Advanced Healthcare Systems. Группа авторов

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СКАЧАТЬ Identity Attack: In this, attacker can create false identity of authorized persons in the system and can do certain things like changing the entries and deleting some important records; attackers can also downgrade the system performance by consuming system resources unnecessarily.

      Data Tampering Attack: In this, attacker can tamper health-related data by changing, editing, manipulating, and destroying.

      Spamming: In this attack, fake data of patients is created and flooded in the system which induces unnecessary entry in data tables which leads to inaccurate results.

      Denial-of-Service Attack: In this, attacker creates a large number of fake packets to flood the network, and then, the system engages in fulfilling the request generated by the fake packets and denies the request generated by genuine packets. This results in poor system performance and uptime. Eavesdropping: In this, attackers take access of the communication channel and start snooping the packet traveling in that channel. If a very strong encryption technique is not applied, then it is very easy for attackers to read and understand those data.

      Location Privacy: In this, attacker can gain live location access of the patient, generated form the wearable IoT devices attached to the patient or the mobile phone.

      Usages Privacy: In this, attacker can gain usage information of the patient or the person involved in the healthcare to find the useful and predict some sensitive information.

2.5 Conclusion

      Technological advancement in the healthcare industry is increasing rapidly, and a variety of wearable devices are available for gathering health-related patient data. Data gathered from these smart devices are very huge.

The high volume of important data invites attackers to steal and manipulate it. To maintain integrity, security and privacy of health-related data. In this paper we have discussed architecture of the next generation healthcare system with latest available IoT devices, use of fog computing for local processing and storing, then for pattern recognition sends it to the cloud. Here, deep learning and data mining are done on that data. There are many security privacy issues, and challenges are there that need to be addressed very carefully.

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