Biblio

With all the exciting benefits of IoT in healthcare - from mobile applications to wearable and implantable health gadgets-it becomes prominent to ensure that patients, their medical data and the interactions to and from their medical devices are safe and secure. The security and privacy is being breached when the mobile applications are mishandled or tampered by the hackers by performing reverse engineering on the application leading to catastrophic consequences. To combat against these vulnerabilities, there is need to create an awareness of the potential risks of these devices and effective strategies are needed to be implemented to achieve a level of security defense. In this paper, the benefits of healthcare IoT system and the possible vulnerabilities that may result are presented. Also, we propose to develop solutions against these vulnerabilities by protecting mobile applications using obfuscation and return oriented programming techniques. These techniques convert an application into a form which makes difficult for an adversary to interpret or alter the code for illegitimate purpose. The mobile applications use keys to control communication with the implantable medical devices, which need to be protected as they are the critical component for securing communications. Therefore, we also propose access control schemes using white box encryption to make the keys undiscoverable to hackers.

Malware technology makes it difficult for malware analyst to detect same malware files with different obfuscation technique. In this paper we are trying to tackle that problem by analyzing the sequence of system call from an executable file. Malware files which actually are the same should have almost identical or at least a similar sequence of system calls. In this paper, we are going to create a model for each malware class consists of malwares from different families based on its sequence of system calls. Method/algorithm that's used in this paper is profile hidden markov model which is a very well-known tool in the biological informatics field for comparing DNA and protein sequences. Malware classes that we are going to build are trojan and worm class. Accuracy for these classes are pretty high, it's above 90% with also a high false positive rate around 37%.