DefTesPY: Cyber defense model with enhanced data modeling and analysis for Tesla company via Python Language
<|reference_start|>DefTesPY: Cyber defense model with enhanced data modeling and analysis for Tesla company via Python Language: Several types of cyber-attacks on automobiles and business firms keep on rising as we are preparing to counter cybercrimes with several new technologies and defense models. Cyber defense (also, counter intelligence) is a computer network defense mechanism that involves response to activities, critical infrastructure protection, and information assurance for corporations, government bodies, and other conceivable networks. Cyber defense focuses on preventing, detecting, and responding to assaults or threats in a timely manner so that no infrastructure or information is compromised. With the increasing volume and complexity of cyber threats, most companies need cyber defense to protect sensitive information and assets. We can control attacker actions by utilizing firewalls at different levels, an intrusion detection system (IDS), with the intrusion prevention system (IPS) which can be installed independently or in combination with other protection approaches. Tesla is an American clean energy and automotive company in Austin, Texas, USA. The recent data breach at Tesla affected over 75,000 individuals as the company pinpoints two former employees as the offender revealing more than 23,000 internal files from 2015 to 2022. In this work, we will emphasize data modeling and data analysis using cyber defense model and python with a survey of the Tesla company. We have proposed a defense model, DefTesPY, with enhanced data modeling and data analysis based on the encountered cyber-attacks and cybercrimes for Tesla company till date.<|reference_end|>
@article{kshetri2024deftespy:,
    title={DefTesPY: Cyber defense model with enhanced data modeling and analysis
  for Tesla company via Python Language},
    author={Naresh Kshetri, Irin Sultana, Mir Mehedi Rahman, Darshana Shah},
    journal={arXiv preprint arXiv:2407.14671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14671},
    primaryClass={cs.CR}
}