Machine learning is one of the hottest areas in data science. This subset of artificial intelligence allows systems to learn from data and use a variety of techniques to make accurate predictions, identify anomalies, and make recommendations.
Machine learning techniques extract information from vast amounts of data and transform it into valuable business knowledge. Most industries use these technologies, but they are most prominent in the fields of finance, marketing, healthcare, retail, and cybersecurity.
Machine learning can also address emerging cyber threats. Cyber-attacks come in many varieties, including structured query language (SQL) injection, phishing, cross-site scripting attacks, malware, social engineering, man-in-the-middle attacks, distributed denial of service attacks, and ransomware. Organizations use machine learning to continuously evaluate data to find patterns that can lead to potential attacks and mitigate them.
Common uses of machine learning techniques
Among other things, machine learning is often used to monitor network behavior, avoid visiting harmful websites, and identify anomalies by detecting unknown malware. These methods can also protect data in cloud environments. The main security uses of machine learning are intrusion detection, malware classification, and network analysis.
For those in the security field, the low quality or complete lack of data used to train methods presents a serious challenge. Accuracy requirements are high in this industry as errors can have serious consequences.
Below we detail some of the most relevant machine learning applications for security.
- real-time email monitoring. Machine learning uses natural language processing and anomaly detection techniques to analyze email content and identify phishing attempts.
- battle with bots. Bots generate a quarter of web traffic, and some even take control of applications to perform specific malicious activities. There are a variety of machine learning techniques applied to identify bots, but the most relevant are identifying response rates, message variability, and temporal patterns.
- Malware detection. Polymorphic malware can adapt to security measures. Various machine learning techniques such as support vector machines, convolutional neural networks, and decision trees can help combat this type of malware.
How machine learning can improve security
Here are four ways machine learning can be used to improve security.
1) Use predictive analytics to source potential security threats.
Organizations need holistic visibility into potential threats, prioritized by possible impact and likelihood of occurrence. Machine learning algorithms assist in threat identification and prioritized threat assessment at a sophisticated level.
2) Prevent SQL injection-based compromises by learning how attackers can modify code to bypass authentication.
SQL injection is one of the most common techniques used by attackers to penetrate an organization’s network. This type of cyber attack exposes information by injecting malicious code into the SQL server.
SQL injection violations are difficult to track down and repair. One of the most effective approaches to addressing this kind of cybersecurity threat is to implement an enterprise security framework with specific scenarios to catch, stop, and eliminate infected code.
3) Eliminate compromised password attacks with a risk-based model that verifies user identities based on behavioral pattern analysis.
Machine learning utilizes constraint-based pattern matching algorithms that help detect patterns in the way users log into systems and machines containing sensitive information.
A password attack is a form of attack in which threat actors use various programs and password cracking tools to steal passwords. There are many types of password attacks, including brute force attacks, dictionary attacks, keylogger attacks, and others. Machine learning enables technology to learn user behavior through risk-based models. Preventing other users from impersonating real users with stolen passwords can greatly reduce password leak attacks.
4) Maintain corporate security settings using a risk scoring model.
Risk scoring models help organizations across multiple geographies quickly plan and achieve growth strategies. These models also allow for rapid scaling of security frameworks, helping to assess threats across many locations.
Machine learning enables IT resources to accelerate the development of security tools such as multi-factor authentication and scale them globally.
In conclusion, machine learning is a revolutionary technology that will revolutionize cybersecurity and holds immense potential for the future. Its ability to analyze vast amounts of data, identify patterns, and make accurate predictions has unlocked new possibilities in fields as diverse as healthcare, finance, and transportation.
