Network security is experiencing huge challenges as network attacks on traffic data become more frequent and sophisticated. In this paper, we employ hybrid deep learning models and low-rank approximation to present a novel method for multi-label categorization of network assaults on traffic data. Our suggested solution, LR-CNN-MLP, consists of three models While the CNN and MLP models extract features and categorise data, respectively, the low-rank approximation model reduces the input's dimensionality. Overall, by combining hybrid models and low-rank approximation, our proposed LR-CNN-MLP approach provides a promising solution for multi-label categorization of network attacks on traffic data.

A data center (DC) is supposed to efficiently distribute the bandwidth of the network to provide high-quality traffic transmission. However, the load imbalance issue can easily occur due to the complex topology and traffic features. Equal-Cost Multi-Path(ECMP) distributes traffic on different paths but doesn’t consider network congestion. Although HULA solved some of ECMP’s problems, it can easily congest the best path. RPS randomly distributes packets on paths, which can easily lead to packet disorder in some scenarios. This paper presents DHLB, a distributed hop-by-hop load balancing archetucture based on in-band network telemetry. With active In-band network telemetry, DHLB collects the necessary load information and stores it in the load information table. DHLB distributes traffic proportionally on different paths based on their load degree. We build a fat tree topology on mininet to verify the performance of our design. From experimental results, DHLB performs better than other schemes in terms of average flow complete time(FCT). It also performs better on additional overhead than another probe-based scheme.

Network reliability analysis is vital for ensuring efficient and error-free communication within networking and communication applications. Binary Decision Diagrams (BDDs) have emerged as a powerful tool for analyzing and optimizing complex network infrastructures. The objective of this research paper is to conduct a comparative analysis of edge-ordering algorithms for network reliability using BDDs the study aims to evaluate and compare existing algorithms, providing valuable insights for selecting suitable edge-ordering algorithms that enhance network reliability. The paper concludes that snooker is outperforming among selected algorithms.

In this letter, we consider the privacy-preserving strategies of the distributed state estimator over wireless sensor networks. Aiming at an eavesdropper who can intercept the data transmitted on the communication channel, we design a simple preserving scheme to encode the transmitted data by using system dynamics and history estimates. To analyze the privacy-preserving performance, we use the gap between the values deciphered by the eavesdropper and decoded by the sensor as the privacy index to evaluate the degree of robust privacy protection. Finally, we provide some simulations to illustrate the effectiveness of the proposed preserving schemes.