Large language models (LLMs) have made notable advancements across various dimensions of human experience, while prompt engineering has further refined their capability and efficiency in generating relevant, high-quality outputs. Nonetheless, the rise of prompt engineering has also led to an increase in prompt attacks, resulting in critical issues such as privacy breaches, increased latency, and inefficient resource utilization. Existing security mechanisms based on Reinforcement Learning from Human Feedback often fall short in addressing the complexities posed by these diverse prompt attacks, emphasizing the pressing need for effective prompt security mechanisms. The prompt detection model serves as an effective supplementary measure to ensure the safety of large model outputs. However, the current detection mechanism checks all prompts, which increases system resource consumption and user latency. In this paper, we present a holistic analysis of prompt security, service latency, and resource optimization within Edge-Cloud LLM (EC-LLM) systems confronted with various prompt attack scenarios. To strengthen prompt security, we introduce a novel, lightweight attack detection mechanism that uses vector databases. We conceptualize the intricate interplay between prompt detection, latency, and resource optimization within a 2-stage dynamic Bayesian game framework. A reproducible equilibrium strategy is derived by estimating the potential number of malicious tasks and updating beliefs iteratively at each stage using Bayesian methods. Our proposed solution has been rigorously evaluated in a practical EC-LLM system, yielding results that highlight significant enhancements in security, reductions in service latency for legitimate users, and decreased overall resource consumption compared to current leading algorithms.

Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive resources, as these resources may largely remain unused in edge devices. Furthermore, current methods are mainly concentrated on improving the accuracy of DDoS attack detection and saving CPU resources, neglecting the effective allocation of computational power for benign tasks under DDoS attacks. To address these issues, this paper introduces SecEG, a secure and efficient strategy against DDoS attacks for MEC that integrates container-based task isolation with lightweight online anomaly detection on edge nodes. More specifically, a new model is proposed to analyze resource contention dynamics between DDoS attacks and benign tasks. Subsequently, by employing periodic packet sampling and real-time attack intensity predicting, an autoencoder-based method is proposed to detect DDoS attacks. We leverage an efficient scheduling method to optimize the edge resource allocation and the service quality for benign users during DDoS attacks. When executed in the real-world edge environment, our experimental findings validate the efficacy of the proposed SecEG strategy. Compared to conventional methods, the service rate of benign requests increases by 23% under intense DDoS attacks, and the CPU resource is saved up to 35%.

Analyzing blockchain protocols is a notoriously difficult task due to the underlying large scale distributed networks. To address this problem, stochastic model-based approaches are often utilized. However, the abstract models in prior work turn out not to be adoptable to consortium blockchains as the consensus of such a blockchain often consists of multiple processes. To address the lack of efficient analysis tools, we propose a queueing network-based method for analyzing consistency properties of consortium blockchain protocols in this article. Our method provides a way to evaluate the performance of the main stages in blockchain consensus. We apply our framework to the Hyperledger Fabric system and recover key properties of the blockchain network. Using our method, we analyze the security properties of the ordering mechanism and the impact of delaying endorsement messages in consortium blockchain protocols. Then an upper bound is derived of the damage an attacker could cause who is capable of delaying the honest players' messages. Based on the proposed method, we employ analytical derivations to investigate both the security and performance features, and corroborate close agreement with measurements on a wide-area network testbed running the Hyperledger Fabric blockchain. With the proposed method, designers of future blockchains can provide a more rigorous analysis of their consortium blockchain schemes.

Mobile phones and tablets are becoming the primary platform of choice. However, these systems still suffer from limited battery and computation resources. A popular technique in mobile edge systems is computing outsourcing that augments the capabilities of mobile systems by migrating heavy workloads to resourceful clouds located at the edges of cellular networks. In the multi-site scenario, it is possible for mobile devices to save more time and energy by offloading to several cloud service providers. One of the most important challenges is how to choose servers to offload the jobs. In this paper, we consider a multi-site decision problem. We present a scheme to determine the proper assignment probabilities in a two-site mobile-edge computing system. We propose an open queueing network model for an offloading system with two servers and put forward performance metrics used for evaluating the system. Then in the specific scenario of a mobile chess game, where the data transmission is small but the computation jobs are relatively heavy, we conduct offloading experiments to obtain the model parameters. Given the parameters as arrival rates and service rates, we calculate the optimal probability to assign jobs to offload or locally execute and the optimal probabilities to choose different cloud servers. The analysis results confirm that our multi-site offloading scheme is beneficial in terms of response time and energy usage. In addition, sensitivity analysis has been conducted with respect to the system arrival rate to investigate wider implications of the change of parameter values.

In this paper, we present HyperStar2, a tool for fitting Markov Arrival Processes (MAPs) to empirical data. HyperStar2 uses a two-step approach, where the first step is cluster-based fitting of phase-type distributions and the second step is the construction of a correlation matrix. In the first step, we use the cluster-based algorithm for Hyper-Erlang distribution fitting from HyperStar hyper2012. Based on the Hyper-Erlang fitting result and the clusters of samples, in the second step we construct the correlation matrix.