Biblio

Software-Defined Networking (SDN) technique is presented in this paper to manage the Naval Supervisory Control and Data Acquisition (SCADA) network for equipping the network with the function of reconfiguration and scalability. The programmable nature of SDN enables a programmable Modular Topology Generator (MTG), which provides an extensive control over the network’s internal connectivity and traffic control. Specifically, two functions of MTG are developed and examined in this paper, namely linkHosts and linkSwitches. These functions are able to place the network into three different states, i.e., fully connected, fully disconnected, and partially connected. Therefore, it provides extensive security benefits and allows network administrators to dynamically reconfigure the network and adjust settings according to the network’s needs. Extensive tests on Mininet have demonstrated the effectiveness of SDN for enabling the reconfigurable and scalable Naval SCADA network. Therefore, it provides a potent tool to enhance the resiliency/survivability, scalability/compatibility, and security of naval SCADA networks.

Most of existing signal modulation recognition methods attempt to establish a machine learning mechanism by training with a large number of annotated samples, which is hardly applied to the real-world electronic reconnaissance scenario where only a few samples can be intercepted in advance. Few-Shot Learning (FSL) aims to learn from training classes with a lot of samples and transform the knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a more effective feature representation of support samples. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust to low signal-to-noise-ratio conditions.

With the rapid development of traffic encryption technology and the continuous emergence of various network services, the classification of encrypted zero-day applications has become a major challenge in network supervision. More seriously, many attackers will utilize zero-day applications to hide their attack behaviors and make attack undetectable. However, there are very few existing studies on zero-day applications. Existing works usually select and label zero-day applications from unlabeled datasets, and these are not true zero-day applications classification. To address the classification of zero-day applications, this paper proposes an Encrypted Zero-day Applications Classification (EZAC) method that combines Convolutional Neural Network (CNN) and K-Means, which can effectively classify zero-day applications. We first use CNN to classify the flows, and for the flows that may be zero-day applications, we use K-Means to divide them into several categories, which are then manually labeled. Experimental results show that the EZAC achieves 97.4% accuracy on a public dataset (CIC-Darknet2020), which outperforms the state-of-the-art methods.

With the improvement of the current Internet software and hardware performance, cloud storage has become one of the most widely used applications. This paper proposes a user privacy protection algorithm suitable for tennis match live broadcast from media cloud platform. Through theoretical and experimental verification, this algorithm can better protect the privacy of users in the live cloud platform. This algorithm is a ciphertext calculation algorithm based on data blocking. Firstly, plaintext data are grouped, then AES ciphertext calculation is performed on each group of plaintext data simultaneously and respectively, and finally ciphertext data after grouping encryption is spliced to obtain final ciphertext data. Experimental results show that the algorithm has the characteristics of large key space, high execution efficiency, ciphertext statistics and good key sensitivity.

Security auditing is an effective means of safety supervision and risk self-identification. It is also a hot issue in the research of major enterprises and institutions. The existing standard norms cannot effectively guide the internal audit of the enterprise intranet, “how to review” and “review” What is not easy to grasp. This paper analyzes the status quo, problems and development trends of security audit, and proposes a behavior-based security audit mode, which can effectively discover the non-compliance behavior of intranet users, and can provide reference for peers through practice.

With the advent of the Internet of Things (IoT) and big data, high fidelity localization and tracking systems that employ cameras, RFIDs, and attached sensors intrude on personal privacy. However, the benefit of localization information sharing enables trend forecasting and automation. To address this challenge, we introduce Wobly, an attribute based signature (ABS) that measures gait. Wobly passively receives Wi-Fi beacons and produces human signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Because signatures are specific to antenna placement and room configuration and do not require sensor attachments, the identities of the individuals can remain anonymous. However, the gait based signatures are still unique, and thus Wobly is able to track individuals in a building or home. Wobly uses the physical layer channel and the unique human gait as a means of encoding a person's identity. We implemented Wobly on a National Instruments Radio Frequency (RF) test bed. Using a simple naive Bayes classifier, the correct identification rate was 87% with line-of-sight (LoS) and 77% with non-line-of-sight (NLoS).