Deployment of Secure Data Parameters Between Stock Inverters and Interfaces Using Command-Contamination-Stealth Management System

• Published in: International journal of advanced computer science & applications 2024-01, Vol.15 (7)
• Main Authors: Henge, Santosh Kumar, Mandal, Sanjeev Kumar, Rane, Ameya Madhukar, Sharma, Megha, Singh, Ravleen, Kumar, S Anka Siva Phani, Marouthu, Anusha
• Format: Article
• Language: English
• Subjects: Assaults; College professors; Command modules; Computer science; Contamination; Cybersecurity; Data integration; Data integrity; Denial of service attacks; Education; Engineering; Flaw detection; Internet of Things; Inverters; Investments; Investors; Local area networks; Malware; Parameters; Social networks; Stockholders
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150750

The security issues more impact on stock data which allows the stockholders (SHs) and stock-inverters (SIs) to predict and invert false assets and stock values. Because of the security flaws and threads that let an attacker take over network devices, the attacker uses the system to attack another system. These problems have an even greater influence on stock data, which gives stockholders (SHs) and stock-inverters (SIs) the ability to forecast and reverse fictitious assets and stock values. This study suggests test scenarios regulate different BOTNETs, layered threshold-influenced data security parameters, and DDoS vulnerabilities for stock data integration and validation. In order to study the behavioral entry and exit sites of SHs and SIs, it has integrated three-tiered procedures with threshold-impacted data security criteria and data matrices. Role Management (RM), Remote Level of Command Executions (RLCE), LAN-WAN-LAN Transmission (LWL-T), and Detection of Conceal and Prevention (DoCP) environments are the frameworks of the first layer. The RM, RLCE, LWL-T and DoCP are tuned with threshold-influenced data security parameters which are more influencing stock values. The second layer is framed with Module Management (MM), Command Module (ComM), Contamination Module (ConM), and Stealth Module (SM). The third layer is framed with expected scenarios and threshold of various vulnerabilities, a thread which occurs based on DoS and BOTNETs. All these layers are interconnected together and integrated with behavioral factors of SHs and SIs. The vulnerabilities are tuned with SHs and SIs input data, then filtered with SHs and SIs behavioral matrices, the alerts has been generated according to their existing entries of the data. These influenced threshold metrics tuned through ARIMA and LSTM for future analysis of stock values. The authentication mode has synchronized dual and multi authentication mode of execution, which tuned to cross verify the investors credentials.