International Security Journal hears from Tannu Jiwnani, Principal Security Engineer at Microsoft about the importance of having efficient cybersecurity accuracy.
False positives have become an increasingly pressing issue for cybersecurity professionals. As security tools grow more sophisticated, false positives instances where benign activities are incorrectly flagged as malicious are also becoming more common.
These erroneous alerts not only create a significant operational burden but also undermine the trust security professionals place in their detection systems.
Cybersecurity teams can waste valuable time and resources investigating these alerts, sometimes even overlooking genuine threats in the process
The problem is particularly acute in modern cybersecurity environments where the scale of data is enormous and threats are increasingly difficult to identify.
For example, Advanced Persistent Threats (APTs) and polymorphic malware, which can evolve and change their characteristics over time, can easily be mistaken for benign activities, resulting in false positives.
As the sophistication of threats grows, security tools must adapt to accurately identify and mitigate potential risks without overwhelming analysts with alerts that do not pose a real danger.
A false positive in cybersecurity occurs when a security system incorrectly flags benign activity as malicious.
These alerts typically arise in intrusion detection systems (IDS), firewalls, antivirus software and endpoint protection tools.
While these systems are designed to identify and prevent potential threats, the volume and complexity of modern network traffic mean that many security tools struggle to differentiate between malicious and benign activity.
False positives can stem from various factors, including overly broad detection rules, lack of contextual understanding and reliance on outdated or incomplete threat intelligence.
For instance, a new software installation could be flagged by an IDS because it is a new executable file with unfamiliar characteristics, even though the installation is entirely legitimate.
Similarly, large data transfers or unusual access patterns could be misidentified as malicious attempts at data exfiltration.
The challenge of false positives has escalated as threats have become more complex. Cyber-adversaries are constantly evolving their techniques to evade detection.
Polymorphic malware, for example, changes its code each time it infects a new system, making it harder for signature-based systems to identify it as malicious.
Similarly, techniques like “living off the land,” where attackers use legitimate administrative tools to carry out malicious activities, can trigger false positives in systems designed to flag abnormal behaviour.
The sheer volume of data generated by modern networks further complicates the detection process.
Security systems must sift through enormous amounts of data in real-time, increasing the likelihood that benign activities will be misclassified as threats.
As the amount of network traffic grows, the challenge of identifying false positives intensifies and organisations are increasingly overwhelmed with unnecessary alerts that drain resources and attention.
The impact of false positives on cybersecurity operations can be significant. The primary consequences include operational inefficiencies, increased workload for security teams and the erosion of trust in security systems.
One of the most significant challenges associated with false positives is alert fatigue.
As security teams are bombarded with numerous false alarms, it becomes increasingly difficult for them to prioritise genuine threats.
Over time, analysts may begin to overlook or dismiss alerts, either because they are overwhelmed by the sheer number of them or because they have become accustomed to treating them as non-critical.
Alert fatigue can lead to delayed incident response times, which can be catastrophic in environments where real-time action is essential to preventing or mitigating attacks.
The lack of focus on genuine threats increases the risk of a breach or other forms of security compromise.
Investigating false positives consumes valuable resources, including time, personnel and computing power.
Security analysts must examine each alert in detail to determine whether it is legitimate or a false alarm.
This process can be time-consuming and may involve gathering additional context, reviewing logs and consulting with other team members.
Moreover, many security systems require significant computing power to process the large volume of alerts generated by network traffic.
This computational burden can lead to inefficiencies and potentially slow down the entire system, making it harder for teams to focus on critical incidents.
When security professionals encounter a high rate of false positives, they may begin to lose trust in their detection systems.
This lack of trust can lead to reduced reliance on automated alerts, potentially causing analysts to miss important indicators of compromise.
In some cases, security teams may resort to manual detection methods, which are slower and less scalable, further exacerbating operational inefficiencies.
There are several technological approaches to mitigating false positives in cybersecurity. These solutions aim to improve detection accuracy and reduce the number of benign activities incorrectly flagged as threats.
Machine learning (ML) and artificial intelligence (AI) are transforming the way cybersecurity tools handle false positives. By leveraging algorithms that learn from historical data, these technologies can refine detection models over time, improving their ability to distinguish between legitimate and malicious activities.
ML systems are particularly effective in recognising patterns in large datasets, enabling them to identify potential threats that may have been missed by traditional detection methods.
For example, AI-powered security tools can analyse vast amounts of network traffic and detect subtle, previously unknown attack patterns that traditional systems might overlook.
These tools can be trained to improve detection accuracy over time by learning from previous false positives and adapting their models accordingly.
User behaviour analytics (UBA) involves monitoring and analysing user behaviour to detect anomalies that may indicate malicious activity.
By establishing baseline behaviour patterns for individual users, UBA systems can identify when a user’s actions deviate significantly from the norm, triggering alerts for further investigation.
UBA systems are particularly effective at reducing false positives in environments where legitimate user behaviour can sometimes appear suspicious.
For example, a user who regularly accesses sensitive files may not raise any flags, but if they suddenly access an unusually large number of files or perform tasks outside their normal behaviour pattern, a UBA system can flag the activity as suspicious without generating false alerts for routine behaviour.
Incorporating contextual awareness into detection systems can significantly reduce false positives. For instance, the time, location and frequency of certain activities can provide valuable context that helps determine whether an event is legitimate or malicious.
By correlating this contextual information with threat intelligence, cybersecurity tools can make more informed decisions about which alerts to prioritise.
For example, if a user logs in from an unusual location but has previously authorised international travel, this context can prevent the alert from being flagged as a false positive.
Similarly, correlating login activity with other factors, such as the time of day and the user’s typical access patterns, can provide additional insight into whether the event is malicious.
While machine learning and UBA systems are powerful, traditional heuristic and signature-based detection systems still play a crucial role in identifying threats.
These methods can be refined by continuously updating detection rules and signatures to minimise false positives.
For example, signature-based systems can be updated to account for new types of attacks, reducing the likelihood of benign activity being misclassified as a threat.
Heuristic methods, which are based on predefined rules or behaviours associated with known attacks, can also be adjusted to improve accuracy.
By fine-tuning the heuristics and signatures used by detection systems, cybersecurity teams can reduce the number of false positives without sacrificing detection quality.
While technological solutions play a vital role in reducing false positives, cybersecurity teams must also adopt best practices to optimise their response and minimise inefficiencies.
The best practices for cybersecurity teams include: