Every day, cybercriminals are engineering new and sophisticated methods to break into networks and systems to steal people’s information. In 2020 alone, there was a 600% increase in cyberattacks due to the pandemic.
Most of these attacks are now automated using botnets. This makes it quite challenging for cybersecurity professionals to keep up with the pace at which these criminals keep transforming and upgrading their attacks. However, with the power of Machine Learning(ML) and Artificial Intelligence(AI), cybersecurity experts can mitigate and curb most of the attacks these criminals devise.
In this post, we will look at how machine learning and AI are helping the cybersecurity space.
So, without wasting much time, let’s dive into it:
Artificial Intelligence and Machine Learning:
Artificial intelligence and machine learning are two terms people tend to get confused about. However, these terms are different. Before we dive into how these two processes can help us in cybersecurity, let’s throw more light on what they mean.
To make stuff simple, AI is an umbrella that encompasses machine learning. So, what are they?
AI has to do with simulating machines and computers to think and make decisions in real-time using real-time data.
Machine Learning (ML) is a subset of AI. It involves using computer algorithms to improve the thinking ability of systems by feeding them with data to make them more accurate at decision making and predictions.
The Need For AI:
With the increase in IoT devices, everything around us is becoming smart. By the end of 2025, the world is expected to have a total of 30 billion IoT-connected devices. These devices are expected to make our lives easier. However, all these devices will be connected to the internet, which means these devices are susceptible to intrusion and attacks by cybercriminals.
Managing and securing 30 billion devices can be a daunting task, and there will be an increase in the number of attacks. To mitigate these attacks, we will need a trained system to help us automatically detect attacks and protect the valuable information of people and organizations.
Besides the increase in the number of attacks, cybercriminals are now selling hacking tools, ransomware, and DDOS attacks cheaply. These hacking tools are sophisticated, constantly evolving, and are capable of causing devastating havoc to expensive systems and networks. This is why it is crucial to integrate AI and ML into cybersecurity to help us mitigate these kinds of attacks.
In addition, due to the increase in the number of IoT devices, there will be an increase in network logs. The purpose of a log is to help the user identify all activities on a network. With a log, the user can identify any malicious or unauthorized activity on a network.
The increase in IoT devices means network managers will have tons of devices to manage; however, analyzing these network logs would be manually impossible. This is why we need to have AI and ML to help us analyze and manage these logs within a blink of an eye.
How AI and Machine Learning Is Helping Us Deal With Cybersecurity Risks:
1) Fighting Credit Card Fraud
Internet fraud is a massive problem in the world of finance. In 2020 alone, people lost 19.7 billion dollars to fraud. Also, there were 2.2 million fraud reports that same year, which is about 48% of all FTC reports.
All these stats show the magnitude at which fraud is impacting the lives of businesses and individuals. Sadly, these fraud attempts and activities aren’t going to stop. However, one way we can mitigate this is by harnessing the power of AI to help us detect any suspicious activity.
The Problem With Banking Rule-Based Systems:
Most banking systems use transaction rules to counter fraudulent purchases through a human review process. The traditional rule-based approach has two metrics that help prevent fraud: spending limit and location.
One major problem of the traditional system is the high chance of false-positive occurrences. This means that an actual purchase from a real credit card user can be flagged as a suspicious fraudulent activity when it isn’t. For example, suppose a user spends a certain amount of money at an unusual location. In that case, the traditional fraud detection system will flag that as a fraudulent transaction and decline the transaction.
These false-positive occurrences can be frustrating for businesses and consumers and create a flawed perception for a business, resulting in consumers no longer making purchases from the impacted business. Also, banks and companies will have to spend money to ensure their manual review and fraud detection process is accurate.
This goes to show how ineffective the traditional fraud detection process can be inefficient in some cases.
How AI and ML Solve This Problem:
Customers generate a load of data from their activities, especially with how they use their credit cards. And through AI, banking systems can learn their customers’ buying patterns and behaviors to help detect any suspicious activity. This can be accomplished by AI systems analyzing any transaction for fraud risk to help banking systems either approve or notify customers of any suspicious transaction.
So, how will this work?
Every bank has historical data of the transactions of their credit cardholders. Once a purchase is made, their transaction profile gets updated. The bank compares the spending behavior of a credit card holder to other credit cardholders on the banking information system. It groups credit card holders into clusters based on common purchasing behavior. The AI and ML systems then study and analyze a great depth of history to understand how customers use their cards, and it can detect if there is an anomaly in the spending habit of a user.
What happens is once a transaction is made, the AI system compares that transaction with that customer’s previous purchasing history and that of others in the cluster. A risk score is assigned after each analysis. If the risk score is high, the AI integration will decline the transaction, or an alert is sent to the customer and the bank for further analysis.
In addition, the machine learning aspect of AI continues to learn your buying pattern with that of other users. It also records and stores any fraud cases in its database. When a risky transaction is made, it can compare it with the fraud cases stored in its database to make a more accurate decision. This makes it an excellent way for banks to prevent any false positive predictions.
This feature also allows credit cardholders to use their card in unique ways without being flagged as a fraud because their spending habit will be compared with other users’ behavior. If it correlates with the spending habits of the other bank users, the transaction will be permitted. However, before a transaction is declined, the AI system will notify you to either accept or decline the transaction.
2) Improvement Of Enterprise Security.
AI can help enterprises determine and mitigate attacks. Through AI, we can automate the detection and mitigation process of enterprise security by using three main tools:
- SIEM tools
So, how do these tools work together?
Through AI, you can install sensors on every device in an organization to detect any breach. Any logs from these sensors will then be aggregated and funneled through a data lake to the SIEM tool.
The SIEM tool comprises software that can analyze user behavior, identify anomalies, correlate events, respond to incidents, and report. Once there is a threat detected, the SIEM tool will send an alert to the analyst. The SIEM tool helps cybersecurity analysts make effortless analyses which would have taken days with human effort.
Once an alert is detected, the AI network allows organizations to automate the appropriate response to counter some of these alerts through APIs such as NAC, IDS, and what have you.
These tools all work together to improve the overall security posture of the system. Interestingly, the AI network keeps learning, and over time when attackers devise new attacks, the algorithm can detect such attacks by comparing new attacks with old attacks.
3) Botnet Attack Prevention.
Most of the IoT devices put on the market are vulnerable to cyberattacks, making them fundamentally insecure due to the usage of outdated Linux frame ware, open telnet ports, and other security issues. These security gaps make our IoT devices susceptible to botnet attacks. Cybercriminals are now leveraging botnet attacks to compromise multiple computers simultaneously.
A botnet is a network of malware-infected computers controlled by a single person, which is used to execute multiple cyberattacks. Most cyber criminals use botnets to attacks multiple devices and networks simultaneously.
Most botnet attacks are aimed at causing DDoS attacks. DDoS stands for Distributed Denial Of Service. This type of attack aims to disrupt the traffic on a network or system to prevent users from accessing the network or system. DDoS attacks are detrimental, and most prominent businesses have been victims of such attacks. A famous example was in February 2020 when AWS was attacked.
How AI Helps Prevent And Detect DDoS Attacks:
AI uses a binary classification model to classify attacks as being benign or malicious. What happens is that we feed the neural network with information from the captcha server. This dataset is used to train the neural network, and with the training, the neural network can detect if an attack is malicious or benign.
Inside the neural network, we have a binary classifier. This classifier is what helps us distinguish between malicious and benign traffic.
4) Intrusion Prevention and Detection.
AI is changing the way intrusions are detected and mitigated.
AI is helping businesses and individuals protect their devices and valuable information by detecting malicious attacks. There are tons of cyberattacks happening each second, and it is frustrating and laborious for us to detect and mitigate these attacks manually.
Besides the daunting process of mitigating intrusion attacks, attackers keep changing the way they carry out their attacks. However, through AI, we can utilize the power of anomaly detection to protect systems from new and sophisticated attacks.
AI is helping businesses and individuals to prevent unauthorized access from attackers getting access to data centers and confidential information. One way AI is preventing unauthorized access is through biometric security. Every device now requires that a user uses either a facial ID recognition or fingerprint to access the device. This serves as an extra layer of security that is back-breaking to penetrate.
5) BEC Scams.
AI and ML have become essential to helping businesses and governments fight off BEC scams. In 2020, businesses lost 1.8 billion dollars to BEC scams. BEC attackers primarily using phishing attacks and computer intrusion tactics to compel victims to send money to the attacker. However, with the power of AI, we can detect and prevent such attacks.
What Is A BEC attack?
BEC stands for Business Email Compromise, and it involves attackers impersonating or compromising the emails of businesses, NGOs, and even governments to solicit funds from their customers or employees.
How AI Helps Mitigate Such Attacks:
Through machine learning, we can detect the writing style of an email and then compare them with past emails to detect any anomaly. Through this, the ML system can identify any fraudulent emails from compromised and impersonated senders. In case of an anomaly, AI technology will discard such emails.
6) Quick Security Analysis.
AI allows data analysts to perform analysis far quicker than any human speed can catch up to. One major problem in the security space has to do with false alarms being raised. However, the AI neural network can pick up all incoming signals, analyze them, and compare them with pre-existing data to accurately determine whether the attack is malicious or not.
All this information can be used to train and improve the understanding of the AI network.
There are numerous benefits of AI and ML in the world of cybersecurity. Harnessing the power of AI and ML in addition to human knowledge will enable us to keep up with the ever-changing world of cybercrime.
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