In scenarios such as disaster response, remote area surveillance, and military reconnaissance, where stable communication infrastructure may be unavailable, unmanned aerial vehicles (UAVs) are often deployed to physically carry data. Ensuring data reliability in such scenarios, especially in adversarial environments, requires data backup mechanisms for UAV swarms. This paper addresses the critical yet often overlooked challenge of data recovery difficulty in UAV swarm networks. We introduce Data Recovery Entropy (DRE) to quantify the spatial dispersal of backed-up data across the swarm. Based on this metric, we propose two data backup strategies: one enables adjustable level of DRE through a tunable parameter, and the other prioritizes minimizing DRE. The proposed data backup strategies are tailored to the unique characteristics of UAV swarms, focusing particularly on their dynamic communication topologies. Finally, we vary system parameters, including node deployment parameters, communication parameters, and mobility parameters, to validate the proposed approach.

This article introduces a command and control (C2) organizational structure model, which can analyze military C2 organization structure from three perspectives: organizational elements, organizational relationships, and organizational processes. In the organizational processes, a decision-making problem of recommending course of action (COA) is described. Then, a planning method is proposed to solve this decision-making problem, which includes six evaluation metrics of COA effectiveness and a tabu search algorithm implemented within the C2 organization structure model. On this basis, we provide two application examples to demonstrate the usability of the proposed C2 organizational structure model and its COA planning method for operational decision-making.

The complex problems of multiclass imbalance, virtual or real concept drift, concept evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network traffic classification tasks. In this paper, we propose a multiclass imbalanced and concept drift network traffic classification framework based on online active learning (MicFoal), which includes a configurable supervised learner for the initialization of a network traffic classification model, an active learning method with a hybrid label request strategy, a label sliding window group, a sample training weight formula and an adaptive adjustment mechanism for the label cost budget based on a periodic performance evaluation. In addition, a novel uncertain label request strategy based on a variable least confidence threshold vector is designed to address the problems of a variable multiclass imbalance ratio or even the number of classes changing over time. Experiments performed based on eight well-known real-world network traffic datasets demonstrate that MicFoal is more effective and efficient than several state-of-the-art learning algorithms.

A challenge to many real-world applications is multiclass imbalance with concept drift. In this paper, we propose a comprehensive active learning method for multiclass imbalanced streaming data with concept drift (CALMID). First, we design a comprehensive online active learning framework that includes an ensemble classifier, a drift detector, a label sliding window, sample sliding windows and an initialization training sample sequence. Next, a variable threshold uncertainty strategy based on an asymmetric margin threshold matrix is designed to comprehensively address the problem that a given class can simultaneously be a majority to a given subset of classes while also being a minority to others. Last but not least, we design a novel sample weight formula that comprehensively considers the class imbalance ratio of the sample’s category and the prediction difficulty. On 10 multiclass synthetic streams with different imbalance ratios and concept drifts, and on 5 real-world imbalanced streams with 7 to 55 classes and unknown drifts, the experimental results demonstrate that the proposed CALMID is more effective and efficient than several state-of-the-art learning algorithms.

Stealth malware, a representative tool of advanced persistent threat (APT) attacks, in particular poses an increased threat to cyber-physical systems (CPS). Due to the use of stealthy and evasive techniques (e.g., zero-day exploits, obfuscation techniques), stealth malwares usually render conventional heavyweight countermeasures (e.g., exploits patching, specialized ant-malware program) inapplicable. Light-weight countermeasures (e.g., containment techniques), on the other hand, can help retard the spread of stealth malwares, but the ensuing side effects might violate the primary safety requirement of CPS. Hence, defenders need to find a balance between the gain and loss of deploying light-weight countermeasures. To address this challenge, we model the persistent anti-malware process as a shortest-path tree interdiction (SPTI) Stackelberg game, and safety requirements of CPS are introduced as constraints in the defender's decision model. Specifically, we first propose a static game (SSPTI), and then extend it to a multi-stage dynamic game (DSPTI) to meet the need of real-time decision making. Both games are modelled as bi-level integer programs, and proved to be NP-hard. We then develop a Benders decomposition algorithm to achieve the Stackelberg Equilibrium of SSPTI. Finally, we design a model predictive control strategy to solve DSPTI approximately by sequentially solving an approximation of SSPTI. The extensive simulation results demonstrate that the proposed dynamic defense strategy can achieve a balance between fail-secure ability and fail-safe ability while retarding the stealth malware propagation in CPS.

Graphical models are widely used in cyber security analysis to capture relationships among variables in attack scenarios. However, most models are difficult to build due to the greatly imbalanced data in cyber attacks. To solve this problem, we propose a two-level Bayesian network framework in this paper. We firstly classify the cyber attacks into two levels, one is used to identify general types of attacks and the other is used to classify specific forms. Then we train Bayesian networks for each level. To help administrators cope with threats in time, we propose an analysis method. This method finds the important node's Markov blanket and sorts nodes in it by their influence on each specific form, which could help to understand key threats in cyberspace.

Networks portray interactions about people communication, goods transportation and information exchange within a society, etc. How to effectively quantify the node importance is a fundamental problem in complex networks research community. In this paper, we propose a node importance evaluation method called Error Reconstruction (ER), which is mainly based on the measurement and reconstruction of errors (strong correlation with difference) between nodes in the network. First, we introduced the Network Representation Learning (NRL) approach to obtain the feature matrix about the node information and network structure, and thus we extracted the background nodes (less important nodes) to reconstruct the dense and sparse error in a single-scale network. Then we calculated the reconstruction error in a series of transformed multi-scale networks by analyzing the error with different network scales and integrated the reconstruction error with Bayesian estimation to get the final quantization of all nodes in the network. Experimental results show that the proposed method could effectively discover the vital nodes in the network.

In this paper we propose a novel method for modeling the shape of vessel trajectories in a manner which may facilitate the application of machine learning techniques. This is achieved by transforming the topological feature of vessel trajectories into vectors. More specifically, we calculate scale-invariance indicators for every vessel trajectory as shape characteristics, and other indicators to denote the trajectory area. The proposed method is validated using both synthetic trajectories and real-world AIS datasets. We demonstrate that it can achieve good time efficiency and may support vessel trajectory related analysis.

Goal recognition, which is the task of inferring an agent’s goals given some or all of the agent’s observed actions, is one of the important approaches in bridging the gap between the observation and decision making within an observe-orient-decide-act cycle. Unfortunately, few researches focus on how to improve the utilization of knowledge produced by a goal recognition system. In this work, we propose a Markov Decision Process-based goal recognition approach tailored to a dynamic shortest-path local network interdiction (DSPLNI) problem. We first introduce a novel DSPLNI model and its solvable dual form so as to incorporate real-time knowledge acquired from goal recognition system. Then a Markov Decision Process-based goal recognition model along with its dynamic Bayesian network representation and the applied goal inference method is proposed to identify the evader’s real goal within the DSPLNI context. Based on that, we further propose an efficient scalable technique in maintaining action utility map used in fast goal inference, and develop a flexible resource assignment mechanism in DSPLNI using knowledge from goal recognition system. Experimental results show the effectiveness and accuracy of our methods both in goal recognition and dynamic network interdiction.

Key element protection of combat system is a challenging problem in modern combat. Effectively evaluating the key elements would be of great help in force deployment in crisis situations. This paper proposes a new evaluation method that combines expert evaluation, PCA (Principal Component Analysis) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). Specifically, we first use expert evaluation to get attributes' values of each element. Then, we apply PCA to extract principal components from elements' attributes. Next, we use TOPSIS to calculate elements' proximities to ideal solutions under different principal components. Finally, we synthesize each element's proximity under different principal component to get the relative superiority. In addition, we test the proposed method in an experimental combat scenario, and show the plausibility of this method.

To solve a real‐world planning problem with interfering subgoals, it is essential to perform early detection of subgoal dependencies and achieve the subgoals in the correct order. This is also the case for planning problems with forced goal‐ordering (FGO) constraints. In automated planning, forward search with FGO constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. Many existing methods such as goal agenda manager and ordered landmarks cannot detect the FGOs accurately, and thus, the undiscovered ordering relationship may cause the forward search to suffer from deadlocks. In this article, we put forward an approach via an effective search heuristic to constrain a planner to satisfy the FGOs. We make use of an atomic goal‐achievement graph in a look‐ahead search under the FGO constraints. This allows a forward search strategy to plan forward efficiently in multiple steps toward a goal state along a search path. Experimental results illustrate that, by avoiding deadlocks, we can solve more benchmark planning problems more efficiently than previous approaches. We also prove several formal properties for search that are related to FGO detection.

Community structure is very important for many real-world networks. It has been shown that communities are overlapping and hierarchical. However, most previous methods, based on the graph model, can't investigate these two properties of community structure simultaneously. Moreover, in some cases the use of simple graphs does not provide a complete description of the real-world network. After introducing hyper graphs to describe real-world networks and defining hyper-edge similarity measurement, we propose a Hierarchical Clustering method based on Hyper-edge Similarity (HCHS) to simultaneously detect both the overlapping and hierarchical properties of complex community structure, as well as using the newly introduced community density to evaluate the goodness of a community. The examples of application to real-world networks give excellent results.

Human-System systems design is a critical part of Command and Control (C2) design, in order to improve the whole effectiveness of C2 design, the human beings and systems must be in harmony in the human-in-loop systems, which can be referred to as Cyber-physical-social systems (CPSS). Based on the coordination theory, self synchronization of a CPSS is both a self-organizing and self-adapting process, of which the end effect will be shown as the emergence of all the dependency relationships among the courses of action (COAs). Human-system systems design for C2 will be modeled in different views: the system view, the human view and the coordination view, being a loop iteration process.

Military mission planning aims to coordinate a set of platforms with different capacities to accomplish a set of tasks under temporal, spatial and resource constraints. However, as military operations are intrinsically dynamic and uncertain, a solution (plan) corresponding to the deterministic circumstance is fragile due to unexpected events. To tackle this problem, this paper proposes a chance constrained programming model, which incorporates many uncertain factors, such as the durations, locations and resource requirements of tasks. The objective of mission planning is to coordinate the platforms to maximize the probability that all of tasks are completed successfully while satisfying the chance constraints. The problem is solved under a computational framework combining GA and Monte Carlo Simulation, the GA is used to solve the platform allocation and task scheduling while Monte Carlo Simulation is used to process the chance constraints. A mission instance is presented which demonstrates the usefulness of the proposed model and algorithm.

The goal of image quality assessment research is to design quantitative measurements for the evaluation of image quality such that it is consistent with subjective human evaluation. Inspired by intrinsic geometric structure of nature images and characteristic of visual perception, we propose a novel geometric structural distortion model for image quality assessment in this paper, which has relatively low computational complexity and clear physical meanings. The experimental results of LIVE image database show that the proposed method is consistent with the subjective assessment of human beings and has a good performance for all distortion types.

This paper presents an efficient decentralized Grid service discovery approach based on service ontology. It uses two techniques to improve efficiency. First, Grid information nodes are organized into community overlays of different service categories defined in service ontology. A distributed hash table (DHT) based upper layer network is constructed to provide efficient navigation between communities. Second, a simple and lightweight greedy search based service location (GSBSL) method is introduced to identify service providers with high QoS efficiently within communities. Simulation results show that, the efficiency is improved compared with existing decentralized Grid service discovery approaches, and the overhead is acceptable and controllable.

Service location based on greedy search is studied. A model is built to analyze the influence of the network topologies on this location method, and different network topologies are constructed to validate the model by way of simulation. The results show that, though the model assumes a uniform degree distribution, it agrees with the simulation result well if the average degree of the network is reasonably big. Hops and relative QoS index of the node found in a service location process are used to evaluate the effectiveness of the location method as well as the probability of locating the first 5% nodes with higher QoS level. Both model and simulation result show that the performance of greedy search based service location improves significantly with the increase of the average degree of the network. If changes of both topology and the QoS level of nodes can be ignored during a location process, it can be guaranteed that the location has high probability of finding the nodes with relatively high QoS level in small number of hops in a big service community. Model extension under arbitrary network degree distribution is further studied. A "two-phase" topology construction and service location based on greedy-search is also put forward.