Detection and Response Service (MDR) was used by the bank’s security team. The threat hunting service provided by Paladon is based on data science and machine learning capabilities.

The bank’s advanced attack detection and response capabilities have been improved. Data exfiltration, advanced targeted attacks, ransomware, malware, zero-day attacks, social engineering, and encrypted attacks are all examples of this.

AI’s Drawbacks and Limitations in Cybersecurity:

AI technology has some limitations that keep it from becoming a common security tool.

• Resources—

Organizations require a large amount of resources, such as data, memory, and computing power.

• Sets of data—

To train the AI system, security firms must use a variety of data sets containing anomalies and malware codes. Accurate data sets can necessitate a significant investment of resources and time, which some businesses cannot afford.

• AI is also used by hackers—

to enhance and improve their malware AI-based malware can be extremely dangerous because it can learn from existing AI tools to develop more advanced attacks.

• Fuzzing in the brain—

is used to detect software vulnerabilities by putting large amounts of random input data through their paces. A threat actor can use neural fuzzing in conjunction with neural networks to gather information about a target software or system and identify its flaws.

Conclusion:

As more technology is integrated into our daily lives, the impact of AI on our lives will grow. Some experts believe that AI will have a negative impact on technology, while others believe that AI will significantly improve our lives. The main advantages for cybersecurity are faster threat analysis and mitigation. Concerns centre on hackers’ ability to launch more sophisticated cyber and technology-based attacks.