• Pinky Mishra
• Prof. Dr Shekhar Nigam

The detection of Android malware has become increasingly critical due to the widespread use of Android devices and the growing sophistication of malicious applications. This paper reviews state-of-the-art methodologies combining Convolutional Neural Networks (CNNs) and Genetic Algorithms (GAs) for malware detection. CNNs, known for their powerful feature extraction capabilities, are explored for their ability to identify patterns in malware behavior by analyzing application code, network activity, and permissions. Meanwhile, GAs, inspired by evolutionary principles, are used to optimize CNN architectures and parameters, improving detection accuracy and computational efficiency. This review provides a comprehensive analysis of various approaches integrating these techniques, discussing their strengths, limitations, and practical implications. Additionally, it highlights emerging trends, challenges in dynamic malware environments, and future directions for enhancing Android malware detection systems. The findings underscore the potential of CNNs and GAs as complementary tools in building robust and adaptive cybersecurity solutions

android malware detection; machine learning; genetic algorithm; feature selection; static analysis

"Android Malware Detection Using Convolutional Neural Networks and Genetic Algorithm: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.l402-l407, April-2025, Available :http://www.jetir.org/papers/JETIR2504B48.pdf

"Android Malware Detection Using Convolutional Neural Networks and Genetic Algorithm: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppl402-l407, April-2025, Available at : http://www.jetir.org/papers/JETIR2504B48.pdf