scholarly journals Security Metric Methods for Network Multistep Attacks Using AMC and Big Data Correlation Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Hao Hu ◽  
Yuling Liu ◽  
Hongqi Zhang ◽  
Yuchen Zhang
Keyword(s):  
Big Data ◽  
Network Security ◽  
Correlation Analysis ◽  
Transition Probability ◽  
Success Probability ◽  
Transition Probability Matrix ◽  
Data Correlation ◽  
Security Metrics ◽  
Proposed Model ◽  
Attack Scenario

Network security metrics allow quantitatively evaluating the overall resilience of networked systems against attacks. From this aim, security metrics are of great importance to the security-related decision-making process of enterprises. In this paper, we employ absorbing Markov chain (AMC) to estimate the network security combining with the technique of big data correlation analysis. Specifically, we construct the model of AMC using a large amount of alert data to describe the scenario of multistep attacks in the real world. In addition, we implement big data correlation analysis to generate the transition probability matrix from alert stream, which defines the probabilities of transferring from one attack action to another according to a given scenario before reaching one of some attack targets. Based on the probability reasoning, two metric algorithms are designed to estimate the attack scenario as well as the attackers, namely, the expected number of visits (ENV) and the expected success probability (ESP). The superiority is that the proposed model and algorithms assist the administrator in building new scenarios, prioritizing alerts, and ranking them.

scholarly journals Machine learning concepts for correlated Big Data privacy

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sreemoyee Biswas ◽  
Nilay Khare ◽  
Pragati Agrawal ◽  
Priyank Jain
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Correlation Analysis ◽  
Real World ◽  
Data Privacy ◽  
Correlated Data ◽  
Data Correlation ◽  
The Real ◽  
Big Data Privacy ◽  
Real World Datasets

AbstractWith data becoming a salient asset worldwide, dependence amongst data kept on growing. Hence the real-world datasets that one works upon in today’s time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found a correlation among data. The existing data privacy guarantees cannot assure the expected data privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms. Some of the research has considered utilizing a well-known machine learning concept, i.e., Data Correlation Analysis, to understand the relationship between data in a better way. This concept has given some promising results as well. Though it is still concise, the researchers did a considerable amount of research on correlated data privacy. Researchers have provided solutions using probabilistic models, behavioral analysis, sensitivity analysis, information theory models, statistical correlation analysis, exhaustive combination analysis, temporal privacy leakages, and weighted hierarchical graphs. Nevertheless, researchers are doing work upon the real-world datasets that are often large (technologically termed big data) and house a high amount of data correlation. Firstly, the data correlation in big data must be studied. Researchers are exploring different analysis techniques to find the best suitable. Then, they might suggest a measure to guarantee privacy for correlated big data. This survey paper presents a detailed survey of the methods proposed by different researchers to deal with the problem of correlated data privacy and correlated big data privacy and highlights the future scope in this area. The quantitative analysis of the reviewed articles suggests that data correlation is a significant threat to data privacy. This threat further gets magnified with big data. While considering and analyzing data correlation, then parameters such as Maximum queries executed, Mean average error values show better results when compared with other methods. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.

scholarly journals The Theoretical and Experimental Analysis of the Maximal Information Coefficient Approximate Algorithm

2021 ◽  
Vol 9 (1) ◽  
pp. 95-104
Author(s):  
Fubo Shao ◽  
Hui Liu
Keyword(s):  
Big Data ◽  
Correlation Analysis ◽  
Experimental Analysis ◽  
Time Complexity ◽  
Computation Time ◽  
Approximate Algorithm ◽  
Data Correlation ◽  
Information Coefficient ◽  
Maximal Information Coefficient ◽  
Good Preparation

Abstract In the era of big data, correlation analysis is significant because it can quickly detect the correlation between factors. And then, it has been received much attention. Due to the good properties of generality and equitability of the maximal information coefficient (MIC), MIC is a hotspot in the research of correlation analysis. However, if the original approximate algorithm of MIC is directly applied into mining correlations in big data, the computation time is very long. Then the theoretical time complexity of the original approximate algorithm is analyzed in depth and the time complexity is n 2.4 when parameters are default. And the experiments show that the large number of candidate partitions of random relationships results in long computation time. The analysis is a good preparation for the next step work of designing new fast algorithms.

scholarly journals Geographic spatiotemporal big data correlation analysis via the Hilbert–Huang transformation

2017 ◽  
Vol 89 ◽  
pp. 130-141 ◽  
Author(s):  
Weijing Song ◽  
Lizhe Wang ◽  
Yang Xiang ◽  
Albert Y. Zomaya
Keyword(s):  
Big Data ◽  
Correlation Analysis ◽  
Data Correlation

scholarly journals Incorporating Reverse Search for Friend Recommendation with Random Walk

2020 ◽  
Vol 17 (3) ◽  
pp. 291-298
Author(s):  
Qing Yang ◽  
Haiyang Wang ◽  
Mengyang Bian ◽  
Yuming Lin ◽  
Jingwei Zhang
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Social Relationships ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Forward Search ◽  
Search Mode ◽  
Friend Recommendation ◽  
Proposed Model ◽  
Better Than

Recommending friends is an important mechanism for social networks to enhance their vitality and attractions to users. The huge user base as well as the sparse user relationships give great challenges to propose friends on social networks. Random walk is a classic strategy for recommendations, which provides a feasible solution for the above challenges. However, most of the existing recommendation methods based on random walk are only weighing the forward search, which ignore the significance of reverse social relationships. In this paper, we proposed a method to recommend friends by integrating reverse search into random walk. First, we introduced the FP-Growth algorithm to construct both web graphs of social networks and their corresponding transition probability matrix. Second, we defined the reverse search strategy to include the reverse social influences and to collaborate with random walk for recommending friends. The proposed model both optimized the transition probability matrix and improved the search mode to provide better recommendation performance. Experimental results on real datasets showed that the proposed method performs better than the naive random walk method which considered the forward search mode only.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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