How cybersecurity experts use machine learning to prevent attacks and threats
If you scan the news, you will invariably find an article about data breaches and hacking. These cyber-attacks are becoming more common and target businesses of all sizes. As a result, there is an increased demand for cybersecurity specialists who can help businesses safeguard their sensitive data, systems and networks. With more businesses embracing digital transformation and enabling work from home, more opportunities will open up. One of the issues that many businesses face is finding a proficient cybersecurity experts.
Businesses face a whole range of cyber threats each day. As businesses take measures to protect themselves, cybercriminals are evolving their techniques to exploit vulnerabilities in digital systems and networks. They carry out malware attacks, phishing, social engineering, advanced persistent threats, distributed denial-of-service (DDoS) attacks, and zero-day exploits, which refer to exploiting flaws before the vendor releases a patch to fix them. You need knowledge and expertise to address these threats, and that is the reason why many businesses are actively looking for skilled cybersecurity specialists.
If you love everything related to technology and are keen to make a difference in the way that companies protect themselves from external and internal attacks, you should consider becoming a cybersecurity expert. The US Bureau of Labor Statistics estimates that over the 10 years from 2021 to 2031, the demand for cybersecurity experts will grow by 35%. These professionals enjoy new and engaging challenges every day as they work to keep critical infrastructure safe. Hence, they get an attractive salary. On average, in 2021, cybersecurity professionals earned just over $102,000 annually.
So, if you want an exciting and lucrative career in this field, you may want to know how to become a cyber security specialist. The good news is that it is not that difficult. You can enroll in a Master’s of Science in Cybersecurity program at St. Bonaventure University. This two-year program is completely online and does not require GMAT or GRE. You learn skills and knowledge that make you a proficient cybersecurity expert.
As a cybersecurity expert, you always need to be proactive and anticipate attacks from cybercriminals. However, it is not just external threats that you need to worry about – you must also take into account insider threats. Unintentional or intentional compromises of security by those who have authorized access also pose a risk. Thankfully, you can use a whole range of technologies to your advantage besides the knowledge and skills that you acquire through education.
One of the technologies that you can use to prevent attacks is machine learning (ML). This is a subset of artificial intelligence and allows you to analyze vast amounts of data to identify patterns and make intelligent predictions. You can use ML algorithms to enhance your defensive capability and prevent attacks proactively.
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How machine learning is used in cybersecurity
To ensure that algorithms perform optimally to identify threats and take measures to prevent damage, it is necessary to train ML algorithms using historical data. When you use historical data on threats and attacks, it allows the ML algorithm to recognize and understand patterns and anomalies. As a result, it can respond to actual and potential threats in real time. You can then build an effective intrusion detection system that monitors all traffic on the network non-stop. If the algorithm finds suspicious activity, it can either alert the IT team or take immediate action, depending on the response parameters that you have included in it.
You can also use ML to detect malware. ML algorithms can analyze file characteristics, network traffic and user behavior to find malware, spam and phishing attempts. It is also important to remember that cybercriminals keep evolving their tactics. So, with the help of historical and real-time data, you can train ML models to predict future threats that an organization may face. This allows cybersecurity professionals to be more proactive and implement cybersecurity measures to protect infrastructure as well as systems.
It is prudent to remember that ML is designed to enhance human capabilities and not replace them. So, you should not leave the entire task of cybersecurity solely to the ML model. You should use a mix of ML and traditional cybersecurity methods to ensure optimal security of your company. This will help reduce the risk of data breaches and also protect systems and infrastructure.
Training machine learning models in cybersecurity
Any ML model requires training before it can become adept in cybersecurity. Usually, cybersecurity professionals make use of supervised learning. In this method of training, each sample training data set needs to correspond with specific output labels. When the model analyzes the training data set, it can identify the right label. Remember, the model cannot see the labels, so supervised training ensures that it continues until the model gets it right.
You can use supervised learning to identify as well as categorize anything out of the ordinary. For example, you can use training that has both safe and malicious network traffic. With continued training, the model will be able to identify when traffic is malicious and take measures to prevent it from wreaking havoc on the infrastructure.
Another way to train ML models is with the help of unsupervised learning. The model works on data sets that do not have labels. It analyzes the data to identify patterns. Usually, this method of training ML models is perfect for large data volumes. It would not be practical to label these volumes manually. So, you can use the data as it is and train the ML model to figure out anomalous patterns.
Since unsupervised learning is about recognizing patterns, an ML algorithm learns to group similar patterns in the network traffic. If it finds a pattern that is suspicious and does not conform with the rest of the patterns, it will raise an alert.
As a cybersecurity specialist, you can also use reinforcement learning. Here, the focus is exposing the ML model to actual attack scenarios. The model then uses a trial-and-error method to come up with the right defense strategy. During the training process, you give the model rewards for the right answer and penalties when it makes a mistake. Using the reward and penalty system allows the model to learn, and you can use the learning to create a robust cybersecurity system that can learn from real cyber-attacks and develop defense strategies on the go. Over time, the model will be able to make quicker and more informed decisions.
While a combination of the three training techniques is usually used, you should understand the types of threats that your organization faces and then select the right training methods.
Why data matters when using machine learning for cybersecurity
Since the ML model training hinges on data, it is important that you always use high-quality data sets. Also, make sure that the data covers a wide range of security threats. It should include data such as network traffic, user behavior, threat intelligence, system events and more.
However, you cannot feed raw data into ML models. You need to preprocess it so that the model can understand the data set. Hence, you need to clean the data, reduce dimensionality and extract features. When you extract features, focus only on those that pertain to the cybersecurity issues that your organization faces. This will improve the performance of the model. You also need to add missing values and take care of outliers. This way, the ML model will not produce biased results.
How to use machine learning in cybersecurity
After training the model on different data sets, you are now ready to deploy it. Here are some ways that you can use ML models in cybersecurity.
If you train the model to identify and find intrusion attempts, it allows you to monitor network traffic. This helps ensure that hackers cannot gain access to the network. Usually, ML models use real-time network analysis to find anomalies in patterns. If they do find them, the models can either block the IP address from where the traffic originates or quarantine the affected systems.
Analyzing and finding malware
Cybercriminals always try to stay one step ahead of cybersecurity measures. Therefore, it is common for new malware to appear nearly every day. While there are malware detecting and isolating software, they may not be current. You can use ML models in conjunction with these software. The model finds malware through analysis, but you need to train it using data set samples that are labeled. This allows the model to learn the features of malware families. When it does this, it will be able to identify new as well as unknown malware precisely.
Detecting phishing attempts and spam
Spam and phishing emails are common nowadays. If employees in your organization don’t have an understanding and knowledge of these attempts by cybercriminals to infiltrate networks, you have a major problem on your hands. You can train the ML model to find phishing attempts and spam using labeled data sets. This allows the model to identify spam emails through analysis of email content, the URL from where the email generates, and the headers of the email. ML models look for patterns, keywords and structure to identify spam and phishing emails.
Analyzing user behavior
Threats do not always originate from external sources. In some instances, threats can be internal. These threats can be intentional or unintentional. So, to ensure that internal threats do not pose a problem, it is necessary to train ML models to identify and isolate them. You can do this by using user logins, usage of resources, interaction with systems, and access to files. It allows the model to learn normal user behavior. If it notices a deviation from this behavior, it will flag it immediately.
Challenges of using machine learning in cybersecurity
There is no doubt that you can benefit from using ML in ramping up the cybersecurity measures in your organization. At the same time, you also need to be aware of the problems and limitations you may encounter when using this technology.
Some of the issues that you may face include the following.
Quality of data sets used for training
One of the major problems that cybersecurity specialists face is getting access to high-quality data. You may find it difficult to get data sets with the right labels to train ML models. This is because there are few such examples. Even if you do manage to get hold of a data set, it could have more information on one category of attacks than others. This could lead to biased training of the ML model.
Your organization has a lot of sensitive data. You may find that decision makers will be hesitant to share this data with you to train the ML model due to concerns about privacy or adhering to regulatory requirements. So, it would be difficult for you to get your hands on this existing data. If you try to get it from external sources, you may face the same obstacles.
Adversarial attacks by cybercriminals
Most cybercriminals are tech-savvy and come up with unique ways to bypass the cybersecurity measures that organizations use. At times, these criminals can use brute force to get access to the network. However, to prevent the ML model from detecting the intrusion, they change the input data. This prevents the model from detecting the intrusion.
Overdependence on machine learning models
Some cybersecurity professionals end up relying too much on ML models for cybersecurity. It is important to remember that these models have limitations and cannot be foolproof. ML models may not be able to detect complex or emerging cyber-attacks. So, if you rely solely on ML for cybersecurity, it may not pay off. Instead, you should use a combination of ML and traditional cybersecurity measures to get the best results.
ML is revolutionizing cybersecurity. So, if you are keen to become a cybersecurity specialist who uses modern and traditional methods, then look for the right education and qualifications. Also, understand the limitations of ML models so that you can use them more effectively for cybersecurity.
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