This study develops a reinforcement learning (RL)-based control framework with guaranteed predefined performance for nonlinear switched interconnected systems. This approach effectively addresses challenges arising from unmeasurable states and group average dwell time switching mechanisms, allowing both convergence time and accuracy to be preset via parameter configuration. First, the system equations are reconstructed to target nonlinear and interconnected terms, which are then approximated using neural networks (NNs). Additionally, an NNs-based switching state observer is designed to estimate the unmeasurable states. Second, within the backstepping synthesis framework, a distributed optimal controller is designed by integrating a performance transformation function into the cost function, with the resulting control law approximated via an identifier-actor-critic architecture. Furthermore, the group average dwell time-based stability analysis is generalized to address the optimal control challenges inherent in nonlinear switched interconnected systems. Compared with existing studies, this approach demonstrates enhanced extensibility and practicality for real-world applications. Finally, two simulation examples verify the effectiveness and superiority of the proposed method over state-of-the-art alternatives.

With the development of the space-air-ground integration technology, the satellites are armed with a certain on-board computing resource. To fully offload communication tasks to each satellite, we investigate a multiuser multi-satellite cooperative communication system without a central processing unit (CPU), where the satellites are equipped with tunable load antennas leveraging the mutual coupling effect to reconfigure the wireless channel. First, we formulate the sum spectral efficiency (SE) maximization problem with respect to the beamforming and the tunable loads under the power constraint and the constraints of the tunable loads. Afterwards, we propose a cooperative algorithm with closed-form updates to obtain a stationary point based on parallel successive convex approximation (SCA). Furthermore, we propose an efficient information exchange strategy for the satellites based on the ring all-reduce method, which significantly reduces the information exchange overhead of each satellite. Lastly, numerical results verify the proposed design's notable gain over the baselines. As far as we know, this is the first work to study the multi-satellite cooperative communication system with tunable load antennas.

Cybersecurity controls are essential for ensuring information confidentiality, integrity, and availability. However, selecting the most effective controls to maximize return on investment (RoI) in cyber defense is a complex task involving numerous factors such as vulnerabilities, threat prioritization, and budget constraints. This paper introduces an innovative model and optimization techniques to select cybersecurity controls (CSC) for optimal risk mitigation, balancing residual risk, budget, and resiliency requirements. Our approach features the Security Control Grid (SCG) model, which automatically determines the necessary controls based on their security functions (Identify, Protect, Detect, Respond, and Recover), strategic placement within the cyber environment, and effectiveness at different stages of the attack kill chain. We formulate cybersecurity control decision-making as a multidimensional optimization problem, solving it using Satisfiability Modulo Theories (SMT). Additionally, we integrate a domain-specific language model that links CSCs with Common Vulnerabilities and Exposures (CVEs). This approach is implemented in the SCG solver tool, which generates scalable and robust CSC deployment plans that optimize cybersecurity RoI and maintain acceptable residual risk for large-scale enterprises.

Since real‐world multiview data frequently contains numerous samples that are not observed from some viewpoints, the incomplete multiview clustering (IMC) issue has received a great deal of attention recently. However, most existing IMC methods choose to zero‐fill the missing instances, which leads to the failure to exploit information hidden in the missing instances, and high‐order interactions between various views. To tackle these problems, we proposed an effective IMC method using low‐rank tensor ring completion, which was demonstrated to be powerful in exploiting high‐order correlation. Specifically, we first stack the incomplete similarity graphs of all views into a 3rd‐order incomplete tensor and then restore it via the tensor ring decomposition. Next, using an adaptive weighting technique, we apply multiview spectral clustering to all entire graphs in order to balance the contributions of different viewpoints and identify the consensus representation for grouping. Finally, we employ the alternating direction method of multipliers (ADMM) to optimize the suggested model. Numerous experimental findings on numerous different datasets show that the suggested approach is superior to other cutting‐edge approaches.

Malware is commonly used by adversaries to compromise and infiltrate cyber systems in order to steal sensitive information or destroy critical assets. Active Cyber Deception (ACD) has emerged as an effective proactive cyber defense against malware to enable misleading adversaries by presenting fake data and engaging them to learn novel attack techniques. However, real-time malware deception is a complex and challenging task because (1) it requires a comprehensive understanding of the malware behaviors at technical and tactical levels in order to create the appropriate deception ploys and resources that can leverage this behavior and mislead malware, and (2) it requires a configurable yet provably valid deception planning to guarantee effective and safe real-time deception orchestration. This article presents symbSODA, a highly configurable and verifiable cyber deception system that analyzes real-world malware using multipath execution to discover API patterns that represent attack techniques/tactics critical for deception, enables users to create their own customized deception ploys based on the malware type and objectives, allows for constructing conflict-free Deception Playbooks , and finally automates the deception orchestration to execute the malware inside a deceptive environment. symbSODA extracts Malicious Sub-graphs (MSGs) consisting of WinAPIs from real-world malware and maps them to tactics and techniques using the ATT&CK framework to facilitate the construction of meaningful user-defined deception playbooks. We conducted a comprehensive evaluation study on symbSODA using 255 recent malware samples. We demonstrated that the accuracy of the end-to-end malware deception is 95% on average, with negligible overhead using various deception goals and strategies. Furthermore, our approach successfully extracted MSGs with a 97% recall, and our MSG-to-MITRE mapping achieved a top-1 accuracy of 88.75%. Our study suggests that symbSODA can serve as a general-purpose Malware Deception Factory to automatically produce customized deception playbooks against arbitrary malware behavior.

BACKGROUND: Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. RESULTS: Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. CONCLUSIONS: Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.

Static system configuration provides a significant advantage for the adversaries to discover the assets and launch attacks. Configuration-based moving target defense (MTD) reverses the cyber warfare asymmetry by mutating certain configuration parameters to disrupt the attack planning or increase the attack cost significantly. In this research, we present a methodology for the formal verification of MTD techniques. We formally modeled MTD techniques and verified them against constraints. We use Random Host Mutation (RHM) as a case study for MTD formal verification. The RHM transparently mutates the IP addresses of end-hosts and turns into untraceable moving targets. We apply the formal methodology to verify the correctness, safety, mutation, mutation quality, and deadlock-freeness of RHM using the model checking tool. An adversary is also modeled to validate the effectiveness of the MTD technique. Our experimentation validates the scalability and feasibility of the formal verification methodology.

Realizing accurate prediction of base station traffic and effectively controlling the entire network has become a major problem that needs to be solved urgently in the rapidly developing mobile communications environment. We propose a base station traffic prediction method based on STL-LSTM model, and introduce a Seasonal and Trend decomposition using Loess (STL) method based on robust local weighted regression to achieve smoothness. By this method, the trend, period, and noise of the base station data are separately decomposed to achieve efficient use of data. Then the paper introduces a long-term short-term memory network (LSTM), uses its back-propagation time training and overcomes the characteristics of the disappearance gradient to predict the processed data to achieve the prediction closest to the true value. The experimental results show that using this algorithm to predict the base station traffic has better performance comparing with the other algorithms. And the high-accuracy prediction can be realized effectively according to the dynamic transformation of the real state of the base station traffic.

Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision. High quality reconstruction is usually challenging especially when fine detail is needed and the albedo of the object is non-uniform. This paper introduces vertex overall illumination vectors to model the illumination effect and presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are the two important observations: (1) the illumination over the surface of an object often appears to be piecewise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing the surface, which was often overlooked previously. Thus we propose to use TV to regularize the overall illumination vectors and use visual hull to constrain partial vertices. The reconstruction is formulated as a constrained TV-minimization problem that simultaneously treats the shape and illumination vectors as unknowns. An augmented Lagrangian method is proposed to quickly solve the TV-minimization problem. As a result, our approach is robust, stable and is able to efficiently recover high-quality surface details even when starting with a coarse model obtained using MVS. These advantages are demonstrated by extensive experiments on the state-of-the-art MVS database, which includes challenging objects with varying albedo.

Covert channels are usually used to circumvent security policies and allow information leakage without being observed. In this paper, we propose a novel covert channel technique using the packet reordering phenomenon as a host for carrying secret communications. Packet reordering is a common phenomenon on the Internet. Moreover, it is handled transparently from the user and application-level processes. This makes it an attractive medium to exploit for sending hidden signals to receivers by dynamically manipulating packet order in a network flow. In our approach, specific permutations of successive packets are selected to enhance the reliability of the channel, while the frequency distribution of their usage is tuned to increase stealthiness by imitating real Internet traffic. It is very expensive for the adversary to discover the covert channel due to the tremendous overhead to buffer and sort the packets among huge amount of background traffic. A simple tool is implemented to demonstrate this new channel. We studied extensively the robustness and capabilities of our proposed channel using both simulation and experimentation over large varieties of traffic characteristics. The reliability and capacity of this technique have shown promising results. We also investigated a practical mechanism for distorting and potentially preventing similar novel channels.

DDoS attacks have been a persistent threat to network availability for many years. Most of the existing mitigation techniques attempt to protect against DDoS by filtering out attack traffic. However, as critical network resources are usually static, adversaries are able to bypass filtering by sending stealthy low traffic from large number of bots that mimic benign traffic behavior. Sophisticated stealthy attacks on critical links can cause a devastating effect such as partitioning domains and networks. In this paper, we propose to defend against DDoS attacks by proactively changing the footprint of critical resources in an unpredictable fashion to invalidate an adversary's knowledge and plan of attack against critical network resources. Our present approach employs virtual networks (VNs) to dynamically reallocate network resources using VN placement and offers constant VN migration to new resources. Our approach has two components: (1) a correct-by-construction VN migration planning that significantly increases the uncertainty about critical links of multiple VNs while preserving the VN placement properties, and (2) an efficient VN migration mechanism that identifies the appropriate configuration sequence to enable node migration while maintaining the network integrity (e.g., avoiding session disconnection). We formulate and implement this framework using SMT logic. We also demonstrate the effectiveness of our implemented framework on both PlanetLab and Mininet-based experimentations.

Network reconnaissance of IP addresses and ports is prerequisite to many host and network attacks. Meanwhile, static configurations of networks and hosts simplify this adversarial reconnaissance. In this paper, we present a novel proactive-adaptive defense technique that turns end-hosts into untraceable moving targets, and establishes dynamics into static systems by monitoring the adversarial behavior and reconfiguring the addresses of network hosts adaptively. This adaptability is achieved by discovering hazardous network ranges and addresses and evacuating network hosts from them quickly. Our approach maximizes adaptability by (1) using fast and accurate hypothesis testing for characterization of adversarial behavior, and (2) achieving a very fast IP randomization (i.e., update) rate through separating randomization from end-hosts and managing it via network appliances. The architecture and protocols of our approach can be transparently deployed on legacy networks, as well as software-defined networks. Our extensive analysis and evaluation show that by adaptive distortion of adversarial reconnaissance, our approach slows down the attack and increases its detectability, thus significantly raising the bar against stealthy scanning, major classes of evasive scanning and worm propagation, as well as targeted (hacking) attacks.

Network reconnaissance of addresses and ports is prerequisite to a vast majority of cyber attacks. Meanwhile, the static address configuration of networks and hosts simplifies adversarial reconnaissance for target discovery. Although the randomization of host addresses has been suggested as a proactive disruption mechanism against such reconnaissance, the proposed approaches do not exploit the full potentials of address randomization in provision of unpredictability and attack adaptability. Moreover, these approaches do not provide thorough analysis on effectiveness and limitations of address randomization against relevant threat models, including stealthy scanning and worms. In this paper, we present an effective address randomization technique, called random host address mutation (RHM), that turns end-hosts into untraceable moving targets. This technique achieves maximum efficacy by allowing address randomization to be highly unpredictable and fast, and adaptive to adversarial behavior, while incurring low operational and reconfiguration overhead. Our approach achieves the following objectives: (1) it achieves high uncertainty in adversary scanning by modeling address mutation randomization as a multi-level satisfiability problem; (2) it adapts the mutation scheme by fast characterization of adversarial reconnaissance patterns; (3) it achieves high mutation rate by separating mutation from end-hosts and managing it via network appliances; and (4) it preserves network integrity, manageability and performance by bounding the size of routing tables, preserving end-to-end reachability, and efficient handling of reconfiguration updates. Our extensive analyses and simulation show that the RHM distorts adversarial reconnaissance, slows down (deters) the attack, and increases its detectability. Consequently, the RHM is effective in countering a significant number of sophisticated threat models, including reconnaissance, stealthy/evasive scanning methods, and targeted attacks. We also address limitations of our approach in terms of effectiveness and applicability.

Summary form only given. Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision and high quality reconstruction is usually challenging especially when high detail is needed. This paper presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are our two important observations: (1) the illumination over the surface of an object tends to be piecewise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing geometry, which were previously overlooked. Thus we introduce TV to regularize the lighting and use visual hull to constrain partial vertices. The reconstruction is formulated as a constrained TV minimization problem that treats the shape and lighting as unknowns simultaneously. An augmented Lagrangian method is proposed to quickly solve the TV-minimization problem. As a result, our approach is robust, stable and is able to efficiently recover high quality of surface details even starting with a coarse MVS. These advantages are demonstrated by the experiments with synthetic and real world examples.

We present a class of Minimum Cost Blocking (MCB) problems in Wireless Mesh Networks (WMNs) with multi-path routing protocols. We establish the provable superiority of multi-path routing protocols over conventional protocols against blocking, node-isolation and network-partitioning type attacks. In our attack model, an adversary is considered successful if he is able to capture/isolate a subset of nodes such that no more than a certain amount of traffic from source nodes reaches the gateways. Two scenarios, viz. (a) low mobility for network nodes, and (b) high degree of node mobility, are evaluated. Scenario (a) is proven to be NP-hard and scenario (b) is proven to be #P-hard for the adversary to realize the goal. Further, several approximation algorithms are presented which show that even in the best case scenario it is at least exponentially hard for the adversary to optimally succeed in such blocking-type attacks. These results are verified through simulations which demonstrate the robustness of multi-path routing protocols against such attacks. To the best of our knowledge, this is the first work that theoretically evaluates the attack-resiliency and performance of multi-path protocols with network node mobility.

Interoperability between heterogeneous objects of Internet of Things (IoT) and massive data generated in the course of processing bring enormous challenges to application development of Internet of Things. To accommodate changeful application requirements of Internet of Things, a cloud-based development platform for services and bundles is proposed. On the basis of the analysis of cloud-based development, web services and Open Service Gateway Initiative (OSGi) bundles, the design principles and architecture of our platform are specified. After key designs are introduced, our prototype implementation and its evaluation are presented. Our targeted prototype provides a collaborative platform and highly available storage in cloud, both of which are proved to be feasible.

The existing high-accuracy environment matting extraction methods usually require the capturing of thousands of sample images and spend several hours in data acquisition. In this paper, a fast environment matting algorithm is proposed to ex tract the environment matte data effectively and efficiently. In particular, we incorporate the recently developed compressive sensing theory to simplify the data acquisition process. More over, taking into account special properties of light refraction and reflection effects of transparent object, we further propose to use hierarchical sampling and group clustering based recovery to accelerate the matte extraction process. Compared with the state-of-the-art approaches, our proposed algorithm significantly accelerates the environment matting extraction process while still achieving high-accuracy results.

Abstract In digital image editing, environment matting and compositing are fundamental and interesting operations that can capture and simulate the refraction and reflection effects of light from an environment. The state‐of‐the‐art real‐time environment matting and compositing method is short of flexibility, in the sense that it has to repeat the entire complex matte acquisition process if the distance between the object and the background is different from that in the acquisition stage, and also lacks accuracy, in the sense that it can only remove noises but not errors. In this paper, we introduce the concept of refractive vector and propose to use a refractive vector field as a new representation for environment matte. Such refractive vector field provides great flexibility for transparent‐object environment matting and compositing. Particularly, with only one process of the matte acquisition and the refractive vector field extraction, we are able to composite the transparent object into an arbitrary background at any distance. Furthermore, we introduce a piecewise vector field fitting algorithm to simultaneously remove both noises and errors contained in the extracted matte data. Experimental results show that our method is less sensitive to artefacts and can generate perceptually good composition results for more general scenarios.

The major drawback of real-time environment matting method is that the extracted environment matte data often contains significant amount of noise and errors. Although some filtering methods have been employed to remove the noise and obtain acceptable composition results, they are incapable of removing potential errors. In this paper, we first establish a light motion field to better describe the environmental matting effect of transparent objects and propose a new vector field fitting algorithm to simultaneously remove both noise and errors in the extracted matte data by using energy minimization approach. Experimental results show that our method is less sensitive to noise and error and can generate perceptually better composition results than the existing real-time environment matting approaches.

Time synchronization in highly distributed wireless systems like sensor and ad hoc networks is extremely important in order to maintain a consistent notion of time throughout the network and to support the various timing-based applications. But, cheating behavior by the participating nodes in the network can severely jeopardize the accuracy of the associated time synchronization process. Despite recent advances in this direction, a key fundamental question still remains unanswered: Is it theoretically feasible to secure distributed time synchronization protocols, given complete (or global) time and time difference information in the network?

In this paper, we present a novel hybrid deformation model using global mass-spring medial representation structures and local finite element model. We employ the hybrid models, by fully calculating the FEM deformation in the local operation part while only calculating the global deformation by medial representation method. To achieve the real-time requirement of realistic deformable modeling, it is necessary to use the GPU parallel computing for FEM on regional deformation details, so the major calculation work in the conjugate gradient solver for the solution matrix is moved from CPU to GPU to accelerate the effectiveness. Evaluation and experiments are also discussed.

This paper demonstrates the provable superiority of multi-path routing protocols over other conventional protocols in Wireless Mesh Networks (WMNs) against blocking, node- isolation and network-partitioning type-attacks. Though the underlying network model is of a WMN with mobile nodes, the results in this paper are equally applicable to other types of wireless data networks. The adversarial objective is to isolate a subset of network nodes through minimal cost optimal blocking of certain number of paths in the network (or partitioning the network). If less than a certain threshold of traffic from such node(s) reaches the routers, the adversary is successful. Two scenarios viz. (a) low mobility for network nodes, and (b) high degree of node mobility, are evaluated. Scenario (a) is proven to be NP-hard and scenario (b) is proven to be #P-hard for the adversary to achieve the goal. Further, several approximation algorithms are presented which show that even in the best case scenario it is at least exponentially hard for the adversary to optimally succeed in such blocking-type attacks. Simulations verify the results and demonstrate the robustness of multi-path protocols against such attacks. The objective of this paper is to study the performance and feasibility of multi-path wireless protocols over conventional single-path protocols from a security angle. To the best of our knowledge, this is the first paper to theoretically evaluate the attack-resiliency and performance of multi-path protocols with network node mobility.

Constraining an interpolating curve to be bounded in a given region is an important task in curve design. In (Qi Duan et al., 1999) the rational cubic spline with a linear denominator has been used to control the interpolating curves to be bounded in the given region, but it does not work in some cases. This paper deals with the weighted rational cubic spline with a linear denominator for this kind of constraint, the sufficient condition for controlling the interpolating curves to be bounded in the given region are derived. An example is given which shows that the constraint which cannot be done by the rational spline defined in (Qi Duan et al., 1999) could be achieved by the weighted rational spline.