Real-Time Malware Detection Using Machine Learning Algorithms

Volume no :

1 |

Issue no :

1

Article Type :

Research Article

Author :

Mr.Sidharth Sharma

Published Date :

2017

Publisher :

Journal STAR

Page No: 12 - 16

Abstract : With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection.

Keyword Real-time malware detection, machine learning, cybersecurity, anomaly detection, threat intelligence.

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RealTime Malware Detection MachineLearning: Enhancing Cybersecurity in the Digital Age

With the rapid growth of digital technologies, cyber threats such as malware have become increasingly sophisticated and difficult to detect. Traditional detection methods often struggle to identify new malware variants in time, resulting in compromised systems and data breaches. Therefore, RealTime Malware Detection MachineLearning approaches have emerged as essential tools in cybersecurity.

Machine learning (ML) offers significant advantages by analyzing behavioral patterns and large datasets to identify malicious activities in real time. Unlike signature-based methods, which rely on known malware patterns, ML models learn from data to recognize previously unseen threats. This adaptability makes real-time malware detection faster and more accurate.

Several machine learning techniques, including supervised learning, unsupervised learning, and deep learning, have been applied to improve detection capabilities. For example, recurrent neural networks (RNNs) can analyze sequential data, while convolutional neural networks (CNNs) are effective in extracting features from complex inputs. Together, these models enhance detection accuracy and reduce false positives.

However, implementing RealTime Malware Detection MachineLearning systems also presents challenges. Adversarial attacks can deceive models by introducing subtle changes to malware code, making it harder for algorithms to detect threats. Additionally, the computational overhead required for real-time processing can impact system performance. To address these issues, researchers are exploring ensemble learning, federated learning, and explainable AI techniques, which improve robustness and interpretability.

implementing RealTime Malware Detection MachineLearning systems also presents challenges. Adversarial attacks can deceive models by introducing subtle changes to malware code, making it harder for algorithms to detect threats. Additionally, the computational overhead required for real-time processing can impact system performance. To address these issues, researchers are exploring ensemble learning, federated learning, and explainable AI techniques, which improve

In conclusion, RealTime Malware Detection MachineLearning is a promising direction for strengthening cybersecurity defenses. By continuously evolving and adapting to emerging threats, these intelligent systems can protect computer networks, smart devices, and large-scale infrastructures more effectively than traditional methods. Future research will likely focus on overcoming current limitations and enhancing the scalability of these solutions.