Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
In this paper, we would like to introduce a unique dataset that covers thousands of network flow measurements realized through TCP in a data center environment. The TCP protocol is widely used for reliable data transfers and has many different versions. The various versions of TCP are specific in how they deal with link congestion through the congestion control algorithm (CCA). Our dataset represents a unique, comprehensive comparison of the 17 currently used versions of TCP with different CCAs. Each TCP flow was measured precisely 50 times to eliminate the measurement instability. The comparison of the various TCP versions is based on the knowledge of 18 quantitative attributes representing the parameters of a TCP transmission. Our dataset is suitable for testing and comparing different versions of TCP, creating new CCAs based on machine learning models, or creating and testing machine learning models, allowing the identification and optimization of the currently existing versions of TCP....
The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio- Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular,We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model....
In the domain of data mining, the extraction of frequent patterns from expansive datasets remains a daunting task, compounded by the intricacies of temporal and spatial dimensions. While the Apriori algorithm is seminal in this area, its constraints are accentuated when navigating larger datasets. In response, we introduce an avant-garde solution that leverages parallel network topologies and GPUs. At the heart of our method are two salient features: (1) the use of parallel processing to expedite the realization of optimal results and (2) the integration of the cat and mouse-based optimizer (CMBO) algorithm, an astute algorithm mirroring the instinctual dynamics between predatory cats and evasive mice. This optimizer is structured around a biphasic model: an initial aggressive pursuit by the cats and a subsequent calculated evasion by the mice. This structure is enriched by classifying agents using their objective function scores. Complementing this, our architectural blueprint seamlessly amalgamates dual Nvidia graphics cards in a parallel configuration, establishing a marked ascendancy over conventional CPUs. In amalgamation, our approach not only rectifies the inherent shortfalls of the Apriori algorithm but also accentuates the extraction of association rules, pinpointing frequent patterns with enhanced precision. A comprehensive evaluation across a spectrum of network topologies explains their respective merits and demerits. Set against the benchmark of the Apriori algorithm, our method conspicuously outperforms in terms of speed and effectiveness, heralding a significant stride forward in data mining research....
Secure data transmission and efficient network performance are both key aspects of the modern Internet. Traditionally, Transport Layer Security (TLS)/Transmission Control Protocol (TCP) has been used for reliable and secure networking communications. In the past decade, Quick User Datagram Protocol (UDP) Internet Connections QUIC has been designed and implemented on UDP, attempting to improve security and efficiency of Internet traffic. Real‐world platform investigations are carried out in this paper to evaluate TLS/TCP and QUIC/UDP in maintaining communication, security and efficiency under three different types of popular cyber‐attacks. A set of interesting findings, including delay, loss, server CPU utilisation and server memory usage are presented to provide a comprehensive understanding of the two protocol stacks in performing malicious traffic. More specifically, in terms of the efficiency in achieving short delays and low packet loss rates with limited CPU and memory resources, QUIC/UDP performs better under Denial of Service attacks but TLS/TCP overtakes QUIC/UDP when handling MitM attacks. In terms of security, the implementation of TCP tends to be more secure than QUIC, but QUIC traffic patterns are harder to learn using machine learning methods. We hope that these insights will be informative in protocol selection for future networks and applications, as well as shedding light on the further development of the two protocol stacks....
The progression of the Internet of Things (IoT) has brought about a complete transformation in the way we interact with the physical world. However, this transformation has brought with it a slew of challenges. The advent of intelligent machines that can not only gather data for analysis and decision-making, but also learn and make independent decisions has been a breakthrough. However, the low-cost requirement of IoT devices requires the use of limited resources in processing and storage, which typically leads to a lack of security measures. Consequently, most IoT devices are susceptible to security breaches, turning them into “Bots” that are used in Distributed Denial of Service (DDoS) attacks. In this paper, we propose a new strategy labeled “Temporary Dynamic IP” (TDIP), which offers effective protection against DDoS attacks. The TDIP solution rotates Internet Protocol (IP) addresses frequently, creating a significant deterrent to potential attackers. By maintaining an “IP lease-time” that is short enough to prevent unauthorized access, TDIP enhances overall system security. Our testing, conducted via OMNET++, demonstrated that TDIP was highly effective in preventing DDoS attacks and, at the same time, improving network efficiency and IoT network protection....
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