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.
What is Threat Detection?
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 The use of traditional security measures alone is quickly becoming outdated due to the sophistication of cyberattacks making AI an important tool in the modern cybersecurity landscape.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.
