# 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 authors designed an algorithm called Fast Quartile Deviation Check (FQDC) to catch and locate an attack. This algorithm is based on entropy theory and a modified version of other deviation algorithms such as Quartile Deviation [133; 134]. Generalized Extreme Studentized Deviate (GESD) [135], Linear Regression and Confidence Interval. The authors modify the algorithm to have simple calculations, quick response, and low omission rates, to make it more applicable to IoV. When tested, the system detected all Sybil DDoS Attacks with an average alarm time of under 5 s. Another important area for attack resilience in a system is when considering vehicle-to-vehicle (V2V) communication. The authors of [136] propose a blockchain-based scheme to authenticate vehicles in real time. Since V2V communication must be in real time, the proposed scheme is free from low latency and heavy computation complexities through the use of blockchain technology. This technology creates blocks of information that are each hashed and shared with the corresponding vehicles. The hash function used in this scheme is SHA-1. Using the Pearson Correlation Coefficient, the correlation of communication rate is 0.9749 without an adversary and is calculated as 0.1282 when there is an adversary. This is performed while considering the Received Signal Strength Indicator. This proves that the scheme is an effective method of attack resilience. * Confidentiality Improvements in sensor-enabled vehicles have led to the increased need for physical layer security (PLS). With the growing density of vehicular networks, PLS will become significantly more important. The authors of [137] propose a PLS framework for a network consisting of a legitimate receiver and an eavesdropper. To capture the mobility of the communication channel, a double-Rayleigh fading channel is implemented. To measure the performance of the scheme average secrecy capacity and secrecy outage probability are presented. The two performance measures are then provided in alternative forms to be able to use them with a moment generating function. The tests show that the number of vehicles with signals that interfere will affect the system and that there is a correlation between the performance and the uncertainty of an eavesdropper's vehicle location. * Security Mechanisms
* Anomaly Detection Anomaly detection is the monitoring of data collected by a system to detect either false data or malicious activity. The use of SDN can be used to create systems that can help catch attackers that are targeting a specific node in the network. The authors of [138] propose a hybrid method that uses probabilistic data structures. The scheme is composed of four phases: (i) A monitoring scheme that takes advantage of Count-Min-Sketch as the probabilistic data structure that monitors and filters incoming traffic; (ii) A Bloom filter-based control scheme that is used to authenticate the nodes deemed suspicious; (iii) A quotient filter that is used to store malicious nodes; (iv) A hyperlog counter that measures the flow passing through switches to find malicious nodes with a high level of connection. Through experimental testing, the scheme has proven to be a strong candidate for anomaly detection when considering speed, accuracy, and efficiency in detection. Controller area network (CAN) bus anomaly detection is a form of detection specifically for attacks on the CAN bus protocol in certain vehicles. Many of the current CAN anomaly detection systems have poor performance rates. This tends to be from the lack of abnormal IoV data. The authors of [139] propose a method for message classification in IoV that establishes a weak model to classify redundant data. The method also allows for the classification of a broad range of data. The proposed method is able to decrease computational costs as well as time without taking away from the accuracy. The authors then propose an improved support vector domain description scheme. They add the Markov model and the Gaussian kernel function to reduce redundancies and false negatives.