To safeguard critical services and assets in a distributed environment, collaborative intrusion detection systems (CIDSs) are usually adopted to share necessary data and information among various nodes, and enhance the detection capability. For simplifying the network management, software defined networking (SDN) is an emerging platform that decouples the controller plane from the data plane. Intuitively, SDN can help lighten the management complexity in CIDSs, and a CIDS can protect the security of SDN. In practical implementation, trust management is an important approach to help identify insider attacks (or malicious nodes) in CIDSs, but the challenge is how to ensure the data integrity when evaluating the reputation of a node. Motivated by the recent development of blockchain technology, in this work, we design BlockCSDN — a framework of blockchain-based collaborative intrusion detection in SDN, and take the challenge-based CIDS as a study. The experimental results under both external and internal attacks indicate that using blockchain technology can benefit the robustness and security of CIDSs and SDN.

In recent years, Graph Neural Networks has received enormous attention from academia for its huge potential of modeling the network traits such as macrostructure and single node attributes. However, prior mainstream works mainly focus on homogeneous network and lack the capacity to characterize the network heterogeneous property. Besides, most previous literature cannot the model latent influence link under microscope vision, making it infeasible to model the joint relation between the heterogeneity and mutual interaction within multiple relation type. In this letter, we propose a latent influence based self-attention framework to address the difficulties mentioned above. To model the heterogeneity and mutual interactions, we redesign the attention mechanism with latent influence factor on single-type relation level, which learns the importance coefficient from its adjacent neighbors under the same meta-path based patterns. To incorporate the heterogeneous meta-path in a unified dimension, we developed a novel self-attention based framework for meta-path relation fusion according to the learned meta-path coefficient. Our experimental results demonstrate that our framework not only achieves higher results than current state-of-the-art baselines, but also shows promising vision on depicting heterogeneous interactive relations under complicated network structure.

For many military and civilian large-scale, real-world systems of interest, data are first acquired asynchronously, i. e. at irregular intervals of time, at geographically-dispersed sites, processed utilizing decision-making algorithms, and the processed data then disseminated to other appropriate sites. The term real-world refers to systems under computer control that relate to everyday life and are beneficial to the society in the large. The traditional approach to such problems consists of designing a central entity which collects all data, executes a decision making algorithm sequentially to yield the decisions, and propagates the decisions to the respective sites. Centralized decision making algorithms are slow and highly vulnerable to natural and artificial catastrophes. Recent literature includes successful asynchronous, distributed, decision making algorithm designs wherein the local decision making at every site replaces the centralized decision making to achieve faster response, higher reliability, and greater accuracy of the decisions. Two key issues include the lack of an approach to synthesize asynchronous, distributed, decision making algorithms, for any given problem, and the absence of a comparative analysis of the quality of their decisions. This paper proposes MFAD, a Mathematical Framework for Asynchronous, Distributed Systems, that permits the description of centralized decision-making algorithms and facilities the synthesis of distributed decision-making algorithms. MFAD is based on the Kohn-Nerode distributed hybrid control paradigm. It has been a belief that since the centralized control gathers every necessary data from all entities in the system and utilizes them to compute the decisions, the decisions may be "globally" optimal. In truth, however, as the frequency of the sensor data increases and the environment gets larger, dynamic, and more complex, the decisions are called into question. In the distributed decision-making system, the centralized decision-making is replaced by those of the constituent entities that aim at minimizing a Lagrangian, i. e. a local, non-negative cost criterion, subject to the constraints imposed by the global goal. Thus, computations are carried out locally, utilizing locally obtained dataand appropriate information that is propagated from other sites. It is hypothesized that with each entity engaged in optimizing its individual behavior, asynchronously, concurrently, and independent of other entities, the distributed system will approach "global" optimal behavior. While it does not claim that such algorithms may be synthesized for all centralized real-world systems, this paper implements both the centralized and distributed paradigms for a representative military battlefield command, control, and communication (C3) problem. It also simulates them on a testbed of a network of workstations for a comparative performance evaluation of the centralized and decentralized paradigms in the MFAD framework. While the performance results indicate that the decentralized approach consistently outperforms the centralized scheme, this paper aims at developing a quantitative evaluation of the quality of decisions under the decentralized paradigm. To achieve this goal, it introduces a fundamental concept, embodied through a hypothetical entity termed "Perfect Global Optimization Device (PGOD)," that generates perfect or ideal decisions. PGOD possesses perfect knowledge, i. e. the exact state information of every entity of the entire system, at all times, unaffected by delay. PGOD utilizes the same decision-making algorithm as the centralized paradigm and generates perfect globally-optimal decisions which, though unattainable, provide a fundamental and absolute basis for comparing the quality of decisions. Simulation results reveal that the quality of decisions in the decentralized paradigm are superior to those of the centralized approach and that they approach PGOD's decisions.

Determining an effective way to reduce computation complexity is an essential task for adaptive echo cancellation applications. Recently, a family of partial update (PU) adaptive algorithms has been proposed to effectively reduce computational complexity. However, because a PU algorithm updates only a portion of the weights of the adaptive filters, the rate of convergence is reduced. To address this issue, this paper proposes an enhanced switching-based variable step-size (ES-VSS) approach to the M-max PU least mean square (LMS) algorithm. The step-size is determined by the correlation between the error signals and their noise-free versions. Noise-free error signals are approximated according to the level of convergence achieved during the adaptation process. The approximation of the noise-free error signals switches among four modes, such that the resulting step-size is as close to its optimal value as possible. Simulation results show that when only a half of all taps are updated in a single iteration, the proposed method significantly enhances the convergence rate of the M-max PU LMS algorithm.

This paper compared the approaches concerning the pinball routing problem that occurs in the nested network in network mobility environment and developed the analytic framework to model. Each model was evaluated of transmission latency, memory usage, and BU's occurrence number at routing optimization process. The estimation result showed that the optimization mechanism achievement overhead existed in each model, and the full optimization of the specific model was not attained because of it. Therefore, the most appropriate approach for routing optimization in nested NEMO can be determined only after a careful evaluation, and the proposals must consider using it in combination with other approaches. The modeling framework presented in this paper is intended to quantity the relative merits and demerits of the various approaches.

In this paper, we propose optimization approaches for the parameter determination of the PNNI complex node model. Two optimal objectives are discussed: Least Square Approximation and Maximum Deviation Minimization. For each objective, we propose two practical criteria for setting up bypasses: Maximum Difference Removal and Largest Deviation Removal. Generalized inverse of matrix and linear programming techniques are used to find the solutions. The numerical results show that the least square approximation with the largest deviation removal criteria has the best performance as the number of bypasses increases.

The conventional describing function of Coulo-mb friction is based on the assumption that the reference input is constant. The author proposes the describing function of Coulomb friction for the ramp reference input. The experimental results for the DC servo motor control system with ramp tracking controller are shown.

Lately, an increasing number of wireless local area network (WLAN) access points (APs) are deployed to serve an ever increasing number of mobile stations (STAs). Due to the limited frequency spectrum, more and more AP and STA nodes try to access the same channel. Spatial spectrum reuse is promoted by the IEEE 802.11ax task group through dynamic sensitivity control (DSC), which permits cochannel operation when the received signal power at the prospective transmitting node (PTN) is lower than an adjusted carrier sensing threshold (CST). Previously-proposed DSC approaches typically calculate the CST without node grouping by using a margin parameter that remains fixed during operation. Setting the margin has previously been done heuristically. Finding a suitable value has remained an open problem. Therefore, herein, we propose a DSC approach that employs a node grouping method for adaptive calculation of the CST at the PTN with a channel-aware and margin-free formula. Numerical simulations for dense residential WLAN scenario reveal total throughput and Jain's fairness index gains of 8.4% and 7.6%, respectively, vs. no DSC (as in WLANs deployed to present).

With the proliferation of multimedia group applications, the construction of multicast trees satisfying the Quality of Service (QoS) requirements is becoming a problem of the prime importance. An essential factor of these real-time application is to optimize the Delay- and delay Variation-Bounded Multicast Tree (DVBMT) problem. This problem is to satisfy the minimum delay variation and the end-to-end delay within an upper bound. The DVBMT problem is known as NP-complete problem. The representative algorithms for the problem are DVMA, DDVCA, and so on. In this paper, we show that the proposed algorithm outperforms any other algorithm. The efficiency of our algorithm is verified through the performance evaluation and the enhancement is up to about 13.5% in terms of the multicast delay variation. The time complexity of our algorithm is O(mn2) which is comparable to well known DDVCA.

Inspired from the idea of data representation in manifold learning, we derive a novel model which combines the original training images and their tangent vectors to represent each image in the testing set. Different from the previous methods, the L1 norm is used to control the reconstruction error. Considering the fact that the objective function in the proposed model is non-smooth, we utilize the majorization minimization (MM) method to solve the proposed optimization model. It is interesting to note that at each iteration a quadratic optimization problem is formulated and its analytical solution can be achieved, thereby making the proposed algorithm effective. Extensive experiments on face images demonstrate that our method achieves better performance than some previous methods.

Rapid development of modern communications has initiated essential requirements for providing efficient algorithms that can solve the routing and wavelength assignment (RWA) problem in satellite optical networks. In this paper, the bee colony algorithm optimization based on link cost for RWA (BCO-LCRWA) is tailored for satellite networks composed of intersatellite laser links. In BCO-LCRWA, a cost model of intersatellite laser links is established based on metrics of network transmission performance namely delay and wavelengths utilization, with constraints of Doppler wavelength drift, transmission delay, wavelength consistency and continuity. Specifically, the fitness function of bee colony exploited in the proposed algorithm takes wavelength resources utilization and communication hops into account to implement effective utilization of wavelengths, to avoid unnecessary over-detouring and ensure bit error rate (BER) performance of the system. The simulation results corroborate the improved performance of the proposed algorithm compared with the existing alternatives.

Aiming at the problem of the deterioration of the contact performance caused by the wear debris generated during the fretting wear of the electrical connector, low-frequency fretting wear experiments were carried out on the contacts of electrical connectors, the accumulation and distribution of the wear debris were detected by the electrical capacitance tomography technology; the influence of fretting cycles, vibration direction, vibration frequency and vibration amplitude on the accumulation and distribution of wear debris were analyzed; the correlation between characteristic value of wear debris and contact resistance value was studied, and a performance degradation model based on the accumulation and distribution of wear debris was built. The results show that fretting wear and performance degradation are the most serious in axial vibration; the characteristic value of wear debris and contact resistance are positively correlated with the fretting cycles, vibration frequency and vibration amplitude; there is a strong correlation between the sum of characteristic value of wear debris and the contact resistance value; the prediction error of ABC-SVR model of fretting wear performance degradation of electrical connectors constructed by the characteristic value of wear debris is less than 6%. Therefore, the characteristic value of wear debris in contact subareas can quantitatively describe the degree of fretting wear and the process of performance degradation.

Reconfigurable intelligent surface (RIS) has the capability of boosting system performance by manipulating the wireless propagation environment. This paper investigates a downlink RIS-aided non-orthogonal multiple access (NOMA) system, where a RIS is deployed to enhance physical-layer security (PLS) in the presence of an eavesdropper. In order to improve the main link's security, the RIS is deployed between the source and the users, in which a reflecting element separation scheme is developed to aid data transmission of both the cell-center and the cell-edge users. Additionally, the closed-form expressions of secrecy outage probability (SOP) are derived for the proposed RIS-aided NOMA scheme. To obtain more deep insights on the derived results, the asymptotic performance of the derived SOP is analyzed. Moreover, the secrecy diversity order is derived according to the asymptotic approximation in the high signal-to-noise ratio (SNR) and main-to-eavesdropper ratio (MER) regime. Furthermore, based on the derived results, the power allocation coefficient and number of elements are optimized to minimize the system SOP. Simulations demonstrate that the theoretical results match well with the simulation results and the SOP of the proposed scheme is clearly less than that of the conventional orthogonal multiple access (OMA) scheme obviously.

Frame synchronization detection before data transmission is an important module which directly affects the lifetime and coexistence of underwater acoustic communication (UAC) networks, where linear frequency modulation (LFM) is a frame preamble signal commonly used for synchronization. Unlike terrestrial wireless communications, strong bursty noise frequently appears in UAC. Due to the long transmission distance and the low signal-to-noise ratio, strong short-distance bursty noise will greatly reduce the accuracy of conventional fractional fourier transform (FrFT) detection. We propose a multi-segment verification fractional fourier transform (MFrFT) preamble detection algorithm to address this challenge. In the proposed algorithm, 4 times of adjacent FrFT operations are carried out. And the LFM signal identifies by observing the linear correlation between two lines connected in pair among three adjacent peak points, called ‘dual-line-correlation mechanism’. The accurate starting time of the LFM signal can be found according to the peak frequency of the adjacent FrFT. More importantly, MFrFT do not result in an increase in computational complexity. Compared with the conventional FrFT detection method, experimental results show that the proposed algorithm can effectively distinguish between signal starting points and bursty noise with much lower error detection rate, which in turn minimizes the cost of retransmission.