Enhance Penetration Testing with Machine Learning

Introduction

The vast improvement of the digital transformation era has enabled many industries with high-performing processes and real-time information. When the technology improved, so do the potential threats and risks within. Securing information and assets is as principal as improving the technology, and is also part of the digital strategy enhancement. The dangers of cyber threats have overshadowed the IT environment and continue to operate covertly, causing various damages for businesses, from revenue loss to reputational damages.

Whether it's a small business or a big corporation, the unavoidable threats of cyberattacks are ever-present. The improvement of cyber security strategy should be a priority to enable businesses in reducing the impact of cyber threats, predict potential risks, and use insightful data to overcome future challenges in cyber security.

The Cybersecurity Challenge

With the high volume of internet usage, there's no secret that everything is vulnerable to cyberattacks. From security passcode, and financial reports, to personal data breaches. Cyber awareness is near to sufficient when it comes to securing assets and information in the IT environment. According to BeyondTrust survey, the most anticipated cybersecurity threats in 2021 are ransomware (67%), phishing/social engineering (60%), disinformation (57%), and remote worker or contractor vulnerabilities (65%) to become the most potential cyber threats, These show that the cyber security strategy also needed to support by a high-awareness of cyber security within the employees and resources. The implementation of high-performing cybersecurity becomes lacking when the employees working in the IT environment are not well-aware of the potential cyber threats and cyberattacks that could emerge.

How to Overcome Challenges: Machine Learning

Machine learning can improve cyber security processes to become more simple, more proactive, and more effective. In addition, machine learning implementation can be a cost-effective solution for cyber security strategy. Typically, cyberattacks occurred when companies fail to recognize vulnerabilities in their system, leading to the inability of predicting potential attacks. Another reason is based on the aforementioned issue, where employees and resources are lacking of awareness in threats and cyberattacks. When it comes to minimizing risks, utilizing machine learning can be an effective tool to minimize human error that could trigger vulnerabilities, Utilizing machine learning for cybersecurity enhancement can improve the sustainability of businesses' cybersecurity.

Machine learning also offers automation capacity, which will automate processes and procedures in the entire cybersecurity cycle. This automation helps businesses in analyzing various data and summarizing them into great insights that could be used for predicting potential risks and overcoming vulnerabilities.

Penetration Testing with Integrated Machine Learning

The penetration testing process after being integrated with machine learning enables cognitive automation that could reduce human dependency upon its entire process. By integrating machine learning into penetration testing, businesses can transform the endpoint by adding accuracy and contextual learning.

Here are the ways in how machine learning integration can enhance penetration testing processes:

1. Reconnaissance of Footprinting

It is one of the first phases, and the main objective is to comprehend the target. To increase the likelihood of breaking into the system, several passive techniques are used to acquire all information that is readily accessible to the public.

Traditionally, testers have extracted publicly accessible data about the target using manual approaches. adding AI and ML at this stage, the process is not only automated, but it also produces superior outcomes and saves a significant amount of time and money.

2. Scanning

Typically, a large amount of information is gathered during the reconnaissance stage. Additionally, all of this data must be timely examined to find any potential risks.

This operation can be impossible to complete manually, so it's crucial to automate the scanning procedure. Without ML and AI, automating it, however, would merely result in a time reduction with minimal improvements to its effectiveness and dependability.

The ability to use previous data to infer logical conclusions is the basis for machine learning's effectiveness.

3. Threat Modeling

By gathering, compiling, and analyzing all the information available that has an impact on the application security system, a threat model is created in this case in the form of a structured representation.

This procedure entails producing a lot of data and interpreting it in light of how it affects sensitive and mission-critical components of the application. This task can be quite difficult to complete manually, so automating the procedure is essential. However, by automating it with machine learning and supporting technologies, cognitive talents can also be incorporated into the procedure, enhancing its dependability and efficiency.

Organizations may discover and anticipate potentially suspicious activity while correlating it to produce reliable alerts proactively and accurately by integrating machine learning into the workflow.

4. Vulnerability Analysis

Software flaws draw hackers from all across the internet, just way a few drips of fruit juice attract colonies of ants. No matter how little the vulnerability was, sometimes even organizations' security professionals are taken aback by how quickly it was found. It occurs primarily because modern cybercriminals increasingly employ machine learning and supporting technology to quickly and effectively find vulnerability

In the vulnerability analysis stage, employing machine learning in this context is most critical.

Here, all the information that has been filtered out is used to find potential vulnerabilities and separate them based on how serious they are. Enterprises can use context-based vulnerability risk assessment to organize their optimum remediation efforts by embracing machine learning-backed analysis.

5. Exploitation

Attack preparation begins at this phase. Here, QE engineers commandeer a number of network gadgets and launch a number of attacks against the system.

A system may contain numerous weaknesses, making it difficult to manually attack it from different angles. With machine learning these attacks may be carried out quickly by utilizing web application exploits like SQL injection, cross-site scripting, and backdoors to take advantage of the weaknesses in the target software.

In order to determine the extent of the system's vulnerability, QA testers take advantage of these flaws by elevating their privileges, eavesdropping on traffic, stealing data, etc.

To Conclude

Machine learning implementation in penetration testing can improve efficiency in the overall processes. Aside from reducing human errors, and improving risk analysis, machine learning can optimize vulnerability elimination and elevate cybersecurity strategy.

Reference:

BeyondTrust. (2021). 2021 Survey of Evolving Cyber Threats in the Public Sector: Key Takeaways.

https://qualitestgroup.com/insights/blog/how-to-leverage-machine-learning-for-improved-penetration-testing/ Cited in September 29, 2022