In this modern digital world, artificial intelligence (AI) and machine learning (ML) have surfaced as the most valuable tools for handling cybersecurity threats. It proves to be a huge loss when an organization or individual encounters a cyberthreat. Various factors contribute to these threats and it may not be always straightforward to completely prevent these threats from happening. Fortunately, ML and AI technologies have emerged to be quite effective and efficient at detecting cyber threats and protecting critical data.
As per the research conducted by Accenture, 81% of firms face trouble while acquiring the high-quality, reliable data required to train ML and AI models for various cybersecurity applications. This fact implies that it is challenging to deal with cyber threats.
The Cybersecurity and Infrastructure Security Agency (CISA) predicts that cyberattacks amount the $242 billion to the United States every year. With the evolution of technology, hackers’ attempts are getting more and more sophisticated. Therefore, it is inevitable to use the best practices of AL and ML to detect cyber threats and protect data. Fortunately, AI & ML engineers have achieved considerable success in developing threat detection technology. Currently, the finest AI cybersecurity systems employ machine learning in their threat detection operations.
Significance of Artificial Intelligence (AI) In Cybersecurity
The use of Artificial intelligence (AI) allows machines to efficiently accomplish tasks that usually demand human intelligence. The tasks can involve making decisions, identifying human speech, remarking on visual components, and language translation. Specifically, AI utilizes training data to understand context and conclude what approach to adopt to respond in various situations.
AI in cybersecurity involves the implementation of techniques like machine learning, deep learning, and natural language processing. The key intention behind using these techniques is to create more automated and smart security defenses. By using AI, organizations can be able to automatically identify new threats, detect unauthorized attack vectors, and ultimately protect their sensitive data.
The process of using AI in cybersecurity involves using AI techniques namely machine learning, deep learning, knowledge demonstration and reasoning, and NLP (natural language processing). The outcome of implementing these techniques is that AI would ensure intelligent and automated cyber defense. Consequently, organizations can determine how to mitigate cyber-attacks.
Understanding the overview of the significance of these technologies in cybersecurity, let’s explore the significance of them when it comes to cyber threat detection and prevention.
What makes AI and ML-powered threat detection and prevention approaches valuable?
The popularity of using ML and AI-driven security & threat detection methods is growing. One of the prominent reasons behind this is that businesses are encountering sophisticated cyber-attacks. Such technologies are equipped with advanced capabilities to detect and avoid cyberattacks in real time. Hence, they offer highly effective defense against cybercriminals.
Some of the leading AI companies support cyber threat detection services. The services are used by various organizations spanning industries like healthcare, finance, and government. By investing in these services, companies can safeguard their sensitive data and systems against cyberattacks. So, they ensure that their processes continue operating smoothly.
Now let’s get familiar with various ways in which Machine Learning detects threats in Cybersecurity.
Applications of AI in Cybersecurity
How effective AI is when it comes to detecting cyber threats and protecting data can be best understood if you know its applications. So, let’s get familiar with the prominent applications of AI in cybersecurity.
Phishing detection and prevention control:
One of the greatest cybersecurity threats that businesses of all industries encounter is phishing. The use of AI in email security solutions allows companies to learn about anomalies and alerts of malicious activities. Moreover, it can evaluate the content and perspective of emails to promptly determine whether they are legitimate, spam messages, or part of phishing campaigns. Let’s take an example to understand this. AI can easily and quickly detect indications of phishing like forged senders, erroneous domain names, and email spoofing.
Password protection and authentication:
Implementing AI in cybersecurity allows organizations to more effectively protect passwords and safeguard user accounts by employing authentication. Today most websites support features that permit users to log in to buy products or submit contact forms that ask for inputting sensitive data. Hence, additional security layers are required to ensure data security. AI prevents your sensitive data from getting vulnerable into the hands of malicious cyberattackers.
Network security is accomplished through many processes and one of them is time-intensive processes. These processes create policies and understand the network’s topography. By following the policies, organizations can execute processes to identify authentic connections compared to those that may need to be evaluated for malicious conduct. Such policies can assist organizations in applying a zero-trust approach to ensure security.
Cybercriminals continually use more sophisticated techniques and methods. Hence, each year, we witness tons of new vulnerabilities. Therefore, businesses struggle to handle the huge volume of new vulnerabilities being encountered daily. The traditional systems are incompetent at avoiding such perilous threats in real-time.
Behavioral analytics help organizations detect evolving threats and commonly occurring vulnerabilities. Conventional security defenses depend on attack signatures and indicators of compromise (IOCs) to detect threats. But this approach is impracticable since we witness thousands of new cyberattacks each year.
How AI can help security teams detect threats?
Every day we witness tons of unknown cyberattacks happen. Currently, there are over 1 billion malware samples, and the number continues growing. The IT security teams are trying to keep pace with the ever-changing technology prospect and cyber rivals. However, they are still stunned looking at the speed and volume of threats. Humans can’t manage them on their own.
This is where machine learning and AI come into the picture. They help security teams administer their workload by supervising, identifying, avoiding, and alleviating threats. The corresponding tools use predictive intelligence and sophisticated algorithms. So, they can combat malware, perform pattern recognition, and find and prevent attacks before they spread damage. Furthermore, they instruct teams about new attacks, anomalies, and prevention tactics.
Decreases false positives
The mentioned AI algorithm can easily and quickly learn from its mistakes. This lets it always be the frontrunner and always prove to be the best version.
You can consider an ML discipline to be good if it shows patterns of behavior, estimates the type of attack, and indicates ways to combat cyber threats. The ML algorithm can be trained with various kinds of cyberattacks and can learn various methods to get privileged access. Moreover, they can customize in real-time to a particular situation. An outstanding ML approach can effectively learn from the false positives. However, it would also generate false alerts that would need human examination.
Most elements implemented in the NIST cybersecurity framework can detect, defend, respond, recognize, and recover. ML can accelerate all these tasks.
In addition to quickly detecting threats, ML can also detect 90% or more of all recognized and unrecognized threats using reinforced and unsupervised learning.
Detects threat actors
ML can’t estimate future attacks, but it efficiently predicts the future move through an adversary after an attack is identified. This allows it to control intrusion. For example, if a resource unlocks a connection to a familiar malicious IP address, ML can recognize the same and automatically close it before any data gets exfiltrated.
Anomaly detection is defined as the procedure of recognizing behaviors or patterns that deviate from a system’s standard behavior. This approach helps detect the impending security threats. ML and AI techniques are used to augment the potential of anomaly detection systems. They allow them to detect anonymous threats and acclimate to shifting attack patterns.
In the threat intelligence landscape, one of the most famous applications of ML and AI is threat hunting. It employs a proactive approach to detect indications of adversarial and compromise activities conducted in the network infrastructure of an organization. It proactively searches for undetected cyber threats present in an organizational network.
The systems developed using ML and AI considerably intensifies these efforts by using various sophisticated techniques. These techniques are NLP for analyzing textual data, deep learning algorithms for recognizing patterns in complex data structures and analyzing graphs for mapping complex relationships among different network entities.
With the implementation of the aforementioned techniques, ML and AI-powered threat-hunting solutions can autonomously examine huge amounts of data. Moreover, these solutions determine intricate patterns and accurately flag prospective threats.
Earlier, threat hunting was a time-consuming and manual process. However, the adoption of advanced analytics, machine learning, and user behavior analytics (UBA) has partly automated this approach to boost its efficiency.
ML and AI techniques are implemented in threat prediction systems to investigate historical data and identify patterns that might depict future cyber threats. Consequently, organizations can adopt measures to protect themselves before an attack happens.
Both these techniques can also automate the threat intelligence sharing process. Threat intelligence sharing platforms implement these techniques to automatically collect and examine threat intelligence data from various sources. So, they facilitate its distribution to other organizations.
These techniques automate processes like collection, analysis, and distribution of threat intelligence. So, they help organizations strengthen their cybersecurity posture and effectively protect themselves from cyber threats.
Challenges faced in threat prediction:
ML and AI techniques promise to improve cyber-attack prediction. However, they come with certain challenges. One of the prime challenges is the need for a huge volume of data to train the relevant deep-learning models. Other challenges are the need for substantial computational resources to employ reinforcement learning and the prospective for false positives/false negatives using unsupervised machine learning.
Use of obsolete antivirus protection:
Antivirus software is a vital element of cybersecurity because it strengthens data protection. It detects and avoids malware infections happening on computer systems. The obsolete antivirus software uses signature-based detection methods. They can only identify known threats and are usually unproductive to prevent new malware.
The following list shows some of the prominent antivirus software that employs these algorithms.
- Norton Antivirus
- Kaspersky Antivirus
- McAfee Antivirus
- Avast Antivirus
- Bitdefender Antivirus
Not only machine learning, they also implement other technologies like predictive modeling and deep learning to evaluate a giant volume of data and identify patterns and cyber threats.
Limited vulnerability management:
Vulnerabilities means flaws which cyber attackers can exploit. The implementation of ML and AI can automate vulnerability management to boost efficiency. ML algorithms can scan systems to detect potential vulnerabilities and then prioritize them as per their risk level. Consequently, security teams can effectively prioritize critical vulnerabilities.
To identify potential vulnerabilities, AI-powered systems can constantly monitor and inspect the systems, network, and applications. So, they facilitate early detection that will help bring timely solutions.
Note that AI-powered vulnerability management solutions are not comprehensive solutions. They can’t cure all vulnerability issues. They must be implemented in addition to other security measures like security audits and penetration testing.
How machine learning handles network vulnerabilities?
Companies nowadays don’t wait for cyberattacks to occur. Instead, they adopt a practical approach using machine learning. One of the approaches to uncovering network vulnerabilities is penetration testing. It involves triggering a cyberattack to discover weak points in a company’s systems, networks, and firewalls. Machine learning adopts this approach and also applies code fixes, software patches, and other solutions to address holes if they exist in an organization’s security collection.
Challenges AI face in detecting threats in cybersecurity:
Despite being quite effective at detecting cyber threats and defending data, AI faces the following challenges in cybersecurity.
Infiltration of valuable data and malicious AI:
71% of malware-infected apps found on different app stores are still active there. This indicates that downloading such apps will invite cyber threats on your device. Smartphone malware can rob user data, undermine user privacy, and spy on other apps. It is recommended to employ Malicious AI to infiltrate data. It does so in the following ways:
- Scans social media to determine the correct people to target using spear-phishing campaigns
- Interprets CAPTCHAs to avoid this kind of authentication method
- Creates more compelling spam that is tailored towards the target victim
Use of low-quality data to train AI systems:
Safety assurance hugely depends on data quality. The misinterpreted inputs can compel AI systems to make serious mistakes. It is expensive to acquire high-quality data and train huge neural networks. Usually, external sources provide existing data. However, this can make interconnected AI mechanisms vulnerable to new perils.
To develop an AI-powered solution, you must use extensive and precise datasets. Developers require them to let algorithms learn but even to test them. If you want to prepare the datasets for a cybersecurity solution, it is necessary to find examples of malware, malicious code, and anomalies based on the task your solution is aimed to do.
Inconsistency in data privacy laws/policies/regulations
After being lured with the services, individuals usually reveal their sensitive information. Individuals have the option of complying with the data being shared. However, they can put them in a confusing scenario while providing their personal data for services they need.
Before developing an AI-powered cybersecurity product, you must assess different types of hacking undertakings. Keep in mind that the hackers also employ AI to let their attacks work smarter. It is difficult but necessary to invent ways to address such threats.
Your AI-powered solution can be vulnerable to cyberattacks. Some of the widespread attacks on these solutions include attempts to complicate the principal ML model and circumvent what the AI system is proposed to do.
Since AI is a complex technology, it faces barriers during adoption. You must invest time and money in researching this technology if you want to develop a cost-effective AI-driven solution. Thorough research is required if you want to find affordable AI-driven solution.
Make sure not to rush behind developing an AI-powered cybersecurity solution and instead evaluate all potential risks.
Use Cases of ML and AI in cybersecurity solutions:
ML and AI can effectively detect patterns, and cyber threats, and ultimately protect data. They process huge amounts of structured and unstructured data. By now, you have made yourself familiar with the benefits, applications, challenges, and opportunities of AI and ML in cybersecurity. It is also vital to know their use cases which are discussed below.
Discovery of code vulnerabilities:
The application developers and attackers both pursue code vulnerabilities. Whoever detects faster will win. One of the innovative approaches to hunting for risky flaws in code is employing ML and AL algorithms. They can rapidly scan huge amounts of code and identify known vulnerabilities before hackers detect and exploit them.
Although the approach of using AI and ML for flaw detection is not new, it still demands research. Some researchers automate the detection process of software vulnerability by employing hybrid neural networks. This approach is valid, but there are still certain limitations regarding the language used to write the code and the dearth of categorized vulnerability datasets.
Detection of malware and intrusions:
ML techniques are useful to augment signature-centered structures of malware detection. Cyber attackers usually make malware variants to prevent detection when the same malware is repeatedly used.
The traditional signature-centered methods can’t detect anonymous malware variants. On the other hand, behavior-based detection can analyze a file’s behavior and other related characteristics to conclude if it’s actually malware.
Enhancing conventional data scanning and analysis processes with ML capabilities allows a cybersecurity tool to process huge amounts of data. Hence, it speeds up analysis and efficiently identifies malware.
Prediction of data breach risks:
Advanced analytics (AA) enhances your cybersecurity solution. It refers to the autonomous data processing implemented with ML algorithms and AI techniques. This processing aims to make predictions, offer recommendations, and find deep correlations.
Advanced analytics has two categories i.e. prescriptive and predictive. Each of them offers unique outputs after they process big data. The human involvement in each is different.
Predictive analytics is used to analyze current and historical data to predict the events to expect. Prescriptive analytics is used to determine the actions to take to get expected outcomes. It predicts future events and recommends actions to take to obtain the best results.
When it comes to cyber threat detection, AI and ML are game-changers. Although ML & AI-enabled cybersecurity is still in its inception phase, it would create alerts for valid threats. These technologies can sift millions of files and recognize ones with potential hazards. So, they are used to discover cyber threats and mitigate them before they can cause havoc. Today cyber criminals develop sophisticated cyberattacks driven by ML and AI. So, cybersecurity professionals should use these same technologies to mitigate those attacks. The threat landscape is expected to grow continuously, so ML & AI-driven systems that are trained with high-quality data become inevitable.
Q1. Is AI a threat or advantage to cybersecurity?
A1. AI systems are hugely beneficial to cybersecurity teams of organizations. They help them defend their data and networks against emerging cyber threats in real-time. Since cyber criminals use the same tools to develop their attack vectors, it is vital to use them to prevent cyber threats.
Q2. What are the common threats that ML can handle?
A2. ML proactively provides defense for different threats like spam prevention, DDoS (distributed denial of service) mitigation, log analysis (to detect patterns and trends), vulnerability check, malware and intrusion detection, and botnet detection and containment.
Q3. Will machine learning be the future of cybersecurity?
A3. Machine learning offers advanced capabilities like policy recommendation, zero-day detection, automated anomaly detection, and identification and profiling. These capabilities help it drive modern cybersecurity solutions and thus prove to be the future of cybersecurity.
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