AI and machine learning in Cybersecurity – Enhancing Threat Detection
In the digital era, rapid advancement in technology brings immense opportunities as well as daunting challenges. One of the key challenges is Cyber Security as the frequency and sophistication of cyber-crimes are increasing in an unstoppable manner with the adoption of digital technologies in our daily routine. Artificial intelligence and machine learning techniques have shown tremendous potential in improving threat detection and strengthening defense mechanisms. This blog aims to highlight the importance of artificial intelligence and machine learning techniques in the domain of cyber security, particularly in the area of threat detection.
Increase in Cyber Threat Landscape
Cyber Threats are getting more and more complex as cybercriminals employ advanced tools and techniques such as automated code generators, code obfuscators, polymorphism, metamorphism and so on. Conventional security mechanisms rely on signatures or a predefined set of rules and often fail to detect novel and complex threats. Consequently, artificial intelligence (AI) and machine learning (ML) techniques can play a pivotal role in detecting such types of threats with minimal human intervention.
Role of AI and ML in enhancing Threat Detection
AI and ML techniques possess the potential to learn from massive amounts of data and retrieve valuable insights that are usually hidden from human eyes. Some of the applications of AI and ML techniques in the area of cyber security are listed as follows:
- Threat Detection: Threat detection is one of the fundamental applications of ML algorithms. ML algorithms can easily label new samples as either normal or threat by learning behavioral patterns of benign and malicious samples. Further, ML algorithms possess the potential to classify threats into respective categories such as trojan, worm, backdoor, botnet and so on. This process minimizes human intervention and saves human resources. Additionally, it facilitates the detection of many threats within a short period. This capability is specifically advantageous in mitigating or controlling the impact of malware particularly in case of ransomware-related threats.
- Predictive Analysis: ML algorithms analyze historical data to identify trends and retrieve hidden insights. This analysis leverages characteristics of existing threats to detect zero-day, novel or unseen threats with high accuracy and precision. Further, it facilitates security researchers to mitigate them in the early stages to minimize risk and enhance security in today’s cyber world.
- Multiple dataset analysis: Modern threats include various techniques such as code obfuscation, and junk code insertion to evade existing threat detection mechanisms. ML algorithms can quickly analyze multiple datasets of malware to learn the behavior of different types of known malware and understand different strategies used by malware writers. This step allows them to gain in-depth understanding of malware and detect malware in flawless manner.
Application areas:
Many industries/ organizations have integrated AI and ML techniques in their cybersecurity domains:
- Financial firms: Banking sector and other financial firms adopt AI and ML techniques to detect any fraudulent transactions and avoid monetary loss for users and reputation loss for financial organizations.
- Healthcare organizations: Healthcare uses AI techniques to secure sensitive details/records of patients such as data related to their diseases, insurance, etc. AI monitors the regular access patterns and generates alarms in case of unusual/unauthorized attempts to access the records.
- Automobile sector: With the integration of AI in Cyber Security, automobile companies can certainly increase the protection of their vehicles and safeguard their customers. Additionally, AI can quickly identify the breach in the security systems and trigger recovery protocols to reduce the amount of damage.
Critical issues and considerations:
Although AI and ML techniques offer several benefits in the realm of cybersecurity, it is important to mention the unique challenges that come along with these benefits:
Quality of Input data: The performance of any AI/ML model depends on the quality of the dataset used for training purposes. Poor or insufficient data to train the model often leads to inaccurate detection of threats.
Adversarial attacks: AI and ML algorithms are vulnerable to adversarial attacks. Cybercriminals often feed incorrect, irrelevant and redundant data in training sets to mislead the algorithms. Hence, security researchers must devise new mechanisms and techniques that can effectively handle such attacks.
Privacy-related concerns: The application of AI in monitoring user behavior often raises privacy-related concerns, especially in the banking and healthcare sectors. Therefore, it is important to maintain a balance w.r.t security and privacy of users.
It is clear from the above discussion that AI, ML and Cyber Security work in conjunction to deal with the threat landscape. The NorthCap University in NCR recognizes that AI and ML techniques can transform cyber security, particularly in case of threat detection and hence offers threat detection as part of its B.Tech Computer Science program. Faculty at the university pursue their research in threat detection by leveraging the strengths of AI and ML techniques and encourage their students to pursue projects in this emerging domain.
Authored By
Dr. Prachi
Associate Professor
Research Areas: Cyber Security, Threat Detection, Digital Forensics and Machine Learning
The NorthCap University