Browsing by Author "Khan, H.K."

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    Hybrid cryptography for cloud computing
    (Institute of Electrical and Electronics Engineers Inc., 2021) Khan, H.K.; Pradhan, R.; Chandavarkar, B.R.
    In the present scenario, we come across millions and trillions of data in our daily lives which can be handled by a data center. Cloud is the data center that enables users to access the files and applications from almost any device and any geographical location as computing and storage take place on servers in the data center instead of the user device locally. It facilitates users with services like Software, Applications promptly without any hazard. Though the cloud has mesmerized the world with its advanced capabilities still there is safety concern involved in it because the cloud is shareable. All security components must ensure data security for every user. In this study report, a new security model using Hybrid Cryptography is designed as data in the cloud is vulnerable to issues like unauthorized data access, integrity violation, identity management, etc. Hybrid Cryptography comprises both symmetric and asymmetric algorithms. Encryption of communication requires Symmetric-key encryption whereas data exchange is taken care by the Public-key encryption technique. RSA algorithm deals with Authentication, blowfish algorithm ensures Data Confidentiality, and Secure Hash Algorithm-2 deals with Data Integrity. The present study concluded that advanced methods provide high data security over the internet and provide services on demand effectively without delay or error. © 2021 IEEE.
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    Malware Detection in Android Applications Using Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Khan, H.K.
    Android is a widely used smartphone operating system. Because of its open-source architecture, it is becoming increasingly important in our lives. Android applications are now commonly used in several devices like smartphones, smart tv, etc. Due to many different applications and fundamental features, users often trust Android to protect data. However, research has shown that Android is prone to security issues such as malware. Android malware detection is a hot research topic and requires immediate attention and resolution. This research examines the numerous factors of the Android Application Package (APK) and presents a machine learning-based model for detecting malware in Android applications. Experimental analysis of the proposed model using a standard dataset shows that it can be a viable solution in the future. © 2023 IEEE.