Securing Text Messages Using Graph Theory and Steganography

تامين البيانات يعتبر كعنصر مهم في انظمة لاتصالات وتناقل البيانات. ويكمن دوره الرئيسي في الحفاظ على المعلومات الحساسة بأمان وبشكل متكامل من المرسل إلى المتلقي ، وهناك نوعان من مبادئ الامنية هما التشفير وإخفاء المعلومات ، الأول يعمل على تغيير مظهر المعلومات ويغير من هيئتها في حين أن الثاني يخفيها من الدخلاء. النظام المصمم يقترح طريقة جديدة للتشفير باستخدام خصائص نظرية البيانات ؛ يعطي مفتاحًا تم إنشاؤه بتحويل كلمة السر الى مخطط (graph) من نوع متكامل  complete  ثم نستخرج مصفوفة التجاور adjacency matrix  للمخطط ونستخدمها كمفتاح نهائي لتشفير النص  وذلك باستخدام عملية الضرب (ضرب المصفوفات) للحصول على النص المشفر بعدها يتم استخدام طريقة البت الاقل اهمية Least Significant Bit LSB   لإخفاء الرسالة المشفرة في صورة ملونة في المكون الاخضر G من مكوناتها. وكذلك تم توظيف معادلة تحليل PSNR والتي اثبتت كفاءة النظام في اخفاء الرسالة باقل تشويش ممكن بحوالي (97-85)  dB لصورة الغلاف قبل وبعد عملية الاخفاء و MSE تتراوح بين  (4.537e-05 -5.27546e-04) و  SSIM=1.0.       Data security is an important component of data communication and transmission systems. Its main role is to keep sensitive information safe and integrated from the sender to the receiver. The proposed system aims to secure text messages through two security principles encryption and steganography. The system produced a novel method for encryption using graph theory properties; it formed a graph from a password to generate an encryption key as a weight matrix of that graph and invested the Least Significant Bit (LSB) method for hiding the encrypted message in a colored image within a green component. Practical experiments of (perceptibility, capacity, and robustness) were calculated using similarity measures like PSNR, MSE, and SSIM. These measures had proved the efficiency of the system for image quality and hiding messages with PSNR ratio more than 85 dB, MSE ranged (4.537e-05  to 5.27546e-04) and SSIM=1.0 for using a cover file with size ranged from 256×300 to 1200×760 pixels and message ranged from 16 to 300 characters.

Interactions between financial and environmental networks in OECD countries

We analyse a multiplex of networks between OECD countries during the decade 2002-2010, which consists of five financial layers, given by foreign direct investment, equity securities, short-term, long-term and total debt securities, and five environmental layers, given by emissions of N O x, P M 10 SO 2, CO 2 equivalent and the water footprint associated with international trade. We present a new measure of cross-layer correlations between flows in different layers based on reciprocity. For the assessment of results, we implement a null model for this measure based on the exponential random graph theory. We find that short-term financial flows are more correlated with environmental flows than long-term investments. Moreover, the correlations between reverse financial and environmental flows (i.e. flows of different layers going in opposite directions) are generally stronger than correlations between synergic flows (flows going in the same direction). This suggests a trade-off between financial and environmental layers, where, more financialised countries display higher correlations between outgoing financial flows and incoming environmental flows from lower financialised countries, which could have important policy implications. Five countries are identified as hubs in this finance-environment multiplex: The United States, France, Germany, Belgium-Luxembourg and the United Kingdom.Comment: Supplementary Information provide

DCM: D Number Extended Cognitive Map. Application on Location Selection in SCM

Offshore outsourcing is a widely used management technique for performing business functions with the aim of reducing labor and transportation costs. The selection of locations has a significant influence on the supply chain’s resilience and qualities, but the influence of multiple external factors on the supply chain’s performance in local places in a complex and uncertain environment has not been examined. In this study, we investigated the influence of external factors in a highly uncertain and complicated situation in which relationships between external factors and supply chain resilience are complicated. Furthermore, we proposed a novel model to select locations from a comprehensive perspective. Specifically, the fuzzy cognitive map (FCM) is utilized to simulate the dynamic influence process where the adjacency is aggregated by D numbers. The weights of different resilience capabilities are considered from the perspective of maximizing benefits by using the decision-making trial and evaluation laboratory-analytic network processes (DEMATEL-ANP) model. By comparing the distance to the ideal solutions, we selected the best alternative location. Our results differ from the general case, which reveals that the weights of different capabilities influence selections

Graph-embedding Enhanced Attention Adversarial Autoencoder

When dealing with the graph data in real problems, only part of the nodes in the graph are labeled and the rest are not. A core problem is how to use this information to extend the labeling so that all nodes are assigned a label (or labels). Intuitively we can learn the patterns (or extract some representations) from those labeled nodes and then apply the patterns to determine the membership for those unknown nodes. A majority of previous related studies focus on extracting the local information representations and may suffer from lack of additional constraints which are necessary for improving the robustness of representation. In this work, we presented Graph- embedding enhanced attention Adversarial Autoencoder Networks (Great AAN), a new scalable generalized framework for graph-structured data representation learning and node classification. In our framework, we firstly introduce the attention layers and provide insights on the self-attention mechanism with multi-heads. Moreover, the shortest path length between nodes is incorporated into the self-attention mechanism to enhance the embedding of the node’s structural spatial information. Then a generative adversarial autoencoder is proposed to encode both global and local information and enhance the robustness of the embedded data distribution. Due to the scalability of our approach, it has efficient and various applications, including node classification, a recommendation system, and graph link prediction. We applied this Great AAN on multiple datasets (including PPI, Cora, Citeseer, Pubmed and Alipay) from social science and biomedical science. The experimental results demonstrated that our new framework significantly outperforms several popular methods

MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series

Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp. To be exact, they only want to know how does the data in the past influence which in the future. However, one sensor sometimes intervenes in other sensor such as the speed of wind may cause decrease of temperature. Secondly, there exist two categories of model for time series anomaly detection: prediction model and reconstruction model. Prediction model is adept at learning timely representation while short of capability when faced with sparse anomaly. Conversely, reconstruction model is opposite. Therefore, how can we efficiently get the relationship both in terms of both timestamp and sensors becomes our main topic. Our approach uses GAT, which is originated from graph neural network, to obtain connection between sensors. And LSTM is used to obtain relationships timely. Our approach is also designed to be double headed to calculate both prediction loss and reconstruction loss via VAE(Variational Auto-Encoder). In order to take advantage of two sorts of model, multi-task optimization algorithm is used in this model

Understanding Risk Perception Using Fuzzy Cognitive Maps

When making decision that can have far-researching effects, such as governmental policies or decisions on new technologies, decision-makers use their understanding of the risks that are associated with their choices to guide their decisions. Measuring how people perceive risks can be helpful for understanding and possibly improving the decision-making process. Building on a review of existing methods for investigating risk perceptions, this paper suggests Fuzzy Cognitive Maps (FCM) as a method for investigating differences in risk perception among stakeholders and stakeholder groups. The approach is illustrated with an example of wildfire risk perceptions. Results suggest that FCM can contribute to risk perception studies and provide means to improve Communications between different stakeholder groups and their involvement in the decision-making process. © 2016 Portland International Conference on Management of Engineering and Technology, Inc