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Given the large action space and state space involved in penetration testing, reinforcement learning is widely applied to enhance testing efficiency. This paper proposes an automatic penetration testing scheme based on hierarchical reinforcement learning to reduce both action space and state space. The scheme includes a network intelligence responsible for specifying the penetration host and a host intelligence designated to perform penetration testing on the selected host. Specifically, within the network intelligence, an action-masking mechanism is adopted to shield unenabled actions, thereby reducing the explorable action space and improving the penetration testing efficiency. Additionally, the host intelligence employs an invalid discrimination mechanism, terminating testing after actions that do not alter system states, thereby preventing sudden increases in the number of neural network training steps for an action. An optimistic estimation mechanism is also introduced to select penetration strategies suited to various hosts, preventing training crashes due to value confusion between different hosts. Ablation experiments demonstrate that the host intelligence can learn different penetration strategies for varying penetration depths without significant fluctuations in training steps, and the network intelligence can coordinate with the host intelligence to perform network penetration steadily. This hierarchical reinforcement learning framework can detect network vulnerabilities more quickly and accurately, significantly reducing the cost of security policy updates. [ABSTRACT FROM AUTHOR]

With the proliferation of network traffic, replaying traffic in a virtual environment faces many problems. In order to meet the requirements of traffic integrity and accuracy in the replay process, an accelerated traffic replay method based on time-compressed was proposed to achieve accelerated replay of real network traffic in a virtual environment. The method prioritized the compression of the largest time interval in the set of time intervals, and reduced the compression of the smaller intervals, thus reducing the traffic spike per unit time caused by time compression, and reducing the packet loss rate of traffic. The effect was measured in terms of packet loss rate, time interval error and similarity. The experiments show that this method has a lower packet loss rate and lower time interval error compared with the isometric compression method, although it performs worse in similarity.

Software-Defined Wide Area Networks (SD-WANs) are an emerging Software-Defined Network (SDN) technology to reinvent Wide Area Networks (WANs) for ubiquitous network interconnections in cloud computing, edge computing, and the Internet of Everything. The state-of-the-art overlay-based SD-WANs are simply conjunctions of Virtual Private Network (VPN) and SDN architecture to leverage the controllability and programmability of SDN, which are only applicable for specific platforms and do not comply with the extensibility of SDN. This paper motivates us to refactor traditional VPNs with SDN architecture by proposing an overlay-based SD-WAN solution named Software-Defined Virtual Private Network (SD-VPN). An SDN-based auto-constructed VPN model and its evaluating metrics are put forward to automatically construct overlay WANs by node placement and service orchestration of SD-VPN. Therefore, a joint placement algorithm of VPN nodes and algorithms for overlay WAN service loading and offloading are proposed for SD-VPN controllers. Finally, a three-layer SD-VPN system is implemented and deployed in actual network environments. Simulation experiments and system tests are conducted to prove the high-efficiency controllability, real-time programmability, and auto-constructed deployability of the proposed SD-VPN. Performance trade-off between SD-VPN control channels and data channels is evaluated, and SD-VPN controllers are proven to be extensible for other VPN protocols and advanced services. [ABSTRACT FROM AUTHOR]

The strong Ti-O bonds in TiO2 and poor compatibility with Sb2Se3 result in poor performance when used as electron transport layers (ETL) for Sb2Se3 solar cells. Therefore, cadmium sulfide is usually used as a buffer layer to improve its compatibility. In the present work, we deposited a layer of CdS by spin coating method on TiO2 ETL and fabricated TiO2/CdS dual ETL. The CdS layer improved the electronic properties of TiO2 and grain orientation of Sb2Se3 thin films. As a result, the average short circuit current and fill factor of Sb2Se3 solar cells were improved, and the final champion power conversion efficiency was enhanced from 2.6% to 4.71%. This study supplied a route for the application of titanium dioxide as a broad band gap electron transfer material for Sb2Se3 solar cells. Highlights: We adopt a facile spin-coating method to fabricated CdS buffer layer on TiO2 electron transport layer. The CdS buffer layer altered the orientation of Sb2Se3 film and enhanced its performance. The champion PCE of Sb2Se3 solar cells was enhanced to 4.71% from 2.60% by inserting the CdS buffer layer. [ABSTRACT FROM AUTHOR]

Recent adversaries targeting the Industrial Control Systems (ICSs) have started exploiting their sophisticated inherent contextual semantics such as the data associativity among heterogeneous field devices. In light of the subtlety rendered in these semantics, anomalies triggered by such interactions tend to be extremely covert, hence giving rise to extensive challenges in their detection. Driven by the critical demands of securing ICS processes, a Graph-Neural-Network (GNN) based method is presented to tackle these subtle hostilities by leveraging an ICS's advanced contextual features refined from a universal perspective, rather than exclusively following GNN's conventional local aggregation paradigm. Specifically, we design and implement the Graph Sample-and-Integrate Network (GSIN), a general chained framework performing node-level anomaly detection via advanced feature integration, which combines a node's local awareness with the graph's prominent global properties extracted via process-oriented pooling. The proposed GSIN is evaluated on multiple well-known datasets with different kinds of integration configurations, and results demonstrate its superiority consistently on not only anomaly detection performance (e.g., F1 score and AUPRC) but also runtime efficiency over recent representative baselines. [ABSTRACT FROM AUTHOR]

Machine learning-based intrusion detection systems are an effective way to cope with the increasing security threats faced by industrial control systems. Considering that it is hard and expensive to obtain attack data, it is more reasonable to develop a model trained with only normal data. However, both high-dimensional data and the presence of outliers in the training set result in efficiency degradation. In this research, we present a hybrid intrusion detection method to overcome these two problems. First, we created an improved autoencoder that incorporates the deep support vector data description (Deep SVDD) loss into the training of the autoencoder. Under the combination of Deep SVDD loss and reconstruction loss, the novel autoencoder learns a more compact latent representation from high-dimensional data. The density-based spatial clustering of applications with noise algorithm is then used to remove potential outliers in the training data. Finally, a Bayesian Gaussian mixture model is used to identify anomalies. It learns the distribution of the filtered training data and uses the probabilities to classify normal and anomalous samples. We conducted a series of experiments on two intrusion detection datasets to assess performance. The proposed model performs better than other baseline methods when dealing with high-dimensional and contaminated data. [ABSTRACT FROM AUTHOR]

TiO2 is commonly used as electron transport layers (ETL) for Sb2S3 solar cells. Since its poor compatibility, CdS is usually adopted as buffer layer to enhance the performance. Herein, we prepared TiO2 ETL with a commonly spin-coating method first, and then we deposited CdS on the TiO2 ETL with a facile spin coating method to construct TiO2/CdS double ETL. The spin coating processed CdS is found to exhibit flake morphology, which improved the electronic properties of TiO2 and the grain morphology of Sb2S3 films. The CdS buffer layers by spin coating process were found to afford a unique pathway for electron transport thus enhancing electron extraction. As a result, large Jsc was obtained in the Sb2S3 solar cells on the CdS buffered ETL. A champion PCE of 5.59% was obtained. This work develops a facile and relatively environmentally friendly method to fabricate effective TiO2/CdS ETL. [ABSTRACT FROM AUTHOR]