The technology landscape is every-growing and, as each year pass, it shows no signs of slowing down. Instead, we can see progress towards a seamlessly digital future rapidly increasing. And its evolution has tremendously advanced compared to where it was just a decade ago.

Unfortunately, malicious cyber attacks have also been rapidly rising, right alongside technology. Each year, hackers find new, more complex ways to attack businesses ranging from SMBs to enterprises. Although we don’t know what types of technology hackers will develop next to slide in past your security without detection, there’s still hope.

Companies like Cloudwick use machine learning to help businesses protect themselves from fraud. Read below to find out what machine learning is, the current cybersecurity problems, and how Cloudwick can help you.

What is Machine Learning?

Before we can explain how Cloudwick quickly and efficiently, we have to explain what machine learning is. Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and interrupt data on their own to make accurate predictions without the aid of a programmer. It uses algorithms to identify patterns and make decisions.

Supervised Learning

Supervised learning is when a machine uses labeled training data from inputs and outputs already established to determine the outputs for unlabeled test data. The problems associated with supervised learning can be broken down into either classification or regression.

Classification models focus on predicting a discrete output value based on a labeled training dataset. We’ll use sports balls as an example, specifically a football, basketball, and bowling ball. From data you’ve already collected, you decide to label all the balls based on features such as texture and shape. All the information you gathered up until this point, including the labels, is the training data. In your training data, you noted that a football is a bumpy prolate spheroid, a basketball is a bumpy sphere, and a bowling ball is a smooth sphere. Based on the information in your training data, the machine classifies a smooth sphere in your unlabeled testing data as a bowling ball. While this example only has two features, not all models will be this simple. For a more complex model, you’ll need more features to be able to distinguish different types of balls more accurately.

Regression models focus on predicting a continuous output value. We’ll use house prices as an example because they’re continuous values. The training data includes square footage. So, a 1,500 sq. ft. house in Louisiana would cost $100,000, a 2,000 sq. ft. house would cost $150,000, and a 2,500 sq. ft. house would cost $200,000. Based on our training data, the machine can determine that a 2,250 sq. ft. home in the unlabeled test data would cost somewhere between $150,000 and $200,000. For a more accurate model, you might also include material that the home was made with, its location, and the number of rooms it contains.

The problem with supervised learning systems is that machines become biased because they are only shown data that the humans that designed them knew about. They won’t know how to label test data that they’ve never seen before in the training model.

Unsupervised Learning

Unlike supervised learning, unsupervised learning isn’t labeled, and machines interrupt information without any guidance. These systems are also good at recognizing hidden patterns and interrupting large datasets. The problems associated with unsupervised learning are broken down into either clustering or data compression.

Clustering models focus on grouping data by their similarities. Imagine that you’re trying to segment your email list, but don’t know where to start. The machine will cluster your customers on trends it interrupts in the data. The clusters it might come up with are basic ones such as age and location or complex ones such as hobbies and motivations.

Data compression models focus on reducing data complexity without sacrificing its integrity. An example is the reduction of a photo’s size without hindering its quality. The photo will look the same, but more compact. Data compression models ask themselves if a feature is necessary or redundant. If it finds data to be redundant and believes that it will be the same if removed or decreased, then that’s what it does.

The main problem with unsupervised learning is that it’s unpredictable, so you don’t know if you’re getting the right results. Because there’s no structure, the machine doesn’t know if it’s making effective clusters or reducing something important.

Reinforcement Learning

Reinforcement learning is when an agent learns from its mistakes via trial and error to maximize rewards. A few agents associated with reinforcement learning are value-based and policy-based.

Value-based agents focus on relaying to an agent how much rewards it will get in each state with each action. We’ll use unstable rocks as an example. You’re trying to cross a river and you don’t know how to swim. To get to the other side, you need to navigate your way through unstable rocks. There are two paths of rocks that you can take. Before putting your full weight on a rock, you test both out to see if either gives. One rock gives too much, so you know that if you step on that one, you’ll fall in the water. You continue evaluating each rock before taking a step until you get to the other side. The positive reward you’ll get in this scenario is not falling in, even if you get your feet wet, and the negative reward would be falling in.

Policy-based agents focus on the policy rather than the value by mapping each state to the best action for that specific state. Using a chess board, you can map the best course of action for each chess piece.

The problem with reinforcement learning is that you don’t know how long it will take to get through each state, determine its value, and then reach the maximum rewarding state – especially if the states are more complex.

Machine Learning’s Potential with Cybersecurity

When cyber attacks happen, everyone looks towards the cybersecurity team because they want to know why a few people couldn’t stop it. While cybersecurity teams are a necessary and important part of a company, they shouldn’t have to bear that responsibility alone, especially when there’s a lot of money on the line. Not only should the entire company take part in protecting the company’s assets, but companies should also consider how machine learning can help them detect problems before they arise.

Current Problems

The advancement of technology has brought about a whole new generation of cyber threats that companies have to deal with, such as:

●     crypto-jacking, which is a form of ransomware. Hackers implant crypto mining codes in a victim’s computer via a link in an email or on a website or ad that they clicked on because of an infected script known as Powershell. The most troublesome aspects of Powershell are that they’re hard to track and are virtually undetectable by antivirus softwares.

●     the problems employees unknowingly cause for companies. Even with the best strategy, directions, and steps in place to help them avoid cyber attacks, some don’t follow through, leading to hackers getting in.

●     a lack of focus on protection. So, when a hacker does get past their security, they don’t have any measurements in place. Instead of living in a bubble believing that a hacker can’t get past their security, it’s best to assume that they can and will. By doing this, they’ll make it harder for a hacker to cause substantial amounts of damage or any at all.

●     minimal qualification requirements for cybersecurity experts. Until recently, they only had to complete a certification before they were given a position as head of the cybersecurity team. However, those certifications aren’t sufficient enough for the amount of knowledge that is required to effectively handle all types of malicious activity.

●     the lack of people looking for jobs in cybersecurity. Despite the rise of cyber criminals, the jobs aren’t in as high of demand as you might expect.

How Machine Learning Can Help

Although each type of machine learning has its own problems, in some way, they all can contribute to the fight against malicious activity.

Supervised learning using a regression model would be able to detect fraud to which a company has already been exposed. Since regression models focus on making predictions, it would also be able to determine a hacker’s next moves because of user behavior analytics. If a company has previously experienced a cyber attack, user behavior analytics will conclude that certain traffic patterns on their site can potentially lead to another attack.

When using a classification model, you’ll label normal behavior in your network and any anomalies you’ve encountered, such as phishing, ransomware, malware, and spoofing.

Unsupervised learning using a cluster model would be able to detect fraud before it even happens. The machine learning system clusters all patterns – benign, malicious, and anything in between. However, the first and most difficult obstacle to overcome is understanding each cluster, since there aren’t any labels.

Although it happens over a longer period of time, reinforcement learning also has the potential to accurately and effectively detect anomalies. It involves teaching an agent how to detect anomalies by putting it in an environment where there are different layers of known malicious and normal activity. Once in that environment, the agent will receive a positive reward for correctly tagging malicious activity and a negative reward if it flags anything normal. However, as previously mentioned, it would require months for a reinforcement machine learning system to tag malicious activity accurately enough for it to be an effective cybersecurity tool for a company.

Each machine learning system is imperfect in its own way. Supervised learning uses only historical data to predict the future, while unsupervised learning leaves too much data when up to assumption. Is this clutter malicious data? Can we assume that the smallest clutter is malicious and the larger one is benign?

Even if a cybersecurity expert was to make the right assumption and take action on a certain cluster under the pretense that it was majority malicious activity, they wouldn’t know what patterns had been detected. Reinforcement learning takes time to train and discipline an agent. But supervised and unsupervised learning’s flaws complement each other.

Semi-Supervised Learning

Semi-supervised learning is a hybrid form of supervised and unsupervised learning. It uses both labeled and unlabeled training data. Typically speaking, it’s simple to use supervised learning for small datasets because labeling isn’t too time-consuming. However, when you’re working with larger and more complex datasets, it’s difficult to find domain experts to take the time to carry out and perform such a time-sensitive task, especially because a company may not have the resources to fund the process.

So, that’s where semi-supervised learning comes in. Using labeled training data, you try to understand the unlabeled larger dataset. In reference to cybersecurity, your labeled data probably contains both normal and malicious historical data. And, since you fully comprehend the labeled data, you’ll have a general starting point when the unlabeled data starts grouping itself.

Cloudwick

Cloudwick is a provider of open source, data lake, big data, cloud, and advanced analytics to enterprise companies around the world. The company also developed Cyber Data Lake (CDL), which is the Neural Security System of Intelligence for Cybersecurity.

Data Lake

Before Cloudwick helps with fraud detection, its engineers build pipelines using data lakes. A data lake is a repository of data, both structured and unstructured, that stores and analyzes data for increased flexibility and simplicity. When companies have large amounts of data, while it’s good for machine learning systems to accurately predict fraudulent patterns, it’s even harder to make use of that data when it isn’t cleansed. That’s why data lakes are so useful.

Machine Learning

One of the ways Cloudwick uses machine learning systems to help companies detect fraud is by using the Random Forest algorithm, and then partnering with Spark ML. The Random Forest is a supervised learning classification and regression model algorithm that is a collection of Decision Trees. A Decision Tree divides data based on different features to come to a conclusion and make a decision.

For a better explanation of Decision Trees, let’s say that you’re trying to figure out if your dad will like his new power tool set that you plan on buying him for his birthday. You know what tools your dad has, and the Decision Tree will use this data as tree nodes. From there, it will label whether he likes them or not; these nodes are known as the edge nodes. Once all the data is in the Decision Tree, you’ll be able to make an educated decision regarding whether you should buy the toolset or if you should find something else.

The problem with a Decision Tree is that, by itself, its accuracy is low. However, a Random Forest combines many Decision Trees, which normally results in an higher accuracy. Continuing from our birthday gift example, you might assume that he won’t like the gift. However, when you ask multiple people that he knows, ranging from his siblings to his favorite coworker, you might get an overwhelming response that he will like it.

Cloudwick’s engineers craft a use case based on historical data to find relevant features. Then, the algorithm tests this data in the Random Forest for accuracy via Spark ML. Spark ML is a cluster computing system that is scalable and fault tolerant. Once the Random Forest is accurate enough, Cloudwick reuses it on different datasets for continuous fraud detection via Spark ML’s pipeline.

AWS Advanced Consulting Partner

Amazon Web Services (AWS) is a cloud-based platform where businesses can go to lower IT costs while improving speed and scalability. It offers services such as storage, analytics, and deployment. Cloudwick is an AWS Advanced Consulting Partner with a Big Data Competency certification. On AWS, offers machine learning, analytics, data lake, and data warehouse modernization.

Cloudwick’s consulting service can help you analyze your current and future state, a data lake and data warehouse modernization plan based on your business goals and needs. So, while this won’t necessarily solve your problems, you’ll be able to determine which areas of your security system need improving. Then, you can use this information to conduct a training workshop for the workers you already have on staff.

While machine learning still has a long way to go before it’s even remotely close to being a perfect solution for cybersecurity, it’s a good place to start. Cloudwick not only can help you use machine learning to detect fraud, but it also can help you assemble your data in a data lake for easier access and usage.