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@article{179794,
        author = {Dasari Praneetha and Anish Tirumani and Chitturi Sowmya and Ch Pruthvinath Reddy and Dr M Rajeshwar},
        title = {malware detection using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7988-7993},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179794},
        abstract = {Intrusion detection is one of the significant 
security issues in the current cyber world. A large number 
of methods have been created which are machine 
learning based. So for detecting the intrusion we have 
created the machine learning algorithms. Using the 
algorithm we detect intrusion and we can detect the 
attacker’s information also. IDS are primarily two types: 
Host based and Network based. One host or device is 
monitored by a host-based intrusion detection system 
(HIDS), which alerts the user to any unusual activity, 
such as altering or removing a system file, making 
unnecessary system calls, or making unwelcome 
configuration changes. 
A Network based Intrusion Detection System (NIDS) is 
typically installed at network points like a gateway and 
routers to scan for intrusions in the network traffic. In 
this paper, KDD cup IDS dataset was downloaded from 
dataset repository. Then, we are required to implement the 
pre-processing methods. Then, we are required to 
implement the various machine and deep learning 
algorithms 
like 
Logistic 
regression 
(LR) and 
Convolutional Neural Network (CNN). The experimental 
results indicate that the accuracy of above mentioned 
algorithms. Then, we can deploy the project in web 
application using FLASK.},
        keywords = {malware detection, Machine Learning  Algorithms, Data Security, System Performance, Cyber  Threats, Threat Detection.},
        month = {May},
        }