Software-Defined Networks (SDN) is a new network paradigm that was developed to reduce complexity by controlling and managing the entire network from a centralized location. It is now widely used in various data center environments. Nevertheless, this type of network presents also vulnerabilities, which makes it prone to different cyber-attacks like DoS and DDoS, where the attackers seek to make servers or network resources unavailable temporarily or indefinitely, the problem becomes critical when the SDN controller is targeted. Moreover, machine learning methods have started to play an important role in most modern systems. Several contributions in the literature have addressed the problem related to intrusion detection based on Machine Learning (ML) techniques. Most of the published works use outdated datasets, which are not based on SDN environments like the KDD’99, NSL-KDD and Kyoto datasets, and that makes the proposed models not adapted to such network architectures. Recently, a new dataset called “InSDN” was published, it contains a history of various attacks generated in SDN-based networks. In this work, we proposed supervised learning models for detecting the DoS/DDoS attacks based on the InSDN dataset. The contribution in the paper goes through several steps including the preparation and preprocessing of the used dataset, best features selection, modeling, implementing, and evaluating some ML algorithms. Three classification models are proposed and evaluated, Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). Also, a comparison was done with some recent works on the InSDN dataset, and the results were very satisfying.

Fault diameter is an important parameter to measure the reliability and efficiency of interconnection networks. Strong product is an efficient method to construct large graphs from small graphs. In this paper, we study the fault diameter of strong product graph of [Formula: see text] paths. By recursion and mathematical induction, combined with the connectivity of the strong product graph of complete graph and connected graph, the connectivity of the strong product graph of [Formula: see text] paths is determined. By defining two [Formula: see text]-dimensional vectors [Formula: see text] and [Formula: see text] to construct internally vertex disjoint paths between any two vertices, the fault diameter of the strong product graph of [Formula: see text] paths is determined.

The exchanged hypercube is regarded as an attractive alternative to the hypercube. In this paper, we investigate the Hamiltonicity of the exchanged hypercube [Formula: see text] [Formula: see text]. We further show the conditional edge fault Hamiltonicity of the exchanged hypercube.

The connectivity of a network is an important indicator for assessing its reliability and fault-tolerability. In this paper, we study a novel measure, which is [Formula: see text]-faulty-block connectivity. Given a connected graph [Formula: see text] and a non-negative integer [Formula: see text], let [Formula: see text] and [Formula: see text] be connected subgraphs. Then, [Formula: see text] is called an [Formula: see text]-faulty-block of [Formula: see text] if [Formula: see text] disconnects [Formula: see text], and every remaining component of [Formula: see text] has at least [Formula: see text] nodes. The minimum cardinality over all [Formula: see text]-faulty-blocks of [Formula: see text] is called [Formula: see text]-faulty-block connectivity of [Formula: see text], denoted by [Formula: see text]. In this paper, we focus on the locally twisted cube [Formula: see text]. We study the [Formula: see text]-faulty-block connectivity of [Formula: see text] and show that [Formula: see text] for [Formula: see text], [Formula: see text] for [Formula: see text], and [Formula: see text] for [Formula: see text].

Networks derived from the hypercube are noted for their efficiency in simulating any bounded-degree communication network and hence they play a significant role in parallel machines. For a graph [Formula: see text], a geodetic cover is a subset [Formula: see text] of [Formula: see text], satisfying the condition that every vertex of [Formula: see text] other than the vertices in [Formula: see text] are covered using geodesics connecting any two vertices in [Formula: see text]. The geodetic number of [Formula: see text] is the least order of such covers in [Formula: see text]. This seminal distance-based parameter is well employed in the areas of location theory, convexity theory and game theory. The strong variant and its edge version are recent variations of the problem that were introduced in due of their applications in social networks. The most established and strong hypercube derivative networks are studied in this paper with respect to certain geodetic parameters. Notably, among the networks that we have considered, the butterfly and the Benes networks belong to the class of bipartite graphs for which the geodetic number problem and its strong version are NP-complete. We have determined the geodetic parameters that include the geodetic number, the strong geodetic number and their corresponding edge variants of the networks taken into consideration. In the process, we have identified a new class of graphs, as semi-extreme edge geodesic graphs and that the enhanced butterfly networks belong to this class. The study of such geodetic parameters in the structural models of parallel computation could be useful for the design of efficient algorithms for the structures considered.

The problem of finding the induced subgraph with the maximum number of edges among all subsets of a fixed cardinality is known as the maximum subgraph problem (MSP). The lexicographic order has been proven optimal for Cartesian products of specific regular graphs [B. Sergei, B. Pavle and K. Nikola, New infinite family of regular edge isoperimetric graphs, Theoretical Computer Science 721 (2018) 42–53]. This paper is dedicated to the precise calculation of edge values within the resulting subgraphs, enhancing our comprehension of their structural properties and implications. In this paper, we focus on solving the MSP of [Formula: see text] for any [Formula: see text] and for odd [Formula: see text], [Formula: see text] and [Formula: see text], where [Formula: see text] is obtained by the Cartesian product of a complete graph with [Formula: see text] vertices with a removal of a cycle on [Formula: see text] vertices. It has been observed that the graph obtained by deleting a cycle of length [Formula: see text] from a complete graph [Formula: see text] is isomorphic to a circulant graph [Formula: see text], while the particular case [Formula: see text] has been previously studied in [A. Syeda and M. Rajesh, MinLA of [Formula: see text] and its optimal layout into certain trees, The Journal of Supercomputing (2023), doi:10.1007/s11227-023-05140-3]. Our work extends the understanding of the MSP to a broader class of graphs with similar properties and applications.

The graph bipartization problem, arising from via minimization in VLSI design and related areas, consists in finding a vertex subset [Formula: see text] of graph [Formula: see text] such that the induced subgraph [Formula: see text] is bipartite and [Formula: see text] is maximized. The problem has been proved to be NP-hard even for planar graphs and cubic graphs. On the other hand, the study of polynomial-time algorithms for typical graph classes is significant in both theoretical and applied aspects. This paper focuses on several intersection graph classes, such as line graphs, circular-arc graphs, and directed path graphs. For the line graphs, we show the NP-hardness results in general and present the polynomial-time algorithms for special cases. For circular-arc graphs and directed path graphs, we propose algorithms that improve on the previously known ones.

Motivated by the problem of network monitoring, Foucaud, Krishna and Ramasubramony Sulochana introduced the concept of monitoring edge-geodetic set and a related graph invariant. A monitoring edge-geodetic set (MEG-set for short) is a set such that the removal of any edge changes the distance between some pair of vertices in the set. The minimum size of the monitoring edge-geodetic set is called the monitoring edge-geodetic number. In this paper, we study the monitoring edge-geodetic numbers of some well-known networks, including folded hypercube, folded [Formula: see text]-cube, prism graph, anti-prism graph, Jahangir graph and windmill graphs.

Finding the values of [Formula: see text] and [Formula: see text] is a fundamental problem in classical coding theory. The [Formula: see text] is the size of the maximum independent set of [Formula: see text] which is the induced subgraph of vectors of weight [Formula: see text] of the [Formula: see text]-power of [Formula: see text]-dimensional hypercubes. Obviously, [Formula: see text]. In order to further understand and study the independent set of [Formula: see text], we explore its clique number and the structure of the maximum clique. In this paper, we obtain the clique number and the structure of the maximum clique of [Formula: see text] for [Formula: see text] As an application, by [Formula: see text] we obtain the upper bounds of [Formula: see text] for [Formula: see text]