Samaresh Bera

In next-generation consumer electronics (NGCEs), advanced CE devices heavily rely on machine learning and artificial intelligence (AI), where data and models are the basic building blocks for making automated decisions and optimized services. Nowadays, generative adversarial networks has led to model inversion as well as model/data poisoning attacks in NGCEs that can significantly degrade the model performance and accuracy. In addition, the majority of the CEs data are collected through vulnerable wireless public channels, posing significant security challenges to data privacy, integrity, and confidentiality. However, traditional authentication schemes relying on the discrete logarithm problem (DLP) have been utilized form the past few decades for securing CE’s data. With the advancement of quantum computing and the adaptation of the quantum Shor’s algorithm, these schemes become no more secure and therefore vulnerable to quantum attacks. Moreover, the wireless CEs applications are vulnerable to impersonation attacks by malicious entities. To overcome these challenges, we propose a radio frequency fingerprint (RFF)-based adversarial attack detection and mitigation with a lightweight quantum-secure authentication and key agreement protocol for securing CEs communication. We utilize a vector similarity search technique for attack detection and unauthorized access using the CE’s RFF. A comprehensive performance analysis and comparative analysis presents its efficiency and scalability for CE applications. A testbed experiment using Raspberry Pi, verification using the Scyther tool, and performance under unknown attacks shows the practicability, correctness, and robustness of the proposed scheme. The network performance using NS-3 highlights the novelty of the proposed scheme.

The emergence of new applications, such as online gaming and virtual reality, necessitates the underlying network capable of fulfilling high bandwidth and low latency requirements. The software-defined multipath routing is a viable approach to fulfill such quality-of-service (QoS) requirements by improving the data delivery performance through multipath. In this paper, we propose a QoS-aware dynamic multipath routing scheme for enhancing QoS of high-bandwidth applications in an SDN-enabled network. The proposed scheme consists of three phases – flow splitting, multipath routing, and flow reordering. In the first phase, we propose a flow splitting scheme to decide how to split the incoming flows to enable multipath routing in the network. In the second phase, we design a cost function for routing the splittable subflows and formulate a min-cost routing problem as an integer linear program (ILP). To solve the problem in polynomial time, we propose a greedy heuristic approach. Finally, in the third phase, we propose a flow reordering scheme for the received subflows through multiple paths to maintain the desired flow sequence at the destination. The experimental results show that the proposed scheme achieves higher network throughput by 22% compared to the benchmark schemes. Further, the proposed scheme achieves a reduction in QoS violated flows by 24% compared to the benchmark schemes.

The deployment of software-defined wireless local area networks (SDWLANs) in hostile regions requires solar energy harvesting-enabled battery-powered access points (APs). The aforementioned APs replenish batteries and forward data flows by avoiding the problem of frequent energy outages. In this work, we propose a scheme called SeamFlow to place APs in energy harvesting mode without disrupting the forwarding of flows. In contrast to previous works, we focus not only on the energy replenishment of battery, but also, seamless forwarding of flows without violating flow constraints. Specifically, we formulate SeamFlow as a two-phase optimization problem – a) optimal selection of APs and b) finding forwarding paths. As solving both optimization problems are NP-hard, we propose two greedy-heuristic schemes to solve them in polynomial time. Extensive simulation results show that proposed scheme reduces flow forwarding cost by 24% and 16%, per-flow energy consumption by 27% and 21%, and flow violations by 92% and 86% compared to the benchmarks using random and grid topologies, respectively.

The next-generation cyber-physical systems (CPS) with heterogeneous applications have diverse quality-of-service (QoS) requirements in terms of throughput, end-to-end latency, and packet drop reliability. To meet such diverse QoS requirements, in this paper, we propose a QoS-aware traffic forwarding scheme in software-defined CPS. The proposed scheme is presented as a two-stage optimization framework to minimize the associated costs in traffic forwarding. In the first stage, we aim to minimize the required number of ‘candidate’ switches for a given network to minimize network deployment costs. In the second stage, we design a comprehensive cost function considering end-to-end delay, flow-rule utilization, and link utilization in the network. Based on the designed cost function, we formulate another optimization problem for optimal traffic forwarding (OTF). As solving OTF is NP-hard, we propose an efficient greedy-heuristic approach to solve the problem while considering application-specific QoS requirements. Further, we propose a packet-tagging method to assist the controller in mitigating rule congestion at the software-defined networking devices, and hence improve the overall network performance. Extensive results show that the proposed scheme minimizes the network delay and QoS-violated flows by up to 50% and 90%, respectively, compared to the state-of-the-art schemes.

Software-defined IoT (SDIoT) is a promising approach to address the requirements of the Internet of Things (IoT), such as network management, Quality of Service (QoS), and resource utilization. The advantages of SDIoT are facilitated by the separation of the data- and the control-planes using flow-rules , that allow fine-grained control over individual flows. However, the number of flow-rules that can be placed at the switches is limited, leading to scalability issues in SDIoT. Existing approaches to flow-rule management either do not consider the impact on QoS or are applicable only to a particular topology. In this article, we propose a QoS-aware flow-rule aggregation scheme for generic network topologies, which aims to achieve a satisfactory tradeoff among flow-rule compression and its impact on the QoS of IoT traffic flows. Specifically, the proposed scheme adaptively aggregates flow-rules while considering different QoS requirements of IoT applications in the network, and the flow-rule capacity of the switches. The proposed scheme consists of the following components—1) a path selection heuristic to increase the total number of flow-rules that can be accommodated in the network and 2) a multiarm bandit-based flow-rule aggregation scheme capable of reducing the number of flow-rules, while maintaining adequate performance in terms of QoS. Experimental results using IoT traffic show that, on average, the proposed scheme is capable of reducing the average end-to-end delay and QoS-violated flows in the network by 22% and 30%, respectively, compared to the state-of-the-art schemes.

In this paper, we propose a dynamic controller assignment scheme while considering flow-specific requirements, with an aim to minimize controller response time in softwaredefined networks (SDN). We adopt the concept of FlowVisor, in which a virtualized platform acts as a manager between control- and data planes of SDN architecture. The proposed scheme consists of two phases – adaptive window selection and controller assignment. In the window selection phase, the virtualized manager determines time to wait before incoming flows can be assigned to controllers in adaptive manner. Based on the adaptive window size, the flows are assigned to the controllers in the second phase. We use dynamic stable-matching game to assign flows to controllers, while defining their preference lists. The extensive simulation results show that the proposed scheme is capable of minimizing controller response time by 31%, 39%, and 37% compared to the state-of-the-art schemes – simple stablematching (SM), static assignment (Static), and minimum quota processing (MQP), respectively. Further, the proposed scheme also reduces the percentage of QoS violated flows in the network by 72%, 73%, and 74% compared to SM, Static, and MQP, respectively.

In this paper, we propose an adaptive flow-rule placement scheme, FlowStat, in software-defined network (SDN) with an aim to provide per-flow statistics to SDN controller, while enhancing overall network performance. The proposed scheme consists of three phases — forwarding path selection, flow-rule placement, and rule redistribution. In the first phase, we formulate a max-flow-min-cost optimization problem to determine optimal forwarding paths while considering multi-commodity flows with heterogeneous requirements. In the second phase, an integer linear programming (ILP) problem is formulated to decide forwarding rules for paths computed in the first phase, so that total number of exact-match is minimized. As finding optimal solution to the problems is NP-hard, we propose two greedy heuristic approaches to solve the problems in polynomial time. Finally, we propose a rule redistribution scheme on detecting rule congestion at a switch, in order to accommodate new flows in the network. Extensive experimental results show that the proposed scheme, FlowStat, is capable of providing per-flow statistics to the SDN controller, while enhancing network performance compared to existing schemes — ReWiFlow and ExactMatch. In particular, FlowStat is capable of reducing end-to-end delay and QoS violation by 46% and 75% (approx.), respectively, compared to the ReWiFlow and ExactMatch schemes, while providing 85% accurate per-flow statistics to the SDN controller.

In this paper, we propose a QoS-aware adaptive flow-rule aggregation scheme in software-defined IoT (SDIoT) network with an aim to address flow-table overflow problem in SDN switches. The proposed scheme uses a key-based mechanism that is capable of fast aggregation and provides sufficient reduction in the number of flow rules, while having minimal impact on the QoS of IoT traffic. Further, we observe that it is necessary to adequately select a QoS path from multiple candidate paths, while considering the flow-table utilization at the switches. Accordingly, we present the Best-fit heuristic which takes into account the number of flow-rule insertions along with the bottleneck rule-capacity switch on a path, in order to minimize the total number of flow-rules in the network. Experimental results show that the proposed scheme is capable of reducing the average delay and packet drop by 35% and 12%, respectively, and improving the average throughput by 20% compared to the existing delay-based flow-aggregation scheme, while having comparable performance in terms of rule-aggregation.