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In Software-Defined Networks (SDN), the ternary content addressable memory (TCAM) capacity in switches is limited, making them vulnerable to low-rate flow table overflow attacks. Most existing research in this field has not focused on the influence of flow entry eviction mechanisms on the effectiveness of such attacks. This paper proposes an adaptive low-rate flow table overflow attack (ALFO), which can adopt corresponding attack modes under different flow entry eviction mechanisms, significantly degrading network service quality. Due to the different features of ALFO under different attack modes, the existing attack detection methods are ineffective in this attack. Therefore, this paper proposes a detection and mitigation framework, which is called adaptive low-rate flow table overflow attack guard framework (ALFO-Guard). It extracts flow features from flow entry information in the switch and aggregates them into a current-time graph model. Then, combining graph neural networks, it performs graph anomaly detection and flow entry classification to identify attack flow entries. Finally, the attack can be eliminated by deleting the identified attack flow entries and blocking the attack flows. The effectiveness of ALFO and ALFO-Guard is validated through extensive experiments, and the experimental results demonstrate that ALFO-Guard can effectively defend against ALFO.

Travel online reviews is important experience related information for understanding an inherent personality trait, novelty seeking (NS), which influences tourism motivation and the choice of tourism destinations. Manual classification of these reviews is challenging due to their high volume and unstructured nature. This paper aims to develop a classification framework and deep learning model to overcome these limitations. A multi-dimensional classification framework was created for NS personality trait that includes four dimensions synthesized from prior literature: relaxation seeking, experience seeking, arousal seeking and boredom alleviation. Based on 30 000 reviews from TripAdvisor we propose a deep learning model using Bidirectional Encoder Representations from Transformers (BERT)- Bidirectional Gated Recurrent Unit (BiGRU) to recognize NS automatically from the reviews. The classifier based on BERT- BiGRU and NS multi-dimensional scales achieved precision and F1 scores of 93.4% and 93.3% respectively, showing that NS personality trait can be relatively accurately recognized. This study also demonstrates that the classifier based on multi-dimensional NS scales can produce satisfactory results using the deep learning model. The findings also indicate that the BERT- BiGRU model achieves the best effect compared to the same kind of deep learning models. Moreover, it proves that personality traits can be automatically identified from travel reviews based on computational techniques. For practical purposes, this study provides a comprehensive classification framework for NS, which can be used in marketing and recommendation systems operating in the tourism industry.

In Software-Defined Networks (SDN), the limited flow table capacity of switches makes them susceptible to flow table overflow attacks, which can lead to performance degradation or network corruption. Prior research has mainly focused on rate-based overflow attacks (ROA), which exhibit varying attack effects depending on the overflow rate. This study introduces a novel attack called the preemptive overflow attack (POA), which exploits flow entry eviction mechanism to preempt the flow entries of normal applications, resulting in amplified performance degradation. Notably, when using the widely deployed Least Frequently Used (LFU) eviction algorithm, POA achieves a significant impact while consuming fewer flow entries than existing ROA methods. Furthermore, the detection of POA remains challenging owing to the lack of distinctive flow features. To mitigate POA, we propose POAGuard as a defense mechanism. POAGuard incorporates a table segmentation method for table management, a score-based eviction algorithm that evicts suspicious flow entries, and a concept drift-based detection method that identifies and defends against POA. Extensive experiments are conducted to verify the effectiveness of POAGuard, and the results demonstrate that POAGuard can effectively defend against POA.

In recent years, with regional integrated seabed resource exploitation continuing to expand, offshore oilfield power systems have gradually become networked, forming an offshore oilfield multi-platform interconnected power system. In this paper, a two-level optimization model for offshore oilfield multi-platform interconnected power system structure is developed. The lower-level model uses a minimum cut-load cost model that considers load priority to calculate the power outage losses of all N-1 fault conditions. The reliability assessment method helps to achieve a balance between economy and reliability. Additionally, an optimization model simplification method based on graph theory is proposed, and it uses the minimum cost maximum flow (MCMF) algorithm to optimize the power flow of the grid and narrows the problem's solution space. At the end of modeling, a parallel global taboo search algorithm is used to reduce the computational time and improve the probability of finding an optimal solution. Investigations of a real offshore oilfield multi-platform power system verify the accuracy of the model, the simplification method and the parallel global taboo search algorithm.

With the expansion of the scale of subsea oil and gas resource exploitation, the offshore multiplatform interconnected power systems have become the main form of offshore platform group energy supply. The offshore multiplatform interconnected power system with wind power can effectively reduce the system fuel consumption and carbon emission, which has become an important direction for the development of future offshore multiplatform interconnected power systems. In this paper, the scenario method is adopted to portray the wind power output. System evaluation indicators considering the economy, reliability and environmental benefits of the system are proposed. Considering the nonnegligible impact of the cable size selection problem on the system economic cost, this paper introduces cable size selection variables in the expansion planning model of the offshore multiplatform interconnected power system with wind power to achieve accurate optimization of the system planning scheme but also greatly increases the difficulty of solving the model. A model linearization method is applied to linearize the nonlinear part of the model, so that the optimized model is a mixed-integer linear programming model under the determined system topology, and thus can be solved using commercial planning software. In addition, an improved genetic algorithm is proposed to optimize the system topology, which improves the efficiency of the model solution. The cases of different scale verify the validity of the model and the solution algorithm and show the impact of the maximum penetration rate of wind power on the total system cost.