With various industrial wireless networks greeting booming development, modern industrial devices configured with several network interfaces increasingly become the norm. Such multihomed industrial devices can increase application throughput by making use of multiple network paths, enabled by the multipath transmission control protocol (MTCP) (MPTCP). However, MPTCP might be challenged in the heterogeneous industrial networks because concurrent transmitting industrial application data over asymmetric network paths with different delays is almost bound to the receive buffer blocking problem, which is caused by out-of-order packet arrival and is harmful to the performance of the multipath transmission. The existing MPTCP solutions generally use static mathematical models to evaluate path quality and prohibit transmission on paths with poor quality, which are unable to perform efficiently under highly dynamic and complex network environments. Therefore, in this article, we propose a learning-driven latency-aware MPTCP variant, called ${l}\,^2$ -MPTCP, which seeks to possibly mitigate the out-of-order packet arrival and receive buffer blocking problems associated with the network heterogeneity in the industrial Internet. ${l}\,^2$ -MPTCP accurately computes each MPTCP path’s forward delay and assigns application data to multiple paths according to their calculated forward delay differences by using a novel multiexpert learning-enabled forward delay estimator. ${l}\,^2$ -MPTCP dynamically manages path usage and chooses the optimal path collection for bandwidth aggregation and multipath transmission by using a promising reinforcement learning-empowered multipath manager. Experimental results demonstrate that ${l}\,^2$ -MPTCP outperforms the current MPTCP solutions in terms of multipathing service quality.

With the development of wireless networking technology, current Internet-of-Things (IoT) devices are equipped with multiple network access interfaces. Multipath TCP (MPTCP) technology can improve the throughput of data transmission. However, traditional MPTCP path management may cause problems such as data confusion and even buffer blockage, which severely reduces transmission performance. This research introduces machine learning algorithms into MPTCP path management, and proposes an automatic learning selection path mechanism based on MPTCP (ALPS-MPTCP), which can adaptively select some high-quality paths and transmit data at the same time. This paper designs a simulation experiment that compares the performance of four machine learning algorithms in judging path quality. The experimental results show that, considering the running time and accuracy, the random forest algorithm has the best performance in judging path quality.

The multipath TCP (MPTCP) enables multihomed mobile devices to realize multipath parallel transmission, which greatly improves the transmission performance of the mobile communication network. With the rapid development of all kinds of emerging technologies, network attacks have shown a trend of development with many types and rapid updates. Among them, low‐rate distributed denial of service (LDDoS) attacks are considered to be one of the most threatening issues in the field of network security. In view of the current research status, by using the network simulation software NS2, this paper first compares and analyzes the throughput and delay performance of the MPTCP transmission system under LDDoS attacks and, further, conducts simulation experiments and analysis on the queue occupancy rate of the LDDoS attack flow to extract the basic attack characteristics of the LDDoS attacks. The experimental results show that the LDDoS attacks will have a major destructive effect on the throughput performance and delay performance of the MPTCP transmission system, resulting in a decrease in the robustness of the transmission system. By analyzing and comparing the occupancy rate of the LDDoS attack flow in the MPTCP transmission system, it can be concluded that (1) the occupancy rate of the LDDoS scattered pulse traffic sent by each puppet machine changes slightly, and (2) the occupancy rate of LDDoS attack data flow is much greater than that of ordinary TCP data flow.

With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

Distributed Denial-of-Service (DDoS) attacks can cause significant damage to network applications. A crucial step in combating these attacks lies in promptly and accurately detecting DDoS attack traffic. However, existing solutions struggle with data imbalance and contamination, leading to suboptimal DDoS detection. Furthermore, current methods typically require access to raw data for training, posing a significant privacy risk. To tackle these challenges, we propose HFL-AD, a hierarchical federated learning framework specifically designed for detecting DDoS attack traffic by resolving the data contamination issue. In our approach, a federation of lower-layer clients train local anomaly detection models using diverse raw data. A selected few clients, possessing a small supplementary dataset, serve as upper-layer clients, responsible for excluding model updates uploaded by lower-layer clients that have been trained on contaminated datasets. Experimental results demonstrate that HFL-AD outperforms state-of-the-art (SOTA) solutions in DDoS detection, particularly when some training datasets are contaminated.

With the rapid development of mobile Internet technology and multihost terminal devices, multipath transmission protocol has been widely concerned. Among them, multipath TCP (MPTCP) has become a hot research protocol in recent years because of its good transmission performance and Internet compatibility. Due to the increasing power of Low-Rate Distributed Denial of Service (LDDoS) attack, the network security situation is becoming increasingly serious. The robustness of MPTCP network has become an urgent performance index to improve. Therefore, it is very necessary to detect LDDoS abnormal traffic timely and effectively in the transmission system based on MPTCP. This paper tries to use wavelet transform technology to decompose and reconstruct network traffic and find a detection method of LDDoS abnormal traffic in the MPTCP transmission system. The experimental results show that in the MPTCP transmission system, the signal processing technology based on wavelet transform can realize the identification of LDDoS abnormal traffic. It indicates a direction worth further exploration for the detection and defense of the LDDoS attack.