Cyber-adversaries are becoming more sophisticated in their efforts to avoid detection, and many modern malware tools are already incorporating new ways to bypass antivirus and other threat detection measures. Because networks and organizations use sophisticated methods to detect and respond to attacks, the response can be so strong that criminals try to respond with something even stronger. The complexity of cybercriminals is increasing, combined with the widening potential of artificial intelligence (AI) attacks.
Cybersecurity, however, is at a critical juncture, and the field must focus future research efforts on cyber-attack prediction systems that can anticipate critical scenarios and outcomes, rather than relying on defensive solutions and focusing on mitigation. Computer systems around the world need systems based on a comprehensive, predictive analysis of cyber threats.
Artificial intelligence (AI), which relies heavily on machine learning (ML), has the ability to recognize patterns arising from past experiences and make predictions based on them. In recent years, swarm technology, which can use things like machine learning and artificial intelligence to attack networks and devices, has shown new potential.
Useful patterns of attack can be defined by understanding patterns of behavior, analyzing patterns and connections between malicious activities, predicting future moves, and ultimately preventing or detecting potentially malicious behavior.
The aforementioned cyber-threat prediction systems offer promising and limited possibilities, but large-scale coordinated attacks require progress on several fronts, including the detection and prediction of events generated in computer systems. Obfuscation techniques are used to bypass detection by deliberately making malicious code difficult to understand in order to bypass the detection of the network.
When assessing network security risks, hackers’ behavior must be taken into account, which can be a daunting task, given the number of known vulnerabilities and the choices an attacker could make to infiltrate a network.
One of the recent researches used the data is fed into two deep-learning techniques that use sequential data to characterize cyber attacks. They also integrate information theory-based divergence measures to generate and refine hypothetical attacks on computers and networks.
Another research that is funded by NSF (National Science Foundation) aims to simulate scenarios for cyber-attacks based on renewed criminological theories of cybercriminals. The ASSERT/CASCADES project is evolving as we learn more about the ever-evolving techniques of cyberterrorism. The project has the ability to use observable malicious activity occurring on a network to predict upcoming attacks. It is expected that it will be possible to develop strategies to differentiate ongoing malicious activities and respond to upcoming critical threats before these events occur.
NEPAR is another project on attack pattern recognition where to extract data on the patterns of more than 1.5 million cyber attacks in the US and around the world. They took data from both public and private sources and discovered and used characteristics and patterns that were used in each attack. This predicted the likelihood of an attack on a particular system, such as a computer system or network, and predicted the likelihood of a successful attack on those particular systems.
Some organizations have already begun to perform statistical analyses of attacks using the MITRE framework for tactical sequencing. The Blue Team Defensive Game Book is used to predict tactics and map specific threats based on the Red Team’s opposing gamebook, which is created and updated from collected data and analyzed by the organization’s data collection and analysis tools, such as the Open Source Threat Assessment Toolkit (OSTAT). The defensive playbooks can then be developed to create reaction logs to identify attacks using cyber fingerprints.
Combining the two can allow a SOC (Security Operation Center) security team to get an accurate picture of what a phishing attack might look like and how employees can be alerted before they fall for the lure. When an opponent breaks through the network, attack strategies can be identified with the help of the Red Team Defensive Game Book.
In a lazy security environment, predicting the next attack is the only way to stay one step ahead of the disruption associated with a successful email security incident. Identifying anomalies and patterns of where the organization’s vulnerabilities lie and where attackers could strike next is a proactive and proactive approach. Those who are only trying to identify because of yesterday’s attack remain at greater risk.
It is believed that security researchers can use attack pattern recognition or detection methods as an approach that can provide precautions to prevent future attacks.