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Cognitive network management is becoming quintessential to realize autonomic networking. However, the wide spread adoption of the Internet of Things (IoT) devices, increases the risk of cyber attacks. Adversaries can exploit vulnerabilities in IoT devices, which can be harnessed to launch massive Distributed Denial of Service (DDoS) attacks. Therefore, intelligent security mechanisms are needed to harden network security against these threats. In this paper, we propose Chronos, a novel time-based anomaly detection system. The anomaly detector, primarily an Autoencoder, leverages time-based features over multiple time windows to efficiently detect anomalous DDoS traffic. We develop a threshold selection heuristic that maximizes the F1-score across various DDoS attacks. Further, we compare the performance of Chronos against state-of-the-art approaches. We show that Chronos marginally outperforms another time-based system using a less complex anomaly detection pipeline, while out classing flow-based approaches with superior precision. In addition, we showcase the robustness of Chronos in the face of zero-day attacks, noise in training data, and a small number of training packets, asserting its suitability for online deployment. [ABSTRACT FROM AUTHOR]

Summary: One of the best methods for estimating the softmax layer in neural network language models is the noise‐contrastive estimation (NCE) method. However, this method is not proper for word embedding applications compared with some other robust methods such as the negative sampling (NEG) method. The NEG method implements the pointwise mutual information (PMI) relation between the word‐context space in the neural network, and the NCE method implements conditional probability. Both the NCE and NEG methods use dot‐product‐based mapping to map words and contexts vector to the probabilities. This article presents the parametric objective function, which uses the mapping function as the parameter. Also, we obtained a parametric relation between word‐context space according to the mapping parameter. Using the parametric objective function, we identify conditions for a mapping that make it a proper selection for both softmax estimation and word embedding. The article also presents two specific mapping functions with the required conditions, and we compared their performance with that of the dot‐product mapping function. The performance of the new mapping functions is also reported over common word embedding and language models' benchmarks. [ABSTRACT FROM AUTHOR]

An unsupervised method is proposed for link activation in wireless networks by identifying clusters of interfering users. A k-nearest neighbors interference graph is first defined for the wireless network which is then mapped to a stochastic latent space. The users are then clustered in the latent space using a Gaussian mixture model, and one user from each interfering cluster is activated while the rest of the users in that cluster remain idle. The proposed framework is scalable, works across several network topologies such as device to device (D2D), and is close to the optimal solution in performance. [ABSTRACT FROM AUTHOR]

In this paper, distributed heterogeneous wireless networks are studied and a spectrum access scheme that fairly allocates channels among users based on their request levels is investigated. To be closer to what happens in practice, users' request levels to have different priorities are also considered, i.e. some packets are more important for the user to be transmitted than the others. Due to the distributed nature of the problem and since users are not able to coordinate with each other before transmission, a scheme based on reinforcement learning is proposed in which the network state is estimated based on the previous successful transmissions of packets in the network. The reinforcement learning method's adaptive nature lets our method work in heterogeneous settings where there exist users with other medium access control protocols. The performance of the proposed scheme is evaluated in different network settings, and it is shown that it tries to implement a fair spectrum access policy (considering the packets' priorities) while maximising the total throughput of the network. [ABSTRACT FROM AUTHOR]

Objects in an environment affect electromagnetic waves. While this effect varies across frequencies, there exists a correlation between them, and a model with enough capacity can capture this correlation between the measurements in different frequencies. In this paper, we propose the Wi2Vi model for associating variations in the WiFi channel state information with video frames. The proposed Wi2Vi system can generate video frames entirely using CSI measurements. The produced video frames by the Wi2Vi provide auxiliary information to the conventional surveillance system in critical circumstances. Our implementation of the Wi2Vi system confirms the feasibility of constructing a system capable of deriving the correlations between measurements in different frequency spectrums. [ABSTRACT FROM AUTHOR]

One intrinsic property of neural networks is making confident decisions because they do not capture uncertainty in training data. As a result, when Neural Networks (NN) are used in Deep Reinforcement Learning (DRL), agents cannot explore the action-space effectively. Bayesian Neural Networks (BNN) is one alternative that, instead of one value, assigns a probability distribution to the weights of NN. Using BNN as the policy network of an RL agent, the RL agent will have natural exploration capability. Recent studies demonstrate high potential for the application of RL methods in wireless networks. The inefficient exploration capability, however, limits their use cases. In this letter, we show how Bayesian RL agents can be used to solve complex wireless resource allocation problems. We consider the link-level throughput maximization that needs simultaneous power and Modulation/Coding Scheme (MCS) assignment to each user. We show that due to the large and sparse action-space, only Bayes-by-Backprop Q-network (BBQN) agents can find proper assignments. Simulation results show the performance of the proposed scheme in different network settings. [ABSTRACT FROM AUTHOR]

The first step of secure communication is authenticating users and detecting malicious ones. In recent years, some promising schemes have been proposed using wireless medium network's features, in particular, channel state information (CSI) as a means of authentication. These schemes mainly compare the user's previous CSI with the new received CSI to determine if the user is what it is claiming to be. Despite high accuracy, these approaches lack the stability in authentication when the users rotate in their positions. It is due to the significant change in CSI when a user rotates, which misleads the authenticator when it compares the new CSI with the previous ones. Our approach presents a way of extracting features from raw CSI measurements, which are stable with respect to rotation. We employ a deep neural network to extract these features. We also present a scenario in which users can be efficiently authenticated while they are at specific locations in an environment (even if they rotate) and will be rejected if they change their location. In addition, experimental results are presented to show the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation. [ABSTRACT FROM AUTHOR]

A distributed random access network with K users and one Access Point (AP) is considered. It is assumed that users and the AP are equipped with M and N antennas, respectively. Each user independently decides whether to transmit in a time slot or not. We initially focus on two-user random access networks and characterize the network average Degrees of Freedom (DoF). For the K-user networks, an upper-bound on the network average DoF is first proposed. Then, it is shown that the proposed upper-bound can be achieved using single stream data transmission for many network configurations. Finally, we show through a few examples that there exist some network configurations where multi-stream data transmission in conjunction with interference alignment is necessary in order to achieve the upper-bound. [ABSTRACT FROM AUTHOR]

A number of recent source coding techniques compress the source signal into multiple layers such that a destination is able to reconstruct the original signal (with some distortion) even if it has not received all the layers. Implementation of such source coding techniques in wireless networks requires the application of coding mechanisms (such as multilayer coding) which allow unequal error protection for different layers of the transmitted data. In this paper, we study the performance of multilayer coding for quasi-static fading channels where the source and the destination are equipped with multiple antennas and the Channel-State-Information (CSI) is only known at the destination. We limit the study to the scenarios where the destination is only able to perform successive-decoding (joint-decoding is not possible) and the objective is to find the design of a multilayer code which maximizes the average data rate received at the destination. To this end, we first propose a design rule for constructing a proper multilayer code for Multiple-Input-Multiple-Output (MIMO) networks. Furthermore, the paper presents a procedure which uses the proposed design rule to determine the parameters of the multilayer code. The performance of the designed multilayer coding scheme is then studied for different network setups. [ABSTRACT FROM AUTHOR]

This paper considers a two-hop network in which information is transmitted from a source via a relay to a destination. It is assumed that channels are quasi-static fading with additive white Gaussian noise and that all nodes are equipped with a single antenna. The channel state information (CSI) of each hop is available only at the corresponding receiver and relay is not capable of data buffering over multiple coding blocks. One commonly used design criterion in such configurations is the maximization of the average received rate at the destination. Considering infinite-layer coding at both the source and the relay, in conjunction with decode and forward strategy at the relay, we present a procedure to optimally distribute the available source and relay powers to different layers of their corresponding codes. Next, we demonstrate how this transmission technique can be generalized to a multihop setting. Assuming Rayleigh fading, the performance of the proposed coding scheme is evaluated for a two-hop network and compared with the performance of previously known strategies. [ABSTRACT FROM PUBLISHER]

For gathering data in a crowdsensing network, we need to support the connectivity of a massive number of low power devices. In this paper, a new multiple access technique is proposed for such networks with a large number of devices/sensors/users where we have taken advantage of the fact that these devices usually do not become active at the same time, which we refer to as sparse activation property. In this scheme, each device uses a specific spreading code which is changing at symbol level. Symbol‐level change of the spreading code and the random nature of user activation are the two building blocks that provide opportunistic low interference between data transmitted by devices, which are activated in a specific time slot. We also discuss how the proposed scheme can be used in grant free schemes which eliminate the need for heavy signaling before data transmission. The performance of the proposed scheme has been evaluated in different scenarios, and the block error rates are compared with a few other multiple access schemes. [ABSTRACT FROM AUTHOR]

It is known that the achievable data rate per user can be increased when relays are deployed in wireless networks. However, the drawback of this solution is that some of the network's resources should be allocated to the relays. In this paper, we consider a two-tier network in which all users send or receive data in two hops. By applying vector quantization, we compute the relays' locations to improve network's average transmission rate. These locations are also computed analytically when the number of relays is less than six. Having determined the relays' locations, the network's average transmission rate is evaluated. Subsequently, we define the "neutrality-surface" such that the performance of any relay network operating below this surface is inferior to that of the same network without relays. Finally, we study the relative relaying gain for different network configurations. [ABSTRACT FROM PUBLISHER]