Neda Aboutorab

In this paper, we present a real-life, cost-effective implementation of physical layer security (PLS) using friendly jamming (FJ) and the IEEE 802.11 technology. Our approach is based on a recent development in software-defined networking (SDN) called spectrum programming, where a network controller can execute an intelligent access point (AP) selection algorithm to connect the user station to the AP that provides the most secrecy while exploiting idle APs as jammers. Considering a system with two APs, our first contribution is a theoretical optimization of the power of FJ based on system power parameters, as well as distances between the two APs and the user station and the eavesdropper station. Our second contribution is demonstrating not only that PLS can be implemented with commercial-off-the-shelf Wi-Fi devices, but also that our theoretical network-centric approach allows for a significant increase of secrecy capacity and secrecy coverage by applying FJ. Our experiments show that the theoretical optimization of the transmit power of the jamming AP is valid in practice, effectively maximizing the throughput gap between the user and the eavesdropper. To our best knowledge, this is the first work linking information-theoretic optimization of FJ in PLS to a real-world implementation, which is compatible with Wi-Fi standards and devices.

Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. Cooperative Friendly Jamming (CFJ) is among the PLS techniques that have received attention in recent years. However, finding an optimal transmit power allocation that results in the highest secrecy is a complex problem that becomes more difficult to address as the size of the wireless network increases. In this paper, we propose an optimization approach to achieve CFJ in large Wi-Fi networks by using a Reinforcement Learning Algorithm. Obtained results show that our optimization approach offers better secrecy results and becomes more effective as the network size and the density of Wi-Fi access points increase.

Insufficiency of linear coding for the network coding problem was first proved by providing an instance which is solvable only by nonlinear network coding (Dougherty et al., 2005).Based on the work of Effros, et al., 2015, this specific network coding instance can be modeled as a groupcast index coding (GIC)instance with 74 messages and 80 users (where a message can be requested by multiple users). This proves the insufficiency of linear coding for the GIC problem. Using the systematic approach proposed by Maleki et al., 2014, the aforementioned GIC instance can be cast into a unicast index coding (UIC) instance with more than 200 users, each wanting a unique message. This confirms the necessity of nonlinear coding for the UIC problem, but only for achieving the entire capacity region. Nevertheless, the question of whether nonlinear coding is required to achieve the symmetric capacity (broadcast rate) of the UIC problem remained open. In this paper, we settle this question and prove the insufficiency of linear coding, by directly building a UIC instance with only 36users for which there exists a nonlinear index code outperforming the optimal linear code in terms of the broadcast rate.

In this paper, we propose a new scalar linear coding scheme for the index coding problem called update-based maximum column distance (UMCD) coding scheme. The central idea in each transmission is to code messages such that one of the receivers with the minimum size of side information is instantaneously eliminated from unsatisfied receivers. One main contribution of the paper is to prove that the other satisfied receivers can be identified after each transmission, using a polynomial-time algorithm solving the well-known maximum cardinality matching problem in graph theory. This leads to determining the total number of transmissions without knowing the coding coefficients. Once this number and what messages to transmit in each round are found, we then propose a method to determine all coding coefficients from a sufficiently large finite field. We provide concrete instances where the proposed UMCD coding scheme has a better broadcast performance compared to the most efficient existing coding schemes, including the recursive scheme (Arbabjolfaei and Kim, 2014) and the interlinked-cycle cover (ICC) scheme (Thapa et al., 2017). We prove that the proposed UMCD coding scheme performs at least as well as the MDS coding scheme in terms of broadcast rate. By characterizing two classes of index coding instances, we show that the gap between the broadcast rates of the recursive and ICC schemes and the UMCD scheme grows linearly with the number of messages. Then, we extend the UMCD coding scheme to its vector version by applying it as a basic coding block to solve the subinstances.

It has been known that the insufficiency of linear coding in achieving the optimal rate of the general index coding problem is rooted in its rate’s dependency on the field size. However, this dependency has been described only through the two well-known matroid instances, namely the Fano and non-Fano matroids, which, in turn, limits its scope only to the fields with characteristic two. In this paper, we extend this scope to demonstrate the reliance of linear index coding rate on fields with characteristic three. By constructing two index coding instances of size 29, we prove that for the first instance, linear coding is optimal only over the fields with characteristic three, and for the second instance, linear coding over any field with characteristic three can never be optimal. Then, a variation of the second instance is designed as the third index coding instance of size 58. For this instance, it is proved that while linear coding over any field with characteristic three cannot be optimal, there exists a nonlinear code over the fields with characteristic three, which achieves its optimal rate. Connecting the first and third index coding instances in two specific ways, called no-way and two-way connections, will lead to two new index coding instances of size 87 and 91, for which linear coding is outperformed by nonlinear codes. Another main contribution of this paper is the reduction of the key constraints on the space of the linear coding for the first and second index coding instances, each of size 29, into a matroid instance with the ground set of size 9, whose linear representability is dependent on the fields with characteristic three. The proofs and discussions provided in this paper through using these two relatively small matroid instances will shed light on the underlying reason causing the linear coding to become insufficient for the general index coding problem.

It has been known that the insufficiency of linear coding in achieving the optimal rate of the general index coding problem is rooted in its rate's dependency on the field size. However, this dependency has been described only through the two well-known matroid instances, namely the Fano and non-Fano matroids, which, in turn, limits its scope only to the fields with characteristic two. In this paper, we extend this scope to demonstrate the reliance of linear index coding rate on fields with characteristic three. By constructing two index coding instances of size 29, we prove that for the first instance, linear coding is optimal only over the fields with characteristic three, and for the second instance, linear coding over any field with characteristic three can never be optimal. Then, a variation of the second instance is designed as the third index coding instance of size 58. For this instance, it is proved that while linear coding over any field with characteristic three cannot be optimal, there exists a nonlinear code over the fields with characteristic three, which achieves its optimal rate. Connecting the first and third index coding instances in two specific ways, called no-way and two-way connections, will lead to two new index coding instances of size 87 and 91, for which linear coding is outperformed by nonlinear codes. Another main contribution of this paper is the reduction of the key constraints on the space of the linear coding for the first and second index coding instances, each of size 29, into a matroid instance with the ground set of size 9, whose linear representability is dependent on the fields with characteristic three. The proofs and discussions provided in this paper through using these two relatively small matroid instances will shed light on the underlying reason causing the linear coding to become insufficient for the general index coding problem.

In this paper, we propose a new scalar linear coding scheme for the index coding problem called update-based maximum column distance (UMCD) coding scheme. The central idea in each transmission is to code messages such that one of the receivers with the minimum size of side information is instantaneously eliminated from unsatisfied receivers. One main contribution of the paper is to prove that the other satisfied receivers can be identified after each transmission, using a polynomial-time algorithm solving the well-known maximum cardinality matching problem in graph theory. This leads to determining the total number of transmissions without knowing the coding coefficients. Once this number and what messages to transmit in each round are found, we then propose a method to determine all coding coefficients from a sufficiently large finite field. We provide concrete instances where the proposed UMCD coding scheme has a better broadcast performance compared to the most efficient existing coding schemes, including the recursive scheme (Arbabjolfaei and Kim, 2014) and the interlinked-cycle cover (ICC) scheme (Thapa et al., 2017). We prove that the proposed UMCD coding scheme performs at least as well as the MDS coding scheme in terms of broadcast rate. By characterizing two classes of index coding instances, we show that the gap between the broadcast rates of the recursive and ICC schemes and the UMCD scheme grows linearly with the number of messages. Then, we extend the UMCD coding scheme to its vector version by applying it as a basic coding block to solve the subinstances.Preliminary results of this paper are presented, in part, in Sharififar2021UMCDconference This paper has supplementary downloadable material available at http://ieeexplore.ieee.org, provided by the authors. The supplementary material includes Appendix proof:A-2 (proof of Proposition prop:A-2) and Appendix proof:thm-class-ICC (proof of Theorem thm:class-ICC-UMCD). Please contact the authors for further questions about this work The authors are with the School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia (Email:{a.sharififar, n.aboutorab, p.sadeghi}@unsw.edu.au).

Industrial Internet of Thing (IIoT) systems are considered attractive ransomware targets because they operate critical services that affect human lives and have substantial operational costs. The major concern is with brownfield IIoT systems since they have legacy edge systems that are not fully prepared to integrate with IoT technologies. Various existing security solutions can detect and mitigate such attacks but are often ineffective due to the heterogeneous and distributed nature of the IIoT systems and their interoperability demands. Consequently, developing new detection solutions is essential. Therefore, this paper proposes a novel targeted ransomware detection model tailored for IIoT edge systems. It uses Asynchronous Peer-to-Peer Federated Learning (AP2PFL) and Deep Learning (DL) techniques as a targeted ransomware detection algorithm. The proposed model consists of two modules: 1) Data Purifying Module (DPM) aims to refine and reconstruct a valuable and robust representation of data based on Contractive Denoising Auto-Encoder (CDAE), and 2) Diagnostic and Decision Module (DDM) is used to identify targeted ransomware and its stages based on Deep Neural Network (DNN) and Batch Normalization (BN). The main strengths of this proposed model include: 1) each edge gateway’s modules work cooperatively with its neighbors in an asynchronous manner and without a third party, 2) it deals with both homogeneous and heterogeneous data, and 3) it is robust against evasion attacks. An exhaustive set of experiments on three datasets prove the high effectiveness of the proposed model in detecting targeted ransomware (known and unknown attacks) in brownfield IIoT and the superiority over the state-of-the-art models.

Industrial Internet of Things (IIoT) is a high-value cyber target due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions. However, as they lack the capability to deal with an IIoT’s co-existing heterogeneity and interoperability, developing new universal security solutions to fit its requirements is critical. This is challenging due to the scarcity of accurate data about IIoT systems’ activities, connectivities, and attack behaviors. In addition, owing to their multiplatform connectivity protocols and multivendor devices, collecting and creating such data are also challenging. To tackle these issues, we propose a holistic approach for generating an appropriate intrusion data set for an IIoT called X-IIoTID, a connectivity-agnostic and device-agnostic intrusion data set for fitting the heterogeneity and interoperability of IIoT systems. It includes the behaviors of new IIoT connectivity protocols, activities of recent devices, diverse attack types and scenarios, and various attack protocols. It defines an attack taxonomy and consists of multiview features, such as network traffic, host resources, logs and alerts. X-IIoTID is evaluated using popular machine and deep learning algorithms and compared with 18 intrusion data sets to verify its novelty.

This paper studies a Fog Radio Access Network (F-RAN) architecture that utilises the increasing storage and device-to-device communication capacities of users’ smart devices (referred to as F-UEs) in order to reduce the time that the central processing unit (referred to as BBU) has to spend to serve these F-UEs and thus, increases the system’s capacity. Indeed, these F-UEs can employ these capacities in cooperatively serving each other’s file requests, as long as the F-UEs have the files in their storage, rather than always receiving them through the BBU. In addition, network coding (NC) can be employed to minimize the communications among the F-UEs and from the BBU to further offload the BBU resources and reduce the energy consumed in F-UEs’ cooperation. This paper develops an algorithm for scheduling the file encoding and transmissions both among half-duplex F-UEs and from the BBU to minimise the consumed time-frequency resources from the BBU given constraints on the energy consumed by each F-UE for their cooperation to help preserve their battery life. The influence of the energy constraint on BBU offloading is investigated and the performance of the system under a variety of settings is evaluated through extensive simulations. The benefit of using NC is shown and the ability of the F-UEs to transmit with different power levels is also studied and shown to considerably impact the number of cooperating F-UEs and the BBU offloading.