The smart grid is a modern electricity network that incorporates sophisticated communication and control technologies to improve efficiency, reliability, and security. This study presents a secure, lightweight authentication and key agreement scheme for multi-layer Internet of Things (IoT)-enabled smart grid systems, targeting the essential requirement to safeguard industrial control networks against cyber threats. The proposed scheme incorporates two security mechanisms to improve authentication and communication security in smart grid systems. Physical Unclonable Functions (PUFs) facilitate secure device authentication between smart meters and the Master Terminal Unit (MTU) by utilizing hardware-level uniqueness, thereby ensuring resilience against cloning attacks. Additionally, post-quantum lattice-based Ring Learning with Errors (RLWE) hard problem ensures secure communication between the MTU and the Remote Terminal Unit (RTU), providing quantum-resistant security and efficient key exchange. The framework’s resilience is rigorously evaluated with the ProVerif security verification tool to confirm its strength against advanced cyberattacks. A formal security analysis quantitatively assesses the cryptographic robustness of the scheme, whereas an informal security analysis examines its ability to withstand practical cyber threats, including replay attacks, man-in-the-middle (MITM) attacks, and device impersonation in actual smart grid environments.

Low-rate Denial of Service (LDoS) attacks use short, regular bursts of traffic to exploit vulnerabilities in network protocols. They are a major threat to network security, especially in Software-Defined Networking (SDN) frameworks. These attacks are challenging to detect and mitigate because of their low traffic volume, making it impossible to distinguish them from normal traffic. We propose a real-time LDoS attack detection and mitigation framework that can protect SDN. The framework incorporates a detection module that uses a deep learning model, such as a Generative Adversarial Network (GAN), to identify the attack. An efficient mitigation module follows detection, employing mechanisms to identify and filter harmful flows in real time. Deploying the framework into SDN controllers guarantees compliance with OpenFlow standards, thereby avoiding the necessity for additional hardware. Experimental results demonstrate that the proposed system achieves a detection accuracy of over 99.98% with an average response time of 8.58 s, significantly outperforming traditional LDoS detection approaches. This study presents a scalable, real-time methodology to enhance SDN resilience against LDoS attacks.

Network traffic has changed significantly since the introduction of 5G technology. The massive flow of connected devices as well as new applications are causing a variety of traffic patterns, quality of service (QoS) requirements and scalability challenges. The solution to these problems is network slicing which enables operators to distribute resources and set network characteristics per slice by constructing multiple virtual network slices within the shared 5G infrastructure. Resources are reserved for each slice for some time period. Traffic prediction with high accuracy is of great importance in the dynamic environment of 5G networks for resource planning and scheduling as well as for reliable and effective transmission of network data. In this paper, We generate user requests in a slice having arrival and departure rates using Poisson distribution. We did a comparative analysis of traffic prediction and resource allocation using deep learning models such as Long Short Term Memory(LSTM), Bidirectional LSTM(BiLSTM), Stacked LSTM and Gated Recurrent Unit(GRU).

Network Functions Virtualization (NFV) promises programmability and cost savings by replacing hardware middleboxes with more flexible Virtual Network Functions (VNFs) on commodity servers. But, the current virtualization technologies do not fully isolate the system resources like Last Level Cache (LLC) and Memory Bandwidth (MB); therefore, co-location of VNFs on the same commodity server causes interference effects which might severely impact the performance of VNFs in terms of throughput, latency, etc. Contention at LLC is one of the root causes of this performance degradation and it is addressed by LLC resource partitioning. But, it remains unexplored the impact of both LLC and MB on VNF performance. In this work, we investigate the importance of MB partitioning along with LLC partitioning to achieve performance isolation in NFV-based systems. Allocating these system resources among co-located VNFs to meet Service Level Agreements (SLAs) is challenging due to the dynamic nature of traffic and varying functionality of VNFs. In this work, we formulate the resource allocation problem as an Integer Linear Programming (ILP) for maximizing the number of accepted VNF requests with SLA guarantees. Since the problem is NP-hard, we present a polynomial time$\epsilon $-approximation scheme. Further, we propose a heuristic approach namedNFVPermit, a resource manager for NFV-based systems that tries to ensure performance isolation among co-located VNFs based on their current traffic rates and SLA requirements. Through extensive experiments, we show howNFVPermitoutperforms state-of-the-art and baseline approaches.

In a virtualized software defined network (VSDN), PACKET_IN messages of switches must pass through the hypervisor in order to reach the corresponding controller. Hence, the latency experienced by a network element is the sum of latency from network element to hypervisor and the latency from the hypervisor to the controller corresponding to the network element. Therefore, the locations of both the hypervisors and controllers determine the latency of network elements in a virtualized environment. In this paper, we propose a strategy for determining the placement of controllers in a VSDN while fixing the hypervisor(s) in the physical network. We also propose an approach for jointly optimizing the placement of hypervisors and controllers in a VSDN. The objective is to minimize the worst case latency between the network element and its corresponding controller. Furthermore, we propose a generalized model which can be used not only to optimize the worst case latency, but also to optimize other objectives such as the average latency, the maximum average latency, and the average maximum latency. The proposed problems are formulated as integer linear programs. We evaluated the performance of our proposed strategies using the AT&T network of Internet Topology Zoo and the Internet 2 OS3E topology, and compared with the hypervisor placement problem. Evaluations demonstrate that the proposed methods outperform the existing hypervisor placement approach with respect to the various performance metrics.

Software defined networking shifts the control plane of forwarding devices to one or more external entities known as controllers. Determining the optimal location of controllers in the network and the assignment of switches to them is widely known as controller placement problem. In case of controller failures, the switches are disconnected from the controller until they are reassigned to other active controllers with enough spare capacity. However, there is a significant upsurge in the worst case latency after the reassignment due to lack of planning for controller failures. In this paper, we propose a controller placement strategy that not only considers reliability and capacity of controllers but also plans ahead for controller failures to avoid repeated administrative intervention, drastic increase in latency and disconnections. It is formulated as a mixed integer linear program. The objective is to minimize the maximum, for all switches, of the sum of the latency from the switch to the nearest controller with enough capacity (first reference controller) and the latency from the first reference controller to its closest controller with enough capacity (second reference controller). We also proposed a generalized model which can be used to minimize the average latency and extended it for multiple controller failures. Furthermore, we presented a simulated annealing heuristic that efficiently solves the problem on large scale networks. The proposed formulation and heuristic are evaluated on various networks from the Internet Topology Zoo. Simulation results show that our proposed method performs better than the controller placement that does not plan ahead for failures.