Classification of advance malware for autonomous vehicles by using stochastic logic

Alsadat tabatabaei, S; Saraee, MH; Dehghantanha, A

Abstract

Connectivity of vehicles allows the seamless power of communication over the internet but is not without its cyber
risks. Many IoT communication systems - such as vehicle-to-vehicle or vehicle-to-roadside - may require latencies
below a few tens of milliseconds to cope with arbitrary and open-ended circumstances. These systems have
severely limited resources. An autonomous vehicle, for example, can generate more tens of megabytes of data per
second. Therefore, many resource-constrained IoT devices will rely on cloud services. Accordingly, the term
“cloud-to-things” was born. In such an interaction between IoT devices and cloud services, taking a system offline
for any reason - such as if a connected car were to be hacked - has significant consequences for the safety and
privacy of passengers and other citizens. These factors currently fall far outside what mainstream IT security
services can address: autonomous vehicles' safety systems should be able to withstand attacks and continue to function. To solve the issue, the operational requirements of safety features where close interactions between cyber systems and physical systems occur need to be carefully designed. The aim of this research is to propose an implementation of stochastic SVM and ANN classifiers. This approach gives a machine the ability to make predictive judgments about the effects of its actions as is shown in Fig.1. The system will train machine learning models both through both supervised and unsupervised algorithms. Next, by applying the cognitive intelligence to that system, the appropriate decisions on what to do about any detected situation will be performed.

Citation

Alsadat tabatabaei, S., Saraee, M., & Dehghantanha, A. (2018, September). Classification of advance malware for autonomous vehicles by using stochastic logic. Presented at 11th IEEE International Conference on Developments in eSystems Engineering DeSE2018, Cambridge, UK

• Presentation Conference Type: Other
• Conference Name: 11th IEEE International Conference on Developments in eSystems Engineering DeSE2018
• Conference Location: Cambridge, UK
• Start Date: Sep 3, 2018
• End Date: Sep 5, 2018
• Publication Date: Sep 5, 2018
• Deposit Date: Feb 4, 2019
• Publisher URL: http://dese.org.uk/wp-content/uploads/ProgrammeFull_DeSE2018_V7-1-1.pdf
• Related Public URLs: http://dese.org.uk/city-of-cambridge/
• Additional Information: Event Type : Conference