The Use of AI in Detecting and Preventing Cybercrime

The Use of AI in Detecting and Preventing Cybercrime
Photo source: Shutterstock/Blue Planet Studio
Table of Contents

As technology plays an increasingly influential role in our lives, the threat of cybercrime poses a significant challenge to individuals, businesses, and governments worldwide.

With cybercriminals becoming increasingly refined, conventional cybersecurity measures struggle to keep up. However, the emergence of artificial intelligence (AI) has opened up new avenues for developing and implementing AI-based cybersecurity technologies and systems to combat cybercrime.

According to a report by Acumen Research and Consulting, the demand for AI-based security products is on the rise, with the market estimated to reach $133.8 billion by 2030.

In this article, we will examine the use of AI in cybercrime, exploring both the threats it poses and the opportunities it presents for improving cybersecurity. 

What is Cybercrime?

Cybercrime, also known as computer crime, is a type of illegal activity involving computer use. Its primary impact is financial, with cybercriminals resorting to tactics like ransomware attacks, internet and email scams, and identity theft to achieve their goals.

However, cybercrime can also involve spreading illegal information, malware, images, or other harmful materials, which can damage or disable computers and devices.

Initially, cybercrime was mainly an American problem due to theearly adoption of computers and the internet. However, by the 21st century, cybercrime had become a global issue affecting communities worldwide.

Video source: YouTube/Tech Might

The US Department of Justice categorizes cybercrime into three types:

  • Crimes that target computing devices with the intent of gaining unauthorized access to the network.
  • Crimes that use computers as a weapon to launch attacks, such as Denial of Service (DoS) attacks.
  • Crimes that use computers as an accessory to store and transfer illegally obtained data.

The Council of Europe Convention on Cybercrime has defined cybercrime as offenses against confidentiality, integrity, and availability of computer data and systems, computer-related offenses, and content-related offenses, including copyright infringement and data interception.

The internet’s convenience, anonymity, and borderless nature have made cybercrime more prevalent, ranging from individuals with limited technical knowledge to global criminal organizations with skilled developers.

As a result, detecting and prosecuting cybercriminals, particularly those in countries with weak cybercrime laws, has become challenging.

What is AI in Cybersecurity?

Artificial intelligence, specifically machine learning (ML), can play a significant role in discovering insights from data. ML automates the process of finding, contextualizing, and prioritizing relevant data in the threat intelligence lifecycle.

This includes detecting suspicious network activity and finding dark web forum posts indicating a data breach. By utilizing machine learning in various domains of cybersecurity, security processes can be enhanced, and security analysts can quickly identify, prioritize, deal with and remediate new attacks.

This can help develop defense responses and better understand previous cyber-attacks.

Utilizing machine learning in various domains of cybersecurity can enhance security processes, enabling security analysts to quickly identify, prioritize, deal with and remediate new attacks. This can help develop defense responses and better understand previous cyberattacks.

Unlike traditional signature-based systems, AI-based systems use ML algorithms to scrutinize enormous amounts of data, recognize patterns, and detect anomalies that could indicate a potential cyberattack.

AI can quickly learn and improve its protection, enabling it to detect and adapt to new and unknown threats. 

How is AI Used to Tackle Cybercrime?

In the world of cybersecurity, AI is frequently used to distinguish “good” entities from “bad”. AI-powered security systems offer real-time alerts to potential threats and continuously monitor networks, devices, and applications, removing human delay and response time.

AI’s ability to “learn” from previous behavior allows for rapid, actionable insights when confronted with new or unfamiliar information or behaviors. It can make logical inferences based on potentially inadequate data subsets and provide several solutions to a known problem, allowing security teams to choose the best course of action.

As traditional security systems prove slow and ineffective against a growing variety of complex cyberattacks, AI is proving to be a dependable alternative. Its use is helping businesses improve their cybersecurity position and better protect themselves from the ever-evolving threat landscape. 

The Use of AI in Detecting Cybercrime

Sophisticated algorithms are being used to train AI systems to detect malware and recognize even the minutest behaviors of ransomware attacks and run pattern recognition before they infiltrate a system.

AI allows for superior predictive intelligence, leveraging natural language processing to curate data on cyber threats by scraping through articles, news, and studies. This gives cybersecurity teams a better understanding of new anomalies, cyberattacks, and prevention strategies, as cybercriminals constantly follow trends.

Cybersecurity systems powered by AI offer the latest information on both global and industry-specific threats. This enables organizations to make more informed decisions when it comes to prioritizing their defenses against potential attacks.

As an example, bots make up a substantial portion of internet traffic and can present substantial risks, such as account takeovers and data fraud.

However, AI and machine learning can help organizations gain a deeper understanding of website traffic and differentiate between good bots, bad bots, and human users.

By analyzing behavioral patterns, businesses can stay ahead of the bad bots and identify unusual traffic patterns that may indicate a security threat.

Examples of cybersecurity detection systems include:

  • Amazon GuardDuty, an AI-based threat detection service that uses machine learning to analyze AWS logs and identify potential security threats in real-time.
  • IBM Watson for Cybersecurity, another powerful AI-based threat detection system that analyzes security data from multiple sources, such as logs and security alerts. This system can identify threats that traditional security systems may have missed.
  • CylancePROTECT is an AI-based endpoint security solution that uses machine learning to detect and prevent cyber threats. Its predictive model can identify and block malicious files and processes before they can execute on an endpoint.
  • Splunk User Behavior Analytics is an AI-based cybersecurity solution that uses machine learning to identify and respond to anomalous behavior on a network. This system can detect insider threats, such as employees accessing sensitive data outside of their normal behavior patterns. 

The Use of AI in Preventing Cyberattacks

AI is very effective in preventing cyberattacks, especially in stopping spam and phishing. For example, AI-powered cyber threat hunting solutions, such as Darktrace for Email and DNSFilter use AI to detect and block phishing attempts and categorize and block websites based on DNS queries.

Netskope and SentinelOne use AI to scan network traffic and identify malware, and to predict where organizations are most likely to be breached.

AI can also improve authentication processes, ensuring that users’ identities are verified throughout their session. OneLogin’s AI-powered SmartFactor Authentication verifies login attempts in real-time and prompts users to verify their identity if there are suspicious actions.

Finally, AI can predict breaches, allowing organizations to allocate resources effectively to improve their cyber resilience.

All these show that the use of AI in preventing cyberattacks is crucial in the fight against cybercrime, enabling organizations to respond proactively and protect their systems and data.

However, it is important to note that AI is not a magic solution for cybersecurity. Cybercriminals can also harness the power of AI to launch more advanced and sophisticated attacks.

Likewise, AI-based systems are also not immune to attacks themselves. Thus, designing, testing, and appropriately maintaining AI-based cybersecurity systems is crucial to prevent misuse.

AI Tools Used to Combat Cybercrime

AI tools used in cybersecurity are constantly evolving, and many professionals are enhancing existing AI-based cybersecurity technologies and systems to stay ahead of emerging threats.

Video source: YouTube/cyber fellow

Here are some examples of AI tools that combat cybercrime:

  1. CyberSecTK: This python library bridges the gap between techniques and cybersecurity, providing a suite of program data sets, modules,  and tutorials supporting research and teaching in cybersecurity.
  2. Sophos Intercept X: Intercept X utilizes a deep learning neural network to conduct deep analysis and determine if a file is safe or malicious in 20 milliseconds before it executes, resulting in highly accurate and zero-day malware with a lower false positive rate.
  3. Vectra’s Cognito: Cognito detects and responds to attacks inside the cloud, data center, IoT, and enterprise networks. It uses behavioral detection algorithms by collecting network metadata and logs to expose hidden attackers in workloads and IoT devices.
  4. Targeted attack analytics (TAA) by Symantec: The cloud-based analytics in TAA automatically adapts to new attack tactics by delivering continuous attack detections and adding new attack analytics.
  5. BioHAIFCS: Bio-inspired Hybrid Artificial Intelligence Framework for Cyber Security uses ML approaches suited for protecting critical network applications and comes with a Hybrid Evolving Spiking Anomaly Detection Model (HESADM) and the Evolutionary Prevention System from SQL injection attacks.
  6. StringSifter: This ML tool ranks strings automatically based on their relevance for malware examination, providing an output sorted by relevance for malware analysis.
  7. DefPloreX: This ML toolkit uses machine learning and visualization techniques to analyze millions of defaced web pages, turning unstructured data into meaningful high-level descriptions.
  8. IBM QRadar Advisor: QRadar Advisor uses IBM Watson technology to protect against cyberattacks by auto-investigating indicators of compromise or exploitation.
  9. Tessian: Tessian’s ML-powered email filters detect and eradicate suspicious in and outbound activity, providing a single dashboard for real-time monitoring.

As quantum computing continues to develop, it is expected to further enhance the capabilities of AI-based cybersecurity tools, providing even more effective protection against cyberattacks. 

Risks and Limitations of AI in Cybersecurity

Using AI and ML in cybersecurity can be a double-edged sword. On the one hand, these technologies can benefit businesses by analyzing vast amounts of data and making predictions.

However, on the other hand, the same features that make them so helpful can also make them attractive targets for cybercriminals. 

1. The Menace of Deepfakes

One of the most concerning abuses of AI technology is the creation of deepfakes, which can manipulate audio and visual content to produce false but seemingly authentic media.

This makes them a perfect tool for disinformation campaigns and can be used by cybercriminals to destabilize governments or extract funds from companies.

In addition to deepfakes, cybercriminals also use AI to improve their AI algorithms and techniques in areas such as password guessing, human impersonation on social media, and hacking vulnerable hosts. 

2. The Dangers of AI in the Hands of Hackers

AI software is adept at highlighting unusual activity, but it is up to skilled staff members to use this intelligently to focus their efforts on fighting cybercrime. It is important to note that AI also allows hackers to launch cyberattacks on a larger scale.

AI software is increasingly available to all, and criminals can learn how these programs work to adjust their attacks to avoid detection. Using AI to disguise malware is also possible, leading to potential breaches. 

3. AI Weaknesses

Furthermore, AI struggles with smaller amounts of data. The process by which AI acquires knowledge and adjusts to new situations involves analyzing vast quantities of data to identify anomalies and patterns.

Furthermore, AI struggles with smaller amounts of data. AI learns and adapts by analyzing large amounts of data to spot patterns and anomalies.

The ability of AI software to detect abnormal behavior is limited when there is insufficient data to learn from.

The Power of Human and AI Collaboration

To achieve optimal results, it is crucial to adopt a comprehensive strategy that combines the expertise of human analysts with the capabilities of AI software, thus creating a synergistic effect.

By assigning time-consuming tasks related to low-level security risks to software, skilled personnel can devote their attention to security aspects that demand a human touch.

To effectively protect against cybercrime, it is crucial to establish a strong collaboration between AI and experienced personnel.

This highlights the need to consider the risks and limitations of AI in cybersecurity as we work to develop effective strategies to prevent cybercrime.

To mitigate these risks, it is crucial to implement a zero-trust security model, which assumes that all users and devices are potentially compromised and require continuous authentication and authorization to limit potential damage from such imperils. 

The Growing Importance of AI in Cybersecurity

Using AI and ML technologies can help organizations better protect themselves from cyberattacks. Although, it is essential to remain vigilant against advanced attacks.

According to experts, one key element of cybersecurity is an early recognition system that can identify attacks immediately.

However, cyberattacks become more elaborate as AI and ML become more advanced. In other words, finding a flexible risk framework that aligns with legislation and insurance policies is critical for a sustainable solution.

As more businesses undergo digital transformation and more devices such as the Internet of Things (IoT) come online, the risk of cybercrime will increase, and cybersecurity will become even more critical.

Furthermore, the rise of digital transformation also increases the opportunity for cybercrime, which makes cybersecurity even more critical.

It is important to note that security professionals and cybercriminals will continue using AI and ML as tools in their efforts. However, by combining these technologies with skilled personnel, organizations can effectively guard against cybercrime. 

AI in Cybercrime: Key Takeaways

As AI and ML continue to play a critical role in cybersecurity, cybercriminals also leverage these technologies to carry out attacks more efficiently. 

From deepfakes to password guessing, they are finding new ways to evade traditional security measures.

To stay protected, it is crucial to develop a comprehensive approach combining AI’s power with human expertise to combat these threats effectively. With AI-based systems, companies can detect potential vulnerabilities and adapt to new and unknown threats.

Have you taken the necessary steps to establish a comprehensive cybersecurity strategy?

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.