Biblio

Power system intelligent terminal equipment is widely used in real-time monitoring, data acquisition, power management, power distribution and other tasks of smart grid. The power system intelligent terminal can obtain various information of users and power companies in the power grid, but there is still a lack of protection means for the connection and communication process of the terminal components. In this paper, a novel method based on improved deep belief network(IDBN) is proposed to accomplish the business-level security monitoring and attack detection of power system terminal. A non-intrusive business-level monitoring platform for power system terminals is established, which uses energy metering intelligent terminals as an example for non-intrusive data collection. Based on this platform, the I-DBN extracts the spatial and temporal attack characteristics of the external monitoring data of the system. Some fault conditions and cyber attacks of the model have been simulated to demonstrate the effectiveness of the proposed detection method and the results show excellent performance. The method and platform proposed in this paper can be extended to other services in the power industry, providing a theoretical basis and implementation method for realizing the security monitoring of power system intelligent terminals from the business level.

In the existing situation, most of the business process are running through web applications. This helps the enterprises to grow their business efficiently which creates a good consumer relationship. But the main problem is that they failed to provide a vulnerable free environment. To overcome this issue in web applications, vulnerability assessment should be made periodically. They are many vulnerability assessment methodologies which occur earlier are not much proactive. So, machine learning is needed to provide a combined solution to determine vulnerability occurrence and percentage of vulnerability occurred in logical web pages. We use Decision Tree and Bayesian Belief Network (BBN) as a collective solution to find either vulnerability occur in web applications and the vulnerability occurred percentage on different logical web pages.

OpenFlow has been the main standard protocol of software defined networking (SDN) since the launch of this new networking paradigm. It is a programmable network protocol that controls traffic flows among switches and routers regardless of their platforms. Its security relies on the optional implementation of Transport Layer Security (TLS) which has been proven vulnerable. The aim of this research was to develop a secured OpenFlow, so-called Secured-OF. A stateful firewall was used to store state information for further analysis. Dynamic Bayesian Network (DBN) was used to learn denial-of-service attack and distributed denial-of-service attack. It analyzes packet states to determine the nature of an attack and adds that piece of information to the flow table entry. The proposed Secured-OF model in Ryu controller was evaluated with several performance metrics. The analytical evaluation of the proposed Secured-OF scheme was performed on an emulated network. The results showed that the proposed Secured-OF scheme offers a high attack detection accuracy at 99.5%. In conclusion, it was able to improve the security of the OpenFlow controller dramatically with trivial performance degradation compared to an SDN with no security implementation.

Identifying cyberattack vectors on cyber supply chains (CSC) in the event of cyberattacks are very important in mitigating cybercrimes effectively on Cyber Physical Systems CPS. However, in the cyber security domain, the invincibility nature of cybercrimes makes it difficult and challenging to predict the threat probability and impact of cyber attacks. Although cybercrime phenomenon, risks, and treats contain a lot of unpredictability's, uncertainties and fuzziness, cyberattack detection should be practical, methodical and reasonable to be implemented. We explore Bayesian Belief Networks (BBN) as knowledge representation in artificial intelligence to be able to be formally applied probabilistic inference in the cyber security domain. The aim of this paper is to use Bayesian Belief Networks to detect cyberattacks on CSC in the CPS domain. We model cyberattacks using DAG method to determine the attack propagation. Further, we use a smart grid case study to demonstrate the applicability of attack and the cascading effects. The results show that BBN could be adapted to determine uncertainties in the event of cyberattacks in the CSC domain.

We present a novel, and use case agnostic method of identifying and circumventing private data exposure across distributed and high-dimensional data repositories. Examples of distributed high-dimensional data repositories include medical research and treatment data, where oftentimes more than 300 describing attributes appear. As such, providing strong guarantees of data anonymity in these repositories is a hard constraint in adhering to privacy legislation. Yet, when applied to distributed high-dimensional data, existing anonymisation algorithms incur high levels of information loss and do not guarantee privacy defeating the purpose of anonymisation. In this paper, we address this issue by using Bayesian networks to handle data transformation for anonymisation. By evaluating every attribute combination to determine the privacy exposure risk, the conditional probability linking attribute pairs is computed. Pairs with a high conditional probability expose the risk of deanonymisation similar to quasi-identifiers and can be separated instead of deleted, as in previous algorithms. Attribute separation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in information loss. In other words, assimilating the conditional probability of outliers directly in the adjacency matrix in a greedy fashion is quick and thwarts de-anonymisation. Since identifying every privacy violating attribute combination is a W[2]-complete problem, we optimise the procedure with a multigrid solver method by evaluating the conditional probabilities between attribute pairs, and aggregating state space explosion of attribute pairs through manifold learning. Finally, incremental processing of new data is achieved through inexpensive, continuous (delta) learning.

This paper proposes an approach based on Bayesian Networks to support cyber security analysts in improving the cyber-security posture of the smart grid. We build a system model that exploits real world context information from both Information and Operational Technology environments in the smart grid, and we use it to demonstrate sample predictive and diagnostic analyses. The innovative contribution of this work is in the methodology capability of capturing the many dependencies involved in the assessment of security threats, and of supporting the security analysts in planning defense and detection mechanisms for energy digital infrastructures.

In fault diagnosis of industrial process, there are usually more than one variable that are faulty. When multiple faults occur, the generalized reconstruction-based contribution can be helpful while traditional RBC may make mistakes. Due to the correlation between the variables, these faults usually propagate to other normal variables, which is called smearing effect. Thus, it is helpful to consider the pervious fault diagnosis results. In this paper, a data-driven fault diagnosis method which is based on generalized RBC and bayesian decision is presented. This method combines multi-dimensional RBC and bayesian decision. The proposed method improves the diagnosis capability of multiple and minor faults with greater noise. A numerical simulation example is given to show the effectiveness and superiority of the proposed method.

This paper work is focused on Performance comparison of intrusion detection system between DBN Algorithm and SPELM Algorithm. Researchers have used this new algorithm SPELM to perform experiments in the area of face recognition, pedestrian detection, and for network intrusion detection in the area of cyber security. The scholar used the proposed State Preserving Extreme Learning Machine(SPELM) algorithm as machine learning classifier and compared it's performance with Deep Belief Network (DBN) algorithm using NSL KDD dataset. The NSL- KDD dataset has four lakhs of data record; out of which 40% of data were used for training purposes and 60% data used in testing purpose while calculating the performance of both the algorithms. The experiment as performed by the scholar compared the Accuracy, Precision, recall and Computational Time of existing DBN algorithm with proposed SPELM Algorithm. The findings have show better performance of SPELM; when compared its accuracy of 93.20% as against 52.8% of DBN algorithm;69.492 Precision of SPELM as against 66.836 DBN and 90.8 seconds of Computational time taken by SPELM as against 102 seconds DBN Algorithm.

In this paper, we leverage previous work in the Identity Ecosystem, a Bayesian network mathematical representation of a person's identity, to create a framework to evaluate identity protection systems. Information dynamic is considered and a protection game is formed given that the owner and the attacker both gain some level of control over the status of other PII within the dynamic Identity Ecosystem. We present a policy iteration algorithm to solve the optimal policy for the game and discuss its convergence. Finally, an evaluation and comparison of identity protection strategies is provided given that an optimal policy is used against different protection policies. This study is aimed to understand the evolutionary process of identity theft and provide a framework for evaluating different identity protection strategies and future privacy protection system.

Aiming at the problem that one-dimensional parameter optimization in insider threat detection using deep learning will lead to unsatisfactory overall performance of the model, an insider threat detection method based on adaptive optimization DBN by grid search is designed. This method adaptively optimizes the learning rate and the network structure which form the two-dimensional grid, and adaptively selects a set of optimization parameters for threat detection, which optimizes the overall performance of the deep learning model. The experimental results show that the method has good adaptability. The learning rate of the deep belief net is optimized to 0.6, the network structure is optimized to 6 layers, and the threat detection rate is increased to 98.794%. The training efficiency and the threat detection rate of the deep belief net are improved.

Zero-day attacks continue to challenge the enterprise network security defense. A zero-day attack path is formed when a multi-step attack contains one or more zero-day exploits. Detecting zero-day attack paths in time could enable early disclosure of zero-day threats. In this paper, we propose a probabilistic approach to identify zero-day attack paths and implement a prototype system named ZePro. An object instance graph is first built from system calls to capture the intrusion propagation. To further reveal the zero-day attack paths hiding in the instance graph, our system constructs an instance-graph-based Bayesian network. By leveraging intrusion evidence, the Bayesian network can quantitatively compute the probabilities of object instances being infected. The object instances with high infection probabilities reveal themselves and form the zero-day attack paths. The experiment results show that our system can effectively identify zero-day attack paths.

Internet of Things (IoT) era has gradually entered our life, with the rapid development of communication and embedded system, IoT technology has been widely used in many fields. Therefore, to maintain the security of the IoT system is becoming a priority of the successful deployment of IoT networks. This paper presents an intrusion detection model based on improved Deep Belief Network (DBN). Through multiple iterations of the genetic algorithm (GA), the optimal network structure is generated adaptively, so that the intrusion detection model based on DBN achieves a high detection rate. Finally, the KDDCUP data set was used to simulate and evaluate the model. Experimental results show that the improved intrusion detection model can effectively improve the detection rate of intrusion attacks.

Cyber defense can no longer be limited to intrusion detection methods. These systems require malicious activity to enter an internal network before an attack can be detected. Having advanced, predictive knowledge of future attacks allow a potential victim to heighten security and possibly prevent any malicious traffic from breaching the network. This paper investigates the use of Auto-Regressive Integrated Moving Average (ARIMA) models and Bayesian Networks (BN) to predict future cyber attack occurrences and intensities against two target entities. In addition to incident count forecasting, categorical and binary occurrence metrics are proposed to better represent volume forecasts to a victim. Different measurement periods are used in time series construction to better model the temporal patterns unique to each attack type and target configuration, seeing over 86% improvement over baseline forecasts. Using ground truth aggregated over different measurement periods as signals, a BN is trained and tested for each attack type and the obtained results provided further evidence to support the findings from ARIMA. This work highlights the complexity of cyber attack occurrences; each subset has unique characteristics and is influenced by a number of potential external factors.

Inference based techniques are one of the major approaches to analyze DNS data and detect malicious domains. The key idea of inference techniques is to first define associations between domains based on features extracted from DNS data. Then, an inference algorithm is deployed to infer potential malicious domains based on their direct/indirect associations with known malicious ones. The way associations are defined is key to the effectiveness of an inference technique. It is desirable to be both accurate (i.e., avoid falsely associating domains with no meaningful connections) and with good coverage (i.e., identify all associations between domains with meaningful connections). Due to the limited scope of information provided by DNS data, it becomes a challenge to design an association scheme that achieves both high accuracy and good coverage. In this paper, we propose a new approach to identify domains controlled by the same entity. Our key idea is an in-depth analysis of active DNS data to accurately separate public IPs from dedicated ones, which enables us to build high-quality associations between domains. Our scheme avoids the pitfall of naive approaches that rely on weak "co-IP" relationship of domains (i.e., two domains are resolved to the same IP) that results in low detection accuracy, and, meanwhile, identifies many meaningful connections between domains that are discarded by existing state-of-the-art approaches. Our experimental results show that the proposed approach not only significantly improves the domain coverage compared to existing approaches but also achieves better detection accuracy. Existing path-based inference algorithms are specifically designed for DNS data analysis. They are effective but computationally expensive. To further demonstrate the strength of our domain association scheme as well as improve the inference efficiency, we construct a new domain-IP graph that can work well with the generic belief propagation algorithm. Through comprehensive experiments, we show that this approach offers significant efficiency and scalability improvement with only a minor impact to detection accuracy, which suggests that such a combination could offer a good tradeoff for malicious domain detection in practice.

Affective1 engineering is a methodology of designing products by collecting customer affective needs and translating them into product designs. It usually begins with questionnaire surveys to collect customer affective demands and responses. However, this process is expensive, which can only be conducted periodically in a small scale. With the rapid development of e-commerce, a larger number of customer product reviews are available on the Internet. Many studies have been done using opinion mining and sentiment analysis. However, the existing studies focus on the polarity classification from a single perspective (such as positive and negative). The classification of multiple affective attributes receives less attention. In this paper, 3-class classifications of four different affective attributes (i.e. Soft-Hard, Appealing-Unappealing, Handy-Bulky, and Reliable-Shoddy) are performed by using two classical machine learning algorithms (i.e. Softmax regression and Support Vector Machine) and two deep learning methods (i.e. Restricted Boltzmann machines and Deep Belief Network) on an Amazon dataset. The results show that the accuracy of deep learning methods is above 90%, while the accuracy of classical machine learning methods is about 64%. This indicates that deep learning methods are significantly better than classical machine learning methods.

3D modeling usually refers to be the use of 3D software to build production through the virtual 3D space model with 3D data. At present, most 3D modeling software such as 3dmax, FLAC3D and Midas all need adjust models to get a satisfactory model or by coding a precise modeling. There are many matters such as complicated steps, strong profession, the high modeling cost. Aiming at this problem, the paper presents a new 3D modeling methods which is based on Deep Belief Networks (DBN) and Interactive Evolutionary Algorithm (IEA). Following this method, firstly, extract characteristic vectors from vertex, normal, surfaces of the imported model samples. Secondly, use the evolution strategy, to extract feature vector for stochastic evolution by artificial grading control the direction of evolution, and in the process to extract the characteristics of user preferences. Then, use evolution function matrix to establish the fitness approximation evaluation model, and simulate subjective evaluation. Lastly, the user can control the whole machine simulation evaluation process at any time, and get a satisfactory model. The experimental results show that the method in this paper is feasible.

In recent years, new and devastating cyber attacks amplify the need for robust cybersecurity practices. Preventing novel cyber attacks requires the invention of Intrusion Detection Systems (IDSs), which can identify previously unseen attacks. Many researchers have attempted to produce anomaly - based IDSs, however they are not yet able to detect malicious network traffic consistently enough to warrant implementation in real networks. Obviously, it remains a challenge for the security community to produce IDSs that are suitable for implementation in the real world. In this paper, we propose a new approach using a Deep Belief Network with a combination of supervised and unsupervised machine learning methods for port scanning attacks detection - the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. Our proposed approach will be tested with network security datasets and compared with previously existing methods.

The monitoring circuit is widely applied in radiation environment and it is of significance to study the circuit reliability with the radiation effects. In this paper, an intelligent analysis method based on Deep Belief Network (DBN) and Support Vector Method is proposed according to the radiation experiments analysis of the monitoring circuit. The Total Ionizing Dose (TID) of the monitoring circuit is used to identify the circuit degradation trend. Firstly, the output waveforms of the monitoring circuit are obtained by radiating with the different TID. Subsequently, the Deep Belief Network Model is trained to extract the features of the circuit signal. Finally, the Support Vector Machine (SVM) and Support Vector Regression (SVR) are applied to classify and predict the remaining useful life (RUL) of the monitoring circuit. According to the experimental results, the performance of DBN-SVM exceeds DBN method for feature extraction and classification, and SVR is effective for predicting the degradation.

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.

Accurate model is very important for the control of nonlinear system. The traditional identification method based on shallow BP network is easy to fall into local optimal solution. In this paper, a modeling method for nonlinear system based on improved Deep Belief Network (DBN) is proposed. Continuous Restricted Boltzmann Machine (CRBM) is used as the first layer of the DBN, so that the network can more effectively deal with the actual data collected from the real systems. Then, the unsupervised training and supervised tuning were combine to improve the accuracy of identification. The simulation results show that the proposed method has a higher identification accuracy. Finally, this improved algorithm is applied to identification of diameter model of silicon single crystal and the simulation results prove its excellent ability of parameters identification.

Deep neural networks (DNNs) are effective machine learning models to solve a large class of recognition problems, including the classification of nonlinearly separable patterns. The applications of DNNs are, however, limited by the large size and high energy consumption of the networks. Recently, stochastic computation (SC) has been considered to implement DNNs to reduce the hardware cost. However, it requires a large number of random number generators (RNGs) that lower the energy efficiency of the network. To overcome these limitations, we propose the design of an energy-efficient deep belief network (DBN) based on stochastic computation. An approximate SC activation unit (A-SCAU) is designed to implement different types of activation functions in the neurons. The A-SCAU is immune to signal correlations, so the RNGs can be shared among all neurons in the same layer with no accuracy loss. The area and energy of the proposed design are 5.27% and 3.31% (or 26.55% and 29.89%) of a 32-bit floating-point (or an 8-bit fixed-point) implementation. It is shown that the proposed SC-DBN design achieves a higher classification accuracy compared to the fixed-point implementation. The accuracy is only lower by 0.12% than the floating-point design at a similar computation speed, but with a significantly lower energy consumption.

Program analyses necessarily make approximations that often lead them to report true alarms interspersed with many false alarms. We propose a new approach to leverage user feedback to guide program analyses towards true alarms and away from false alarms. Our approach associates each alarm with a confidence value by performing Bayesian inference on a probabilistic model derived from the analysis rules. In each iteration, the user inspects the alarm with the highest confidence and labels its ground truth, and the approach recomputes the confidences of the remaining alarms given this feedback. It thereby maximizes the return on the effort by the user in inspecting each alarm. We have implemented our approach in a tool named Bingo for program analyses expressed in Datalog. Experiments with real users and two sophisticated analyses–-a static datarace analysis for Java programs and a static taint analysis for Android apps–-show significant improvements on a range of metrics, including false alarm rates and number of bugs found.

Traffic classification, i.e. associating network traffic to the application that generated it, is an important tool for several tasks, spanning on different fields (security, management, traffic engineering, R&D). This process is challenged by applications that preserve Internet users' privacy by encrypting the communication content, and even more by anonymity tools, additionally hiding the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide (repeatable) classification results with the aim of investigating to what degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of the other considered anonymity tools, using machine learning approaches based on the sole statistical features. To this end, four classifiers are trained and tested on the dataset: (i) Naïve Bayes, (ii) Bayesian Network, (iii) C4.5, and (iv) Random Forest. Results show that the three considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.99%), telling even the specific application generating the traffic (with an accuracy of 98.00%).

This paper combines FMEA and n2 approaches in order to create a methodology to determine risks associated with the components of an underwater system. This methodology is based on defining the risk level related to each one of the components and interfaces that belong to a complex underwater system. As far as the authors know, this approach has not been reported before. The resulting information from the mentioned procedures is combined to find the system's critical elements and interfaces that are most affected by each failure mode. Finally, a calculation is performed to determine the severity level of each failure mode based on the system's critical elements.