Can artificial intelligence detect cyber-attacks?

Introduction

Artificial Intelligence (AI) has emerged as a powerful tool in the field of cybersecurity, offering advanced methods for detecting and preventing cyber-attacks. Traditional cybersecurity measures often fall short in identifying new and sophisticated threats, making AI an essential component in modern cyber defense strategies. This synthesis explores the effectiveness of AI in detecting cyber-attacks based on insights from multiple research papers.

Key Insights

Effectiveness of AI in Intrusion Detection Systems (IDS)
- AI-based IDS can significantly improve the accuracy of detecting intrusions by analyzing network traffic and identifying anomalies .
- Neural network models, such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), are particularly effective in enhancing IDS performance .
AI in Malware Detection
- AI algorithms, including machine learning (ML) and deep learning (DL), are highly effective in identifying malware by analyzing software behavior and recognizing malicious patterns .
- AI can detect new and unknown malware variants that traditional signature-based methods fail to identify .
Predictive Threat Hunting and Real-Time Threat Detection
- AI enables predictive threat hunting by analyzing past attack data to identify potential future threats, allowing for proactive defense measures .
- Real-time threat detection is enhanced through AI's ability to evaluate vast amounts of data quickly, identifying trends and anomalies that indicate potential cyber-attacks .
AI in Vulnerability Analysis
- AI-based systems can analyze system configurations to identify potential vulnerabilities, improving the accuracy of vulnerability assessments.
- Neural network models can model the behavior of known vulnerabilities and detect new ones, providing a robust defense mechanism.
Automated Incident Response
- AI can automate incident response by evaluating data, identifying risks, and taking actions to contain or mitigate attacks, thereby minimizing damage and disruption.

Conclusion

AI has proven to be a highly effective tool in detecting and preventing cyber-attacks. Its applications in intrusion detection, malware detection, predictive threat hunting, real-time threat detection, vulnerability analysis, and automated incident response significantly enhance cybersecurity measures. By leveraging AI, organizations can better defend against evolving cyber threats and ensure the security of their networks and sensitive data.

Can artificial intelligence detect cyber-attacks?

Summary

Consensus Meter

Introduction

Key Insights

Conclusion

Sources and full results

Cyber Threat Detection Based on Artificial Neural Networks

Our AI-SIEM system using deep learning techniques outperforms traditional auto techniques in detecting cyber threats, aiding experts in quickly responding to attacks.

Artificial Intelligence Based Predictive Threat Hunting In The Field of Cyber Security

AI-based predictive threat hunting effectively detects new cyber threats by analyzing six stages of attack planning, execution, and input, improving cybersecurity.

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

AI methods, such as support vector machines and random forests, are effective in detecting cybersecurity attacks in IoT environments, with potential for future advancements.

Artificial Intelligence based Network Intrusion Detection with Hyper-Parameter Optimization Tuning on the Realistic Cyber Dataset CSE-CIC-IDS2018 using Cloud Computing

Artificial Neural Networks with hyper-parameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 provide 99.97% accurate botnet attack detection, improving cybersecurity.

Enhancing cybersecurity: The power of artificial intelligence in threat detection and prevention

Artificial intelligence enhances cybersecurity by detecting and preventing threats in real-time, allowing businesses to protect networks and sensitive data from evolving online threats.

Cyber Defence Based on Artificial Intelligence and Neural Network Model in Cybersecurity

AI and neural network models show significant potential in cybersecurity by improving intrusion detection, malware detection, and vulnerability analysis, but must be carefully considered due to potential challenges.

Artificial Intelligence in Cyber Security

Artificial Intelligence (AI) algorithms improve cybersecurity by detecting credit card fraud, spam, and reducing false positives, while increasing data processing speed and identifying hidden threats.

Review Study of the Impact of Artificial Intelligence on Cyber Security

Artificial intelligence can improve cyber security by analyzing data, preventing attacks, and enabling malware detection, reducing the strain on specialists and security firms.

Towards a Novel Intrusion Detection Architecture using Artificial Intelligence

Our novel intrusion detection architecture using AI and Random Forest algorithm effectively mitigates malicious traffic in critical infrastructures, offering intelligent self-protection and alert systems.

Cyber Security of Mobile Applications Using Artificial Intelligence

AI techniques, such as machine learning, deep learning, and artificial neural networks, can improve cybersecurity in mobile applications by detecting malware, preserving user privacy, and detecting complex attacks.

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