# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 3 Security Challenges and Mechanisms
### IoT on the Ground
#### 3.2.2 Extensions with Applications in Transportation
The scheme uses a probabilistic model for checking messages between intelligent vehicles and RSUs to find malicious RSUs. The scheme uses information over a period of time to create the model. The scheme then uses a generalized likelihood ratio test to find the RSUs that are malicious as well as check the integrity of messages. Through simulation testing, the scheme shows a slight drop in latency and the number of bits communicated. The scheme also offers a 99% probability of detection. * Intrusion Detection It is not always possible to make a system that is completely impervious to intrusion. That is why it is important to create a detection system that can find when a malicious actor has infiltrated a system. The authors of [156] propose a multitiered hybrid intrusion detection system. The system uses a signature to check the identity of actors in a system. The system also uses an anomaly detection algorithm to identify both known and unknown attacks in a system. Using testing, the scheme has proven to be a strong detection method against many different attacks and is capable of being implemented in real-time systems. Data vulnerability is something that is heavily focused on in the realm of the IoV. Controller area networks (CAN) are the most popular system implemented in vehicles today to allow for sensors in a single vehicle to work in conjunction. Many of these CAN implementations are not secure enough and are vulnerable to attacks, including DoS and Fuzzy attacks. In [157] the authors propose the use of deep learning techniques in intrusion detection to protect CAN. To detect malicious attacks the scheme is based on a VGG-16 architecture developed by a group of researchers at the University of Oxford in 2014. The scheme is trained on the CAN-intrusion dataset to train the deep learning algorithm on types of intrusion on CAN systems. Experimental testing has shown that 96% of intrusions are caught, and false positive rates are lowered in modern CAN intrusion detection systems. * Falsification Detection In IoV the ability for smart traffic routing is made possible by the ability of each vehicle to share its route. These can be useful in routing shipments, but issues arise in most systems because of a lack of storage. This issue can be made worse by the falsification of data being pushed to such a system and overloading the storage. The method proposed by the authors of [158] is a heuristic distributed scheme. The routes submitted by a user either penalize or reward them based on the contacts' confirmation. Through the use of a time-homogeneous semi-Markov process, the system can check the accuracy of mobility patterns and then submit them to the cloud server via RSUs. The cloud then has the capability of calculating whether a vehicle is malicious. Theoretical models and simulations show that the model can effectively identify falsified data. * Threat Hunting With IoV infrastructure becoming larger and more vast in devices, security threats are becoming extensively hard to predict. This leaves IoV open to a lot of different cyber-attacks. The authors of [159] investigate intelligent attacks on IoV and model the process of attack and defense through the Stackleberg game. The Stackleberg game allows the authors to minimize attacks while increasing defense. Through the model the authors were able to effectively create a defense model that was not influenced by what type of attack was performed, allowing for uniform defense. The solution proposed allows for balanced work and does not degrade the performance of the system. Internet of Autonomous Vehicles Improvements in video analytics in recent years have allowed for a tremendous amount of growth in the safety of autonomous vehicles. These improvements come with the added challenge of securely and reliably relaying video data. The authors of [160] propose a framework that implements both blockchain and multi-edge computing in autonomous vehicles. The use of these methods allows for a reduction in latency in the scheme.