AI Threat Detection

Every business, entity, or individual connected to the internet, a network, or reliant on data is at risk of being exploited by cybercriminals. You could say it comes with the territory.

In the ever-evolving world of cyber-security, security involves protecting systems, networks, and data from cyberattacks. Security experts must constantly be vigilant. There must be continuous study of known and emerging threats due to the ever-evolving tactics of cyber attackers.

There are three key aspects of cyber security; prevention, detection, and response. In this discussion, we’ll focus on threat detection and how the integration of Artificial Intelligence (AI) in cybersecurity aims to transform the way threats are being identified and mitigated.

In this article, we will explore how Artificial Intelligence (AI) enhances cybersecurity by improving threat detection and response.

Threat detection is the process of identifying unauthorized access to network data and resources whether from internal or external sources. The goal of threat detection methods is to identify and mitigate threats at their early stages, making it easy for security teams to minimize data loss and prevent damage to systems and processes.

AI-driven threat detection, however, is simply the use of machine learning algorithms systems and artificial intelligence to analyse patterns and spot abnormalities in network traffic. The use of traditional security measures alone is quickly becoming outdated due to the sophistication of cyberattacks making AI an important tool in the modern cybersecurity landscape.

These new systems identify anomalies and potential threats in real-time, making them faster and more accurate. The system also adapts to new attacks quickly when compared to traditional methods.

Examples of common cyber threats include; Advanced Persistent Threats, Insider Threats, Zero-Day Attacks, Encrypted Traffic, Expanding Surface Attacks, DDoS Attacks, and more.

These threats can go undetected for a long time and can be difficult to defend against. The Common traditional threat detection methods include;

Signature-Based Detection: This threat detection method is one of the oldest and most used methods for threat detection in cybersecurity. It relies on predefined patterns or "signatures” that are similar or match with known threats like malware or other network disruptions.

When a signature-based detection system finds a match between the data that is being observed and its database signatures it triggers an alert indicating a potential threat.

This method is employed by antivirus software and network intrusion detection systems (NIDS) to identify and block threats in real-time. It is quite limited in detecting new and unknown threats like zero-day attacks or advanced persistent threats but is more efficient in detecting threats that have been analysed and documented.

Anomaly-Based Detection: Anomaly-based detection was created as a method to address the limitations of signature-based detection. Unlike signature-based methods that rely on predefined patterns, anomaly-based detection involves creating a baseline of normal behaviour in a network by monitoring and analysing historical data.

The system continuously compares real-time activities with this baseline and flags any deviations as potential threats. It can also identify unknown or new threats by recognizing habit that deviates from the norms, making it good to detect zero-day exploits and attacks that do not have predefined signatures.

The challenge with anomaly-based detection lies in establishing "normal" behaviour in a constantly evolving network environment. Any deviation, whether malicious or not, can trigger false positives, which leads to alert fatigue.

Additionally, attackers can employ low-and-slow attacks that gradually compromise a network in a way that seems normal, making them difficult for anomaly-based detection systems to identify.

Heuristic-Based Detection: Heuristic-based detection uses algorithms to identify threats based on previously detected malicious behaviours. It involves evaluating files, code, or network traffic for suspicious patterns. Instead of strict signature matching, it has a broader scope, allowing it to detect new variants of known malware. However, false positives remain a challenge leading to unnecessary alerts. Regular updates ensure that heuristic-based detection stays ahead of emerging threats.

Now let’s explore the common threat detection tools designed to identify and mitigate cyber-attacks;

Intrusion Detection Systems (IDS): This tool monitors network traffic or system activities for anomalies in real-time. It works by analysing packets of data flowing through a network and comparing them against a database of known attack patterns. The two main types of IDS are; network-based (NIDS) and host-based (HIDS).

NIDS: Monitors network traffic at various points within the infrastructure e.g., Snort

HIDS: Monitors individual hosts or servers e.g., OSSEC

Security Information and Event Management (SIEM): These systems collect log data from various sources across an organization's network and infrastructure including firewalls, routers, servers, and applications, and then aggregate and correlate this data to identify potential security breaches.

SIEM systems employ vast analytics algorithms to detect malicious activities such as unauthorized attempts or abnormal system behaviour. They alert security teams in real-time allowing them to respond to these alerts quickly before they escalate. Example - CrowdStrike

Vulnerability Scanners: These systems identify weaknesses in a network system by regularly scanning networks, devices, and applications to uncover vulnerabilities such as outdated software, misconfiguration, etc. These allow security personnel to address security gaps before they are exploited. Example - Nessus

End Point Protection Platforms (EPP): Endpoints like laptops, desktops, and mobile devices are often the target of attackers seeking unauthorized access for data theft. EPP provides organizations with security solutions like antivirus firewall intrusion prevention systems to protect these points. They include features like data encryption to ensure every device's access to the network is protected. e.g., Symantec

Why Traditional Methods of Threat Detection May Fail Against Evolving and Sophisticated Cyber Attacks.

We cannot deny that traditional methods of threat detection have been used for years to prevent and mitigate risk, and are still very much significant to date.

However, these methods may fall short against evolving and sophisticated cyber-attacks because of the rapid advancement of technology, and the fast development of machine learning and artificial intelligence systems.

This advancement has fuelled more creative attack strategies. This includes deepfakes and the use of ransomware-as-a-service (RaaS). As a result, it has become essential to adopt a dynamic method to detect threats.

For organizations to have a strong cybersecurity posture they must implement better strategies to ensure that networks and systems are resilient against attacks protecting digital assets and preventing loss.

The issues with traditional threat detection include;

• High false positive rate which leads to alert fatigue amongst security personnel.

• Heavily relies on signatures, which often fail to detect new, or modified malware

• Resource-intensive processes that strain system resources and slow down response time

Traditional methods are integrated with machine learning (ML) and artificial intelligence (AI) as a solution to building a more resilient and robust defence.

Machine learning and other technologies like Artificial Neural Networks (ANNs), Deep Learning, Reinforcement Learning, and Big Data Analytics play an important role in AI-driven threat detection.

Let us briefly explore what these technologies are about;

Artificial Neural Networks (ANNs): These are considered the foundation of many AI systems as they are inspired by how the human brain works. ANNs can be trained on labelled (supervised learning) and unlabelled(unsupervised) learning data to identify anomalies that indicate potential threats.

They are effective in analysing large datasets and detecting complex patterns in network activities or human behaviour.

Deep Learning: This is a subset of machine learning that analyses large data by using multiple layers of artificial neural networks which recognize complex patterns, and excel in malware detection, phishing detection, etc. It can automatically extract meaningful information from raw data without requiring manual feature engineering, making it highly effective in AI-driven threat detection.

Big Data Analytics: Big Data analytics enables systems to process and analyse huge data from sources such as network logs, threat intelligence feeds, and user activity. AI threat detection systems can use big data to train models that can detect threats faster and more accurately.

Reinforcement Learning: Reinforcement learning is a type of machine learning where a system learns to make decisions by interacting with an environment and receives feedback in the form of rewards or penalties.

Reinforcement learning enables AI systems to improve decision-making through trial and error, and it can use these response strategies to decide what action to take when a threat is detected.

In other words, AI-driven threat detection employs reinforcement learning to help security systems adapt to cyber threats by continuously improving the approach they take in detection and response.

AI introduces a different approach to threat detection such as;

Machine Learning and Pattern Recognition: Machine learning algorithms can analyse large network traffic, system logs, and user behaviour by recognizing patterns to determine what a normal or abnormal activity is in real time.

As the ML model receives more data and is trained using these data it becomes better at distinguishing legitimate activities and suspicious ones. This provides more accurate detection of insider threats, malware, or other attacks.

Anomaly Detection Algorithms: Anomaly detection algorithms, such as time-series analysis, analyse system networks and user behaviour over time to establish a benchmark. If there's a difference or deviation from this baseline, they may indicate a security breach or attack. These could be abnormal login attempts or irregular file access patterns.

Natural Language Processing for Threat Detection: Natural language processing (NLP), a field of Machine Learning (ML), enables AI systems to understand and interpret human language. This field produces large language models(LLMs) that can detect human behaviour threats like phishing, and social engineering.

AI Automated Threat Hunting: AI-driven threat-hunting systems search for indicators of compromise (IoCs) across systems and networks. This automation process allows systems to identify potential threats without waiting for defined warnings.

Image and Video Analysis: Learning algorithms like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can be trained on images and videos to detect suspicious behaviour, unauthorized, and other security breaches. Face recognition models based on CNNs can be used to detect individuals attempting to gain access without the right authorization.

AI Real-Time Incident Response and Mitigation: AI can scan large amounts of data learning from the registered threats globally and it uses these data to recognize patterns and to predict and prevent attacks.

To enhance threat detection and response, traditional threat detection methods are being integrated with AI and Machine learning, resulting in hybrid models that combine the strengths of both approaches.

For instance, AI-driven systems can automatically update signature databases with newly discovered threats while machine learning algorithms can refine detection rules to identify anomalies more efficiently and accurately.

Machine Learning can evaluate large sets of data from various sources which can help organizations identify potential risks before they even form.

The future of cybersecurity includes;

• AI-enhanced detection: Detects threats much faster and accurately.

• Explainable AI (XAI): AI tools would provide explanations for the decisions they make for trust and transparency.

• Behavioural analysis with AI automation: The use of AI to observe patterns in user behaviour, detect anomalies, predict outcomes, and automate the process of decision-making to enhance customer data protection, etc.

These advancements will ensure that cybersecurity systems are adaptive, proactive, and efficient against evolving cyber threats.

Many tools leverage the power of AI to help systems stay alert, and ahead of cyber threats or attacks. Examples of these tools include;

SentinelOne: SentinelOne is an endpoint security tool designed to detect, prevent, and remove the spread of malware and other security threats.

DarkTrace: Darktrace is a cybersecurity solution that takes action against cyber threats in progress. It stops the spread of an attack in real time limiting damage.

Vectra AI: Vectra AI uses AI for hybrid attack detection, investigation and response.

CrowdStrike Falcon: Crowdstrike is an AI-powered cybersecurity platform that provides endpoint detection, response, and threat-intelligent functionalities.

Detection and timely response have been one major challenge as cyber attackers use advanced techniques like encryption and machine learning for attacks in recent times.

Traditional methods alone may not be efficient, especially against sophisticated attacks that are AI-driven which is why AI-powered threat detection is now the option for cybersecurity experts. The combination of Machine Learning [ML] with Artificial Intelligence [AI] can help organizations build robust and resilient defences for their systems and networks.