Annals of Telecommunications最新文献

One of the most important challenges of this century will be to minimise as much as possible the energy consumption of the worldwide digital infrastructure to have a significant contribution on our emissions of CO₂ reduction since energy consumption and emission of CO₂ are directly linked. Therefore, after an introduction (part 1), in part 2 of this paper, we will describe the status of the worldwide production of electricity, the contribution of information and communications technology (ICT) in terms of electricity consumption, and the identification of the critical network segments that can have a significant environmental impact. In part 3, we will focus on the data centres and core services that represent important network segments responsible for the largest emission of CO₂. In part 4, we will address the access and aggregation part, which represents the second important network segment to optimise. Part 5 will focus on the home networking and enterprise. And before an estimation of the energy savings obtained when adopting the innovations proposed, the impact of the vertical market will be discussed in part 6. Finally, the conclusion (part 7) will summarise the results and perspectives will be proposed to complete the analysis.

Aiming at the problem of beamforming performance degradation under the coherent signals model, this paper proposes an adaptive beamforming algorithm based on the virtual array. Compared with previous work, the creative construction of virtual arrays in this paper allows the algorithm to ensure strong coherent signal processing and superior output performance with no degradation in coherence capability. The proposed algorithm firstly constructs a virtual array symmetric to the physical array to form a virtual antenna array model; secondly, a full-rank covariance matrix is obtained by matrix reconstruction; then, the direction vector and power of the signals are estimated; finally, the estimated parameters are used to reconstruct the interference plus noise covariance matrix (INCM) and calculate the weight vector. Simulation analysis verifies the superiority of the algorithm and the validity of theoretical analysis.

Abstract

Outstanding progress in unsolicited intrusions along with security threats, which interrupt the normal operations of wireless sensor networks (WSNs), have been attracted by the proliferation of WSNs and their applications. In WSNs, this demands an intrusion detection system (IDS), which can detect such attacks with higher detection accuracy. Designing an effective model for IDS using the SDK-LSHB-based SHAKE-ESDRL algorithm to improve accuracy and lessen training time and response time is the goal of this work. At first, duplicate removal, missing data removal, and data transfer are the steps through which the dataset was processed. From the processed data, by providing the extracted attributes as input to the entropy-based generalized discriminant analysis (E-GDA) method, the number of attributes is reduced. After that, the LogSwish-based deep reinforcement learning algorithm (LS-DRLA) method wielded the reduced attributes for intrusion detection (ID). By utilizing the SHAKE 256 algorithm, the attributes that fall into the attacked class label are hashed and stored in the hash table during this process. Next, to test the real-time data with the trained IDS, the WSN nodes are initialized. For this, by utilizing the supremum distance (SD-K-Means) algorithm, the sensor nodes (SNs) are clustered centered on the cluster heads (CHs) selected by the linear scaling-based honey badger optimization algorithm (LS-HBOA) method. At last, utilizing real-world-based datasets, the proposed algorithms are evaluated and the results are compared using statistical metrics.

Attribute-based encryption (ABE) allows users to encrypt and decrypt data based on attributes. It realizes fine-grained access control and can effectively solve the one-to-many encryption and decryption problem in open cloud application. Linear secret sharing scheme (LSSS) is the common access structure with a matrix on the attributes in ABE schemes, which may depict AND, OR, threshold operations, etc. However, LSSS access structure does not depict the complex and dynamic access policy of attributes, such as the complicated relationship of different attributes and the generation of dynamic attributes. It severely restricts the expansion of the practical application of ABE. Besides, there exists another problem; attribute authority (AA) in traditional ABE has a concentration of power and easily suffers from single-point failure or privacy leakage for being attacked or corrupted. Blockchain is a decentralized, tamper-free, traceable, and multi-party distributed database technology. Consortium blockchain (CB) is a partially centralized blockchain, whose openness is between the public blockchain and the private blockchain. In this paper, an ABE scheme on LSSS access structure with expressive dynamic attributes (EDA) based on CB (LSSS-EDA-ABE-CB) was proposed to resolve the above issues. EDA can construct the comprehensive attribute calculation expressions by conducting various operations, such as arithmetic operations, relational operations, and string operations. In virtue of the application of EDA, the proposed scheme can reconstruct new composite attributes to realize the dynamic adjustment of attributes. A partitioning method of EDA avoids one attribute appearing in two different EDA expressions. The CB technology enhanced the authority and trustworthiness of AA by openly recording AA’s attribute key distributions in CB transactions. The scheme in the paper was proven CPA-secure under the decision q-PBDHE assumption in standard model in the CB application environment. The scheme provides a more general data access policy and maintains the fine-grained character of ABE simultaneously. Finally, the security and performance analysis shows that the proposed scheme is secure and highly efficient.

In an ambient backscatter communication system, the waves generated by a source are reflected by a tag, in a variable manner in time. Therefore, the tag can transmit a message to a reader, without generating any radio wave and without battery. As a consequence, such a communication system is a promising technology for ultra-low energy wireless communications. In the simplest implementation of such a system, the tag sends a binary message by oscillating between two states and the reader detects the bits by comparing the two distinct received powers. In this paper, for the first time, we propose to analyze the impact of the shape of diffusing flat panel surfaces that diffuse in all directions, on an ambient backscatter communication system. We establish the analytical closed form expression of the power contrast in the presence of flat panels, by considering a rectangular surface and a disk-shaped surface, and we show that diffusing surfaces improve the power contrast. Moreover, our approach allows us to express the contrast to noise ratio, and therefore to establish the BER performance. Furthermore, we show that it makes it possible to improve the energetic performance, thanks to diffusing surfaces. For any configuration characterized by a fixed source, tag and reader, we moreover determine the precise locations of diffusing surfaces, which induce a maximum efficiency of the surfaces, whatever the wavelength. Furthermore, we show that it becomes possible to easily determine an optimal frequency which maximizes the contrast power, thanks to the expression of the contrast power.

In recent years, malicious insider attacks have become a common fraudulent activity in which an attacker is often perceived as a trusted entity in Social Networks (SNs). At present, machine learning (ML) approaches are widely used to identify the behavior of users in the network. From this perspective, this paper presents an integrated approach, namely, Social network malicious insider detection (SID), which consists of long short-term memory (LSTM) and time-based trust evaluation (TBTE). The proposed SID aims to identify deviations in SN user behavior by monitoring their data. The proposed SID uses LSTM, an advanced version of the recurrent neural network (RNN), which precisely predicts the behavior of users and identifies the anomaly pattern in SNs. A time-based trust evaluation method is integrated with LSTM, which not only differentiates the abnormal behavior of SN users but also precisely categorizes an anomaly node as a malicious node, a new user or a broken node. Moreover, the proposed SID detects insiders accurately and reduces false alarms by providing a novel quantitative analysis for computing the balancing factor according to time, which avoids the misinterpretation of normal user patterns as anomalies. The performance of the proposed SID is evaluated in real time, which demonstrates that the detection accuracy for attacks is 96% for normal users and 98% for new users with a smaller time span.

Runtime enforcement ensures the respect of a user-specified security policy by a program by providing a valid replacement for any misbehaving sequence of events that may occur during that program’s execution. However, depending on the capabilities of the enforcement mechanism, multiple possible replacement sequences may be available, and the current literature is silent on the question of how to choose the optimal one. Furthermore, the current design of runtime monitors imposes a substantial burden on the designer, since the entirety of the monitoring task is accomplished by a monolithic construct, usually an automata-based model. In this paper, we propose a new modular model of enforcement monitors, in which the tasks of altering the execution, ensuring compliance with the security policy, and selecting the optimal replacement are split into three separate modules, which simplifies the creation of runtime monitors. We implement this approach by using the event stream processor BeepBeep and a use case is presented. Experimental evaluation shows that our proposed framework can dynamically select an adequate enforcement actions at runtime, without the need to manually define an enforcement monitor.

Abstract