How Machine Learning is Used in Cybersecurity
How Machine Learning is Used in Cybersecurity. Today, the rise of technology as well as the use of the internet has made it imperative for Internet service providers uphold individuals, companies, and governments’ security and safety on the internet. Issues such as theft, information disclosure, hacking, and damage to software and hardware have become quite common. Therefore, scientists and engineers are constantly striving to develop new and modified ways to improve the security of computer users.
It is on this note that machine learning has taken centre stage. Primarily, machine learning is used in virtually all online scenarios. Whether you’re shopping online, sending or receiving money via bank transfers, using social media, or playing games, machine learning is deeply at work to keep you safe and give you an efficient and seamless experience.
In this article, we will explain the crux of machine learning and cyber security. Also, how machine learning benefits cyber security, and various other concepts within these fields.
Shall we start with How Machine Learning is Used in Cybersecurity?
What is Machine Learning?
First of all, Machine Learning (ML) is a subdivision of Artificial Intelligence (AI). Basically, that enables machines to automatically learn from data and past experiences to recognize and predict patterns in order to solve problems with minimal human intervention.
Besides, Machine learning employs strategies that enable computers to run autonomously without requiring direct human supervision or regular programming. Unlike regular computer programs and applications, ML applications rely on computed data to execute tasks. They learn from large volumes of data and can grow, develop and adapt independently.
Instead of depending on any given equation as the model for operations, ML algorithms leverage computation methods directly from the data they’re fed. In turn, that leads to adaptively improving their performance, learning and identifying patterns in an iterative process. This again enables machine learning to become a more efficient approach to solving everyday problems.
The more data or samples that ML algorithms feed on, the better they become at learning and performing tasks. For example, machine learning utilizes deep learning to teach computers how to understand and solve problems like humans by learning from experience and available data.
Certainly, the concept of machine learning dates back to as early as World War II with the Enigma Machine. Still, the concept has evolved over time to a more advanced framework of automatically applying complex mathematical computations to expanding volumes and varieties of available data.
Currently, a lot of the technology in various fields have become better due to the infusion of machine learning at the core of their development. ML has become a major foundation for various fields like:
- Computational finance, e.g. credit scoring, algorithmic trading.
- Computer vision, e.g. facial recognition, motion tracking, object detection.
- Computational biology, e.g. DNA sequencing, brain tumour detection, drug discovery.
- Automotive, aerospace, and manufacturing, e.g. predictive maintenance.
- Natural language processing, e.g. voice recognition.
Benefits of Machine Learning in Cybersecurity
Concurrently, cybersecurity is one field that has enjoyed massive growth as a result of advances in machine learning. If not for ML, malicious activities such as data theft, breach of privacy, and financial crimes, would be at peak levels. And the internet will be generally unsafe for everyone. Consequently, machine learning has helped enhance cyber security in so many ways, such as:
Prevention of Future Similar Attacks
One great thing about machine learning in cybersecurity is that it does not just employ strategies to neutralize the threat. Instead, it learns from the attack and properly documents the modalities of the attack, noting its origin, means, damages and mitigation strategy.
It learns from the data to provide preventive measures against such threats in the future. Thanks to that, it gives an advantage over the conventional cyber security practices used today. With the data provided, ML helps cybersecurity teams to learn and prepare adequately against such threats in the future.
Quicker Threats Detection and Response Times
One of the best ways to mitigate a security threat is to avert it from ever happening in the first place. As well as machine learning does this by routinely and frequently scanning the entire system for abnormalities and potential loopholes. So that hackers can latch onto to cause damage to the network.
Thanks to this concept of learning, these loopholes are detected early enough, much faster than humans. This allows you to quickly find proper solutions to the problem, thus saving the system from the often irreversible damage that can ensue from such attacks.
Effectively Secures Authentication
The easiest way for hackers to attack a system is by gaining access to the network. They do this using credential stuffing and brute force to access a company’s network. Big Tech corporations like Adobe, Equifax, and Yahoo have not been spared from this menace. Yahoo lost its users’ private emails, calendars and contacts between 2012 and 2016 to hackers.
Equally, machine learning curtails this issue by securing the authentication when users attempt to log in to their accounts. Authentication mechanisms like facial recognition, motion tracking, voice biometrics, fingerprint recognition, retinal scanners and so on have become rampant in providing security for log in processes. ML builds on data to provide more efficient security solutions for these Two factor authentication tools. For example, Facial Liveness uses this concept to improve facial recognition tools to differentiate between a user’s real face and an image of their face.
Machine Learning Understands the Network's Behaviour Over Time
A business or company’s network’s behaviour is crucial to protect it from cybersecurity attacks. Hence, ML creates clusters of the business network’s patterns that it has identified. It then goes on to detect any security threats or abnormal activities on the network before responding to them. This continuous identification and understanding of patterns over time improves the network’s security and avert future attacks. As it records and blocks potential threats with similar behaviours on the system early enough.
Management and Protection of Large Amounts of Data
A lot of activities occur within a company’s network, many of which include sending and receiving data. The data transferred could be within the company, between the company and other companies or between the company and its customers. In any case, these large volumes of data have to be safe from theft or unwarranted breaches from hackers and malicious software.
As the data increases in volume over time, managing and protecting it becomes increasingly burdensome for cybersecurity personnel. ML seems the best solution to accurately segregate these data and scheme and scan through large volumes of data and traffic. Technologies like residential proxy use machine learning to transfer data securely and detect any threats in the daily barrage of data being transferred.
Top 5 Use Cases of Machine Learning in Cybersecurity
"Covid-19" SMS Scams using Machine Learning
Evidently, during the Covid-19 pandemic, many employees had to resort to working from home. It was an unprecedented time in history as businesses worldwide were forced to start new ways of operating. Finally, this situation led to the rise of remote working or working from home. Due to the global lockdowns restricted nonessential movements. However, scammers and internet fraudsters saw it as a huge opportunity to cash in on unsuspecting internet users through phishing scams disguised as “COVID-19” SMS.
Furthermore, machine learning models use datasets to train the Unified Endpoint Management (UEM) program to increase security for text based applications and SMS by identifying potential threats in authentic messages.
Securing Mobile Endpoints with Machine Learning
Indeed, with machine learning, it applies in many cases involving mobile devices. Android and iOS data privacy, antivirus applications, and security patches this concept at their core. Regarding cyber security, machine learning helps prevent cyber attacks in networks, protects devices and provides vulnerability assessment tools and services.
With the large scale adoption of AI driven personal assistants like Google Assistant, Apple’s Siri, and Amazon’s Alexa, machine learning is being used to improve the security of users’ data from voice based commands. Additionally, to differentiate between the owner’s real voice from hackers’ voice programs.
Mitigating AI based Threats with Machine Learning
For example, as cybersecurity specialists are evolving with AI and ML, so are hackers. On one side, hackers are deploying machine learning to pinpoint loopholes in cybersecurity networks and platforms. Some hackers have created artificial hackers or smart viruses that executes personalized attacks based on the victim’s peculiar conditions.
Enhancing Email Monitoring and Security with Machine Learning
With ML based vulnerability monitoring and assessment software, the speed of cyber attack detection has greatly improved. On the other hand, increased detection accuracy is still a work in progress. However, the most recent monitoring tools detect malware/viruses concealed in emails without opening the email. The Natural Language Processing (NLP) algorithm has enabled ML based vulnerability monitoring and assessment tools to detect phishing attempts concealed in emails sent by scammers or hackers.
Using AI2 to Improve Human Analysis
AI2 is an adaptive machine learning security system developed by MIT to assist cyber security analysts in detecting security threats. For instance, the platform detects and extracts malicious activities from millions of actions performed daily and can significantly detect up to 85 percent of cyber security threats.
Given that, cybersecurity analysts now rely on vulnerability assessment tools like AI2 to detect all types of attacks. Due to the increase in data usage, threat detection and analysis have increasingly become burdensome. Further, it is nearly impossible for humans to do, so analysts had to turn to solutions driven by machine learning to assist in this regard.
Machine Learning Approaches to Cybersecurity
Overall, machine learning algorithms use two major approaches to learning in cybersecurity: supervised learning and unsupervised.
Supervised Learning
Particularly, in supervised learning, ML algorithms learn by feeding on useful information in data with historical labels. In other words, the algorithms know exactly what to look for because the specific kind of anomalies that should not be in the system have been identified and labelled by a cyber security professional. So the algorithm learns to scan through large chunks of data to identify these predetermined anomalies or datasets. Learning is task driven, and targets are predefined to reach a specified set of inputs.
In supervised learning, classification and regression techniques are the most common methods. Presently, these methods are popular for predicting or classifying the target variable for a specific security threat. For instance, to indicate a denial of service (DOS) attack or to identify unique labels of network risks like scanning and spoofing, classification techniques. Example being :Naive Bayes, support vector machines, decision tree, adaptive boosting and logistic regression would most likely be used in shallow models. Supervised learning is very potent in tackling familiar cybersecurity threats and tactics as sometimes hackers resort to using previously known means to attack.
Unsupervised Learning
Secondly, with unsupervised learning, it is a method where ML algorithms analyse and cluster unlabelled datasets. Significantly, the algorithms identify hidden patterns or datasets without requiring human intervention. Unlike supervised learning, unsupervised learning enables ML algorithms to discover similarities and differences in information that the system has not previously identified or labelled.
Similarly, unsupervised learning is best used to mitigate constantly changing attacks or attacks that utilize previously expected or unknown methods. ML algorithms utilizing unsupervised learning achieve this by creating a baseline of the network’s regular patterns. In effect it will flag any operation that breaks these patterns as a potential cybersecurity threat. In fact, unsupervised learning carries out its tasks using the Generative Model or the Discriminative Model.
The Future of Machine Learning in Cybersecurity
With the advances that machine learning has brought to the cyber security space today, there is still much more we can achieve in the near future. While work is ongoing in testing the potential of machine learning in various other fields today, one can only imagine what the future holds. Some of the areas where cybersecurity can utilize machine learning in the future include:
- Identification and profiling of new devices connected to an enterprise’s network.
- Automated detection of anomalies in an enterprise’s network.
- Elimination of potential zero day attacks.
- Instant automation of insights for large volumes of devices.
- Providing policy recommendations for firewalls and security devices.
Thank you for reading our article How Machine Learning is Used in Cybersecurity. Let’s conclude.
How Machine Learning is Used in Cybersecurity Conclusion
Summarizing, with the daily increase in devices, data, and security threats, as well as the large scale scarcity of human resources in the cybersecurity ecosystem, machine learning has proven to be the major solution. Especially to the complicated large scale security problems that organizations face regularly. Despite the ways machine learning is used in cybersecurity as presented in this article, it is safe to say that we have only scratched the surface as to the possibilities of machine learning. Particularly, not just in cyber security but in other spheres of human endeavor.
