7 Ways Cyber Experts Predict and Stop Attacks
By the end of 2025, cybercrime is projected to inflict $10.5 trillion in damages — a cost larger than the global drug trade and most national economies. Attacks are getting faster, stealthier, and more precise, driven by automation and AI. Yet, experts are not defenseless: today, seven predictive methods are giving cyber teams a fighting chance to spot attacks before they happen.
#1 Remote Viewing
Cyberattacks are becoming increasingly complex, and methods for predicting them are evolving alongside technology to respond to global instability. While many rely on traditional cybersecurity tools, there are also lesser-known approaches accessible only to a small circle of specialists.
One such method is remote viewing — a technique that requires practice but can potentially be applied by anyone who trains in it. From our experience, exploring remote viewing in depth shows that it may have promising applications even in cybersecurity, where it can act like a “mental shield”, helping to strengthen protection at the informational level.
To study this, our team used the iOS Verrevio remote viewing training app; the Android version has the same features.
The methodology proved surprisingly straightforward in practice, and noticeable results often appear after just a few months of consistent use. We recommend this tool to anyone interested in exploring innovative approaches to data protection.
Remote viewing is not just theoretical. Some practitioners have already made accurate predictions well in advance. For instance, Edward Riordan reportedly predicted a cyberattack on the Ukrainian mobile operator Kyivstar two years before it occurred, demonstrating the potential of this approach.
#2 Simulation and Cyber Range Testing
Born from military training concepts in the early 2000s, Simulation and Cyber Range Testing (SCRT) has become a cornerstone for preparing security teams. A “cyber range” is essentially a controlled, virtual battleground where real-world attack scenarios are simulated. Teams can practice responding to ransomware, phishing campaigns, or nation-state–style intrusions without putting actual systems at risk. This approach is especially valuable for government agencies, military units, critical infrastructure operators, and large enterprises with advanced security needs. By rehearsing in a safe environment, teams sharpen their skills, expose gaps in defenses, and build confidence to handle real attacks.
#3 Signature-Based Prediction
This approach works like a database of “wanted posters” for cyber threats. When an intrusion detection system (IDS) spots network activity matching one of these signatures — say, malware that always targets a specific server port in the same way — it raises an alert. Its strength is speed and accuracy against familiar threats. Its weakness is equally clear: anything new, slightly modified, or never-before-seen can pass right under the radar. That’s why, despite being one of the oldest and most widely used methods, it usually goes with other approaches for more complete protection.
#4 Anomaly-Based Detection
Anomaly-Based Detection (ABD) helps cybersecurity teams spot unusual activity instead of just known threats. It works by learning what “normal” looks like for a system and flagging anything that deviates — for example, a user logging in at odd hours or sudden spikes in network traffic. ABD became popular in the early 2000s as advanced persistent threats (APTs) started bypassing traditional defenses. Its strength is detecting new or unknown attacks, but it can also generate false alarms since not every unusual event is harmful. Today, ABD is often used alongside signature-based detection to combine speed with adaptability.
#5 Behavioral Analytics
A close one to the 4th method is Behavioral Analytics, more commonly known as UEBA (User and Entity Behavior Analytics). The difference is focus: rather than looking at the system as a whole, UEBA zooms in on users and entities such as devices, applications, or servers. By tracking normal patterns of behavior — how an employee typically accesses files, or how a server usually communicates with the network — it can detect subtle signs of insider threats or compromised accounts. Unlike older tools that rely on fixed rules, UEBA leverages machine learning to evolve with new attack techniques. It gained traction in the mid-2010s as organizations realized that some of the biggest risks came from within, not just from external hackers. Today, it’s widely used in enterprises, finance, and government sectors where insider risk is especially high.
#6 Machine Learning & AI-Based Prediction
While UEBA focuses on people and entities, AI-driven detection takes things to scale. Its real advantage is the ability to digest massive amounts of activity data across entire networks. By learning what “normal” looks like, AI can continuously scan for irregularities, surfacing potential threats in real time. What sets it apart is context: instead of flooding analysts with false alarms, AI engines cross-reference additional data points to separate real risks from background noise. For smaller or resource-strapped security teams, this efficiency is a game-changer, allowing them to punch far above their weight in terms of threat detection.
#7 Threat Intelligence & Predictive Risk Scoring
Finally, no predictive strategy is complete without Threat Intelligence (TI) and Predictive Risk Scoring (PRS). TI focuses on gathering and analyzing data about known and emerging threats — from dark web chatter to malware samples — while PRS builds on this by assigning numerical risk values to users, devices, or systems. For example, an employee logging in from an unusual location on a risky device may score higher, prompting closer monitoring. These methods became mainstream in the early 2010s, as signature-based defenses struggled to keep up with evolving threats.
