The security context used in 5G authentication is generated during the Authentication and Key Agreement (AKA) procedure and stored in both the user equipment (UE) and the network sides for the subsequent fast registration procedure. Given its importance, it is imperative to formally analyze the security mechanism of the security context. The security context in the UE can be stored in the Universal Subscriber Identity Module (USIM) card or in the baseband chip. In this work, we present a comprehensive and formal verification of the fast registration procedure based on the security context under the two scenarios in ProVerif. Our analysis identifies two vulnerabilities, including one that has not been reported before. Specifically, the security context stored in the USIM card can be read illegally, and the validity checking mechanism of the security context in the baseband chip can be bypassed. Moreover, these vulnerabilities also apply to 4G networks. As a consequence, an attacker can exploit these vulnerabilities to register to the network with the victim's identity and then launch other attacks, including one-tap authentication bypass leading to privacy disclosure, location spoofing, etc. To ensure that these attacks are indeed realizable in practice, we have responsibly confirmed them through experimentation in three operators. Our analysis reveals that these vulnerabilities stem from design flaws of the standard and unsafe practices by operators. We finally propose several potential countermeasures to prevent these attacks. We have reported our findings to the GSMA and received a coordinated vulnerability disclosure (CVD) number CVD-2022-0057.
Recently, various BERT-based architectures for fake news detection have been proposed. In many cases, these methods work well on various benchmark datasets. However, the performance quickly deteriorates when the models are tested with samples coming from distributions which essentially differ from the ones the model has been trained on. To overcome this obstacle, in this paper, we have proposed a framework for the model’s parameter transfer in order to redefine fake news detection as a multi-domain classification problem. The proposed solution is intended to possibly leverage different elements of the classification model’s configuration to maximise global performance. The research presented in this paper utilises six publicly available, open benchmark datasets, presents the motivation, the architecture and the technical details of the proposed solution, and reports the achieved results with an array of well-known evaluation metrics. The presented results are promising and open the space for further research.
Recent catastrophic landslide disasters in Japan have highlighted the need for a quick and reliable warning system. The Early Landslide Warning System, which utilizes many IoT sensors, is considered effective in observing landslides. However, the number of IoT connections and the accuracy of the sensors still need to be improved, especially when compared to conventional survey methods. Therefore, this paper proposes the Enhanced MQTT method for the Early Landslide Warning System, and the proposed method improves connectivity, reduces latency, and minimizes transmitting errors by assigning priority values to IoT data. In detail, the proposed method first assigns the priority value to the user property field in the MQTT header according to the abnormality of the observed IoT data. Secondly, the proposed priority value stores the IoT data in the broker node’s priority queues. Finally, the IoT data is asynchronously transmitted to the publisher from the high-priority queue. Then, this paper reports the experiments of the Enhanced MQTT methods by the prototype system, and the effectivity and the future studies are discussed in the paper.
Network intelligence has become an important trend in modern communication networks. In the future 6G network, the unrestricted communication between massive heterogeneous terminals will lead to more and more kinds of DDoS attacks, which will become an important factor affecting network security. In this paper, we propose a knowledge base detection scheme for malicious behavior of DDoS attacks based on graph neural networks. First, this paper constructs a malicious behavior knowledge base for a variety of common DDoS attacks. Considering the problem of multi-source heterogeneity under 6G network, this paper proposes a malicious behavior knowledge graph construction algorithm, which constructs a global malicious behavior knowledge graph from both address correlation and time correlation of network services. And the graph attention network is introduced on the basis of the knowledge graph to identify the malicious behaviors occurring in the network. The experimental results show that the detection scheme can enrich the feature representation of malicious behavior nodes. The scheme has a better performance compared with the machine learning scheme, and ultimately reduces the malicious traffic caused by DDoS attacks by more than an order of magnitude.
Salsa, designed by Daniel J. Bernstein is one of the finalists candidate of eSTREAM projects in 2005. It is a well known stream cipher and it gained its popularity when the most used stream cipher RC4 was subjected to multiple cryptanalytic techniques. The stream cipher Salsa comes in two versions - 128 bits and 256 bits depending upon the size of the seed key. Salsa has went through various key recovery attacks up to its 8-th round where most of the attacks were focused on Salsa 256 bits. There are many more prevalent experimental attacks on Salsa while leaving space for theoretical analysis. Theoretical analysis helps in spotting the feature that subjects the cipher to attack and thus can be used to design a better cipher that is resistive to attacks. Dey et al. [1] have theoretically analyzed the cause of distinguishers in Salsa 256 bits and Chacha 256 bits that occur in experimental attacks. Motivated by this work, in this paper, we have theoretically analyzed differential attack on Salsa 128 bits up to four rounds and mathematically proved the probabilities of the observations. Also, our theoretical analysis results of Salsa 128 bits are on par with the experimental results. As an extension of this work and to find more accurate distinguishers for round 4 and higher rounds, we are working on the idea to find neural distinguisher in Salsa 128-bits based on the model proposed by Gohr in CRYPTO 2019 [2].
