The issue of multi-set membership query is a fundamental task in the fields of distributed systems and computer networks. It entails identifying which sets, out of n sets$S_{0}, S_{1},{\ldots }, S_{n-1}$, in a Multi Set Multi-Membership Querying (MS-MMQ) contain a given element q. To address this problem while minimizing space usage, probabilistic data structures, such as cuckoo filters, are commonly employed. However, existing sketch data structures struggle to effectively balance scalability and query efficiency. To address this challenge, we introduce an enhanced marked cuckoo filter (EMCF), which enhances support for MS-MMQ scenarios by incorporating set markers after the fingerprint field. Additionally, it grants horizontal scalability and collaborative search capabilities at the filter level through the inclusion of global markers. Furthermore, we have developed an optimized variant of the enhanced marked cuckoo filter (EMCF-V) for multi-set scenarios to achieve space optimization. Experimental results using real-world datasets demonstrate that EMCF exceeds CSC-CF by more than 10 times in terms of speed, and EMCF-V exhibits a 33.3% higher query efficiency than CSC-CF methods, particularly in multiset scenarios.

In recent years, the concept of Zero Trust Networks (ZTN) has been proposed to overcome unrealistic security assumptions, e.g., what lies in private networks (such as data centers) is always trusted and safe. In ZTN, no device or user is assumed to be secure, instead all connections have to be authenticated and authorized before being established. Software Defined Perimeter (SDP) is one of the most promising solution for ZTN, where the gateway allows clients to access services only after receiving legitimate Single Packet Authorization (SPA) data. However, existing SDP solutions either (1) need to decouple the SPA from the connection request, resulting in redundant communication processes and impersonation attacks; or (2) need to copy the SPA data to the user space from sniffers, causing the packets to enter the protocol stack repeatedly. Due to the large number of short-lived streams in the data center, inefficiency and insecurity of the SPA process lead to severe connection delays and network attacks (e.g., DDoS). To this end, we propose an eBPF-based Dynamic Perimeter (EDP) to enhance the security and performance of SDP. By using EDP, authentication data can be efficiently embedded into every packet and checked before entering the receiver's protocol stack. Experimental results show that the connection delay of EDP is 80% less than that of the existing state-of-the-art solutions.

DNS has encountered complex and diversified attacks over the years due to its special status on the Internet. The concept of DNS-over-HTTPS (DoH) has been proposed to protect user privacy by encapsulating DNS into HTTPS, which increases the difficulty of DNS tunnel detection but also faces some new attacks. In recent years, many researchers have discussed the detection methods of DoH tunnel. However, most of them need large-scale labeled datasets and extract statistical features, which is time-consuming and costs immense manpower, so it is impractical to be used in the real-world. In this paper, we developed a system called AADDS: an Autoencoder-based Anomaly Detection for the DoH traffic System consists of Traffic Capture module and Anomaly Detection module. The Traffic Capture module is developed based on nff-go, which can collect features stably in a high-speed Ethernet environment and greatly reduce the workload. For the Anomaly Detection module, we used bidirectional Long and Short-Term Memory (Bi-LSTM) to build an autoencoder network. Several essential experiments proved that our method has fewer parameters while ensuring higher accuracy, and it outperforms the state-of-the-art methods.

Research has shown that in-network caching for named data in ICN is an effective way to eliminate redundant network traffic. The caching strategy is the key to improve the efficiency of content distribution in the ICN. However, current complicated caching strategies have high latency and couple with network forwarding, which results in terrible forwarding performance and poor scalability of throughput. In this paper, we propose a novel mechanism for making caching decisions in the source. In our mechanism, to improve the forwarding speed, the decision result is encoded in the cache control field of the content packet so the content packet can be quickly filtered and forwarded by on-path routers with an almost-zero latency. In order to improve the accuracy of caching decision in the mechanism, we further present a lightweight caching strategy based on popularity ranking. In the experiment, we set up a POF-based ICN soft router with a split architecture and implement our caching decision mechanism inside. The result of the experiment shows that on-path decision latency can be reduced by at most 96.7 percentage in our mechanism and a scalable line-speed of at least 10 Gbps is guaranteed. Our mechanism is also lightweight which achieves similar performance with less than 1/20 memory consumption of other popularity-based strategies.

More and more websites adopt Hypertext Transfer Protocol Secure (HTTPS) protocol to provide data security, while it also brings overhead to users and servers. Security and response time are important for user experience, unfortunately they cannot get the best at the same time. Since the demands are different various from users, it is feasible for servers to provide differentiated services. Based on this, we put forward a service differentiation strategy for https web servers. Firstly, we propose the adaptive goals cipher suite selection algorithm to meet the different demands for security and response time. Moreover, we further improve the performance by the priority strategy based on scheduling period. It reduces the response time for higher priority requests and guarantees the response time for lower priority requests, while reducing the average system response time. Experiments prove the efficiency of our method, and when the server load is high, the advantage of our strategy is more obvious.

In the problem of classification of audio signals, the requirements of low-complexity, high-accuracy and short delay are crucial for some practical scenarios. This paper proposes a method of real-time speech/music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected together with a proposed simple harmonic structure stability feature, which is based on a rough estimation of the harmonic structure. A feature subset selection tool is used to select a subset of short and long term features to feed into a hierarchical oblique decision tree classifier. The method is evaluated and compared with the open loop selection mode in AMR-WB+. Experiments show the proposed approach gives a better performance (98.3%) compared to other prevailing approaches. In particular, it comes with promising short delay of 10 ms and low complexity of 1 wmops.

Although technologies of both low-level and high-level descriptors for music information retrieval (MIR) are advancing, there are some essential deficiencies while utilizing them separately. In this paper we propose a model where the low-level and high-level descriptors collaborate to support semantics-based MIR. The ontology of ldquomusic scenerdquo domain is constructed as a demonstration, and a set of domain related low-level and high-level descriptor analyses are introduced. Given the domain ontology and the analysis results as input, an abduction process is adopted to compute the semantics-based interpretations. Evaluations show that the collaborative model does not only give a better recall rate of semantics-based retrieval than separated models, but also maintains a promising precision meanwhile.