The detection and prevention of cyber-attacks is one of the main challenges in Vehicle-to-Everything (V2X) autonomous platooning scenarios. A key tool in this activity is the measurement report that is generated by User Equipment (UE), containing received signal strength and location information. Such data is effective in techniques to detect Rogue Base Stations (RBS) or Subscription Permanent Identifier SUPI/5G-GUTI catchers. An undetected RBS could result in unwanted consequences such as Denial of Service (DoS) attacks and subscriber privacy attacks on the network and UE. Motivated by this, this paper presents the novel simulation of a 5G cellular system to generate a realistic dataset of signal strength measurements that can later be used in the development of techniques to identify and prevent RBS interventions. The results show that the tool can create a large dataset of realistic measurement reports which can be used to develop and validate RBS detection techniques.

The rapid adoption of Internet of Things (IoT) devices in smart homes has introduced security vulnerabilities, with Distributed Denial of Service (DDoS) emerging as a critical threat. Exploiting the often-unsecured nature of these interconnected devices, such attacks overwhelm network resources, causing severe disruptions and privacy breaches. We present a novel anomaly detection system for early-stage DDoS attack identification in smart home IoT environments. Using NS-3 simulator, a realistic IoT network dataset was generated, capturing normal and malicious traffic. Key traffic features, e.g., packet size and inter-arrival times, were extracted to train two lightweight Machine Learning (ML) models: One-Class Support Vector Machine (OCSVM) and Isolation Forest (IF). OCSVM model achieved superior performance with accuracies from 96% to $99 \%$ for various attacks, while the IF model performed marginally worse. We offer a lightweight and scalable solution for real-time deployment in resource-constrained IoT environments, a significant step to enhance smart home security.

Rogue Base Stations (RBS), also known as 5G Subscription Concealed Identifier (SUCI) catchers, were initially developed to maliciously intercept subscribers’ identities. Since then, further advances have been made, not only in RBSs, but also in communication network security. The identification and prevention of RBSs in Fifth Generation (5G) networks are among the main security challenges for users and network infrastructure. The security architecture group in 3GPP clarified that the radio configuration information received from user equipment could contain fingerprints of the RBS. This information is periodically included in the measurement report generated by the user equipment to report location information and Received Signal Strength (RSS) measurements for the strongest base stations. The motivation in this work, then is to generate 5G measurement reports to provide a large and realistic dataset of radio information and RSS measurements for an autonomous vehicle driving along various sections of a road. These simulated measurement reports can then be used to develop and test new methods for identifying an RBS and taking mitigating actions. The proposed approach can generate 20 min of synthetic drive test data in 15 s, which is 80 times faster than real time.

The 5G network has improved Rogue Base Stations (RBS) detection by concealing subscription permanent identifiers, and other security mechanisms passed down from legacy network generations to 5G. However, even with these enhancements, 5G is still vulnerable to RBS threats. A malicious actor can broadcast a signal of sufficient power to impersonate a legitimate base station, thereby deceiving the user devices into connecting to the rogue and potentially compromising confidential info. This work designs and implements a novel zero-trust-based network-side detection system for RBS threats that operates through the filtering of Measurement Reports (MRs). The proposed model introduces a probation period, during which candidate base stations are excluded from handover decisions while their legitimacy is continuously validated. We propose a trust analysis component to implement the RBS detector, initially using rule-based approach. The proposed model is augmented with an extended handover protocol that systematically excludes suspected RBS. We also show that the model employs a realistic dataset of radio signal measurements collected from a vehicle traversing various road segments. This scenario provides a practical use case demonstrating the framework’s potential to enhance mobile network protection. The suggested model’s performance results show that in the case of a big dataset comprising 1000 base stations and 180 rogue agents, the accuracy is 0.98, incorrectly classifying fourteen RBS as legitimate, and sets a new standard procedure for RBS identification.

We introduce an analytical framework for quantitative safety level evaluation of periodic-based safety applications in vehicular ad hoc networks (VANETs). The framework is based on Markov chain modeling and associates the probability of successful reception of safety messages by the safety application with the safety level which is eventually offered to the passengers. The relationship between distance and safety risk is also incorporated in the proposed model. The idea of this paper challenges the common trend in literature in which quality of safety (pertaining to safety application) is evaluated through the same parameters as quality of service (of the network). Based on the proposed framework, we investigate the effects of vehicles' transmission range on the evaluated safety level. Further we showed that despite of poor performance of the MAC layer of current standard, which is based on IEEE 802.11p, invoking application layer TDMA-like scheduling improves the quality of safety noticeably satisfying the requirements of most of the life safety applications.