As the world becomes increasingly reliant on technology, the need for comprehensive cybersecurity measures is more important than ever. Unfortunately, the current cyber security landscape is such that it is almost entirely powered by humans. This leaves plenty of room for error, as well as opportunities for cyber criminals to exploit weaknesses in security systems. However, we are now beginning to see Artificial Intelligence and Machine Learning start to become instrumental in the fight against cyber crime.
This surge in the use of AI and ML in cybersecurity is particularly fueled by the clear shortage of cybersecurity professionals that is pushing cyber security companies to look for alternatives that will ensure the cyberspace is safe.
This article will discuss the role of AI and ML in cyber security, starting with brief introductions.
What is artificial intelligence?
Artificial intelligence is a branch of computer science that deals with the creation of intelligent machines that can work and react like humans. In other words AI is used to develop systems with the human intellectual capacity, such as reasoning, learning from past experiences, generalization, and discovering meaning.
By default, AI is the umbrella category under which many other sub-disciplines fall, such as machine learning and deep learning. The history of AI research dates back to the 1950s, when early efforts were focused on creating programs that could simulate human thinking. However, these early attempts proved largely unsuccessful, and interest in AI waned for several decades. In recent years, however, there has been a renewed interest in AI due to advances in computing power and data storage. This has allowed researchers to develop more sophisticated algorithms that are capable of outperforming humans in many tasks, such as image recognition and object classification.
What is machine learning?
Machine Learning is actually a field of artificial intelligence that deals with the design and development of algorithms that allow computers to learn from data and experience. The machines use the data they consume to imitate human intelligence. This way, ML enables computers to make predictions or recommendations without being explicitly programmed to do so. In other words ML focuses on creating systems that can automatically improve given more data.
What is the difference between artificial intelligence and machine learning?
Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but there is a crucial distinction between the two. AI refers to the general ability of a computer to perform tasks that typically require human intelligence. In contrast, ML is a subfield of AI that focuses on how computers develop this intelligence by learning from data and improving their performance over time. Put simply, all machine learning is AI, but not all AI is machine learning.
Machine learning and artificial intelligence in cybersecurity
AI cybersecurity concentrates on the success of the entire cyber security environment. It is the superset of deep learning and machine learning in cyber security.
Combined, AI and ML can be used in cyber security in the following ways:
1. Data classification
Machine learning allows cybersecurity systems to categorize data points using predetermined rules. Classifying and labeling data points is vital when developing a profile of cyber attacks and vulnerabilities, and in building a proactive vulnerability management strategy. Data classification is the core of deploying ML and Ai for cyber security.
This classification makes it possible to identify patterns and trends that would otherwise be difficult to detect. This information can then be used to improve security protocols and prevent future attacks.
2. Data clustering
Machine learning groups data according to its features and traits using the predetermined rules for classification. Analyzing and grouping such data allows cybersecurity experts to identify vulnerabilities and the types of cyberattacks the system is not ready for. These data clusters help determine how an attack can happen, what is exposed, and what avenues attackers can exploit.
3. Recommending courses of action
ML enables cybersecurity systems to analyze behavior patterns, previous attacks and decisions, and recommends appropriate courses of action. Though it may not be an entirely autonomous AI solution, ML provides an adaptive framework that can determine logical relationships by studying pre-existing data sets. Understanding these relationships helps in developing mitigation strategies and responses to cybersecurity threats.
4. Analyzing datasets
Machine learning is making it possible to analyze unfamiliar and past datasets, which allows the security system to determine and analyze new attack possibilities. Analyzing possibilities focuses on the likelihood that the system's condition or an action by a system user may provide an opportunity for cybercriminals to attack. For instance, machine learning can determine a new or unfamiliar dataset that may help identify a weak point and take proactive action before hackers exploit it.
Perhaps predictive forecasting is one of the most important contributions of machine learning in cyber security. Machine learning enables security systems to predict potential attack possibilities and outcomes by analyzing past data. Understanding such possibilities also assists in developing the right threat models to deter the attacks.
6. Malware detention
Hackers are becoming increasingly efficient at building malware that can evade detection by traditional security systems. According to a report from Ernst & Young, there has been a significant rise in the number of disruptive attacks in recent years. The report attributes this trend to the development of more sophisticated malware that is designed to evade signature-based detection systems. However, artificial intelligence (AI) cyber security systems have the potential to detect the inherent characteristics of malware, rather than their signatures. This makes AI-based security systems much more effective at detecting and thwarting malicious activity.
7. Prioritizing threats
Numerous security alerts, most of which are false alarms, can overwhelm a security system. A system may use most of its resources in analyzing common security threats and miss out on a real attack. Artificial intelligence can enable security systems to identify and prioritize the threats, thus saving time and resources.
8. Initiating automated responses
Artificial intelligence can be used to automate repetitive tasks, like responding to low-risk alerts. Such alerts have a high volume and thus need a fast response but a low risk of making mistakes. Artificial intelligence can handle such alerts with a high degree of certainty. Furthermore, it will help cybersecurity professionals to focus on more serious threats.
9. Endpoint security
As more people continue to work remotely, the need for interconnectivity continues to grow and brings about more cybersecurity challenges. Since companies now require more robust endpoint security, AI-based endpoint solutions can propel endpoint security, proactively identify and block threats.
10. Battling bots
Computer bots are small pieces of code that can carry out repetitive tasks, including interacting with users and other systems across networks and the internet. By masquerading as a human user, bots can carry out all kinds of automated tasks, from spamming message boards to stealing credentials.
The malicious use of bots is nothing new — spammers have been using them for years to clog up email inboxes and flood forums with advertising. However, the rise of social media and the proliferation of online services has given bots a much wider range of potential targets. These days, bots are used to carry out all kinds of automated attacks, from hijacking user accounts to creating fake user accounts.
Manual responses to such bot-driven threats can be challenging and time-consuming, which is why security experts are increasingly turning to AI for help. AI-powered security solutions can help to identify and block bot-driven attacks in real-time, protecting users and systems from harm.
11. Employee training
Traditional training methods can be time-consuming and expensive, and they often don't take into account each employee's individual level of understanding. Enter AI-based training. By using AI to analyze each employee's performance, you can create personalized cyber security training programs that are tailored to each individual's needs. This not only saves the company time and money, but it also helps to ensure that employees are receiving the best possible training on cyber security. And as a bonus, AI-based training can be delivered remotely, making it even more convenient for busy employees.
Best practices for effective use of ML and AI in cybersecurity
Though ML and AI can greatly simplify cyber security as we have seen from the various ways companies can deploy these tools, you need to make sure that you are doing it right. Follow these best practices:
- Keep data clean and organized: This means removing any duplicate or irrelevant data, and making sure that all the data is labeled correctly. This can be a time-consuming process, but it is essential for training accurate models.
- Train multiple models: Training just one machine learning model is often not enough. It is important to train multiple models, using different algorithms and parameters, in order to find the most effective solution.
- Test your models: Once you have trained your machine learning models, it is important to test them on real data. This will help you to assess their accuracy and effectiveness, and make sure that they are able to generalize well.
- Monitor your models: Even after you have deployed your machine learning models for cyber security, it is important to monitor them closely. This way you can detect any issues early on, and make sure that the models are continuing to perform well.
- Be prepared to retrain your models: Machine learning models need to be retrained on a regular basis. As data changes over time, so too must the models that are used to analyze it.
Challenges of ML and AI in cybersecurity
- Data shortage. In order to train a machine learning model, you need a large dataset of labeled data. However, in cybersecurity, it can be difficult to obtain such a dataset. This is because most companies are reluctant to share information about data breaches or attacks. This is making it difficult to obtain the necessary data to train effective machine learning models.
- Complexities around data. Cybersecurity data can be very complex, due to the nature of attacks and the vast amount of data that needs to be analyzed. This complexity can make it difficult for machine learning models to accurately learn from and classify this data.
- Rapid changes. Attack vectors are constantly changing. As a result, the patterns that machine learning models learn today may be different from the patterns tomorrow. This makes it difficult for machine learning models to keep up with the changing landscape of cybersecurity.
- Unsupervised data. In many cases, we only have access to unlabeled data when training machine learning models for cybersecurity. This can make it difficult for the models to learn effectively, as they are not given any specific guidance on what to look for in the data.
- Algorithm hacking. Criminals also understand AI, and in fact they are also using it to devise new attacks. A small tweak in an algorithm can cause severe damage. It means hackers can infiltrate your cyber security algorithm and make it do the opposite. Algorithm hacking has been demonstrated by a couple of researchers. For example, researchers were able to dupe medical ML programs just by tweaking a few things like removing some images and adding some. Bots have been known to fake engagements online while consumers can also interfere with algorithms to benefit them, which in itself is a huge cyber threat.
Interested in learning more about Cybersecurity?
Check out these blogs:
- Why Is Your Security Posture Important?
- Top 10 Types of Cyber Attack Vectors
- Types of Email Attacks
- What is a Vulnerability Management Program and How to Build It
- Common Network Vulnerabilities and their Types
- Importance of Network Security: Staying on Top of IT
- What is Vulnerability Scanning?
- Dark Web Cyber Security Threats
- Patch Management Policy Guide
Getting started with ML and AI for cybersecurity
A good place to start is by considering how machine learning and artificial intelligence can be used to supplement your existing security infrastructure. For example, you might begin by using machine learning algorithms to help identify unusual patterns of activity that could indicate a security breach. Or you might use artificial intelligence to help automate the process of identifying and responding to potential threats.
Another important step is to make sure that you have the right data set for training your machine learning models. This data should include both normal and malicious activity, so that your models can learn to accurately distinguish between the two. Once you have your data set, you'll need to decide which features you want your models to focus on. This will depend on the specific needs of your business, but some common features include user behavior, network traffic, and system log files.
Once you've decided how you want to use machine learning and artificial intelligence in your cybersecurity efforts, it's important to keep up with the latest developments in this rapidly evolving field. This means staying abreast of new attack methods and cyber trends, as well as advances in machine learning and artificial intelligence technology.
While AI may never completely replace human intelligence in this field, it has already begun to show great potential in assisting humans to identify and stop cyber threats. As AI and ML technology advances, these tools will certainly become even more sophisticated and effective in helping to keep our networks safe.