scholarly journals Erdos – Renyi Random Graph and Machine Learning Based Botnet Detection

2020 ◽  
Vol 9 (5) ◽  
pp. 1037-1040
Keyword(s):  
Machine Learning ◽  
Probability Theory ◽  
Graph Theory ◽  
Complex Network ◽  
Random Graphs ◽  
Connected Graph ◽  
Free Graph ◽  
Botnet Detection ◽  
Scale Free ◽  
Made In

Basically large networks are prone to attacks by bots and lead to complexity. When the complexity occurs then it is difficult to overcome the vulnerability in the network connections. In such a case, the complex network could be dealt with the help of probability theory and graph theory concepts like Erdos – Renyi random graphs, Scale free graph, highly connected graph sequences and so on. In this paper, Botnet detection using Erdos – Renyi random graphs whose patterns are recognized as the number of connections that the vertices and edges made in the network is proposed. This paper also presents the botnet detection concepts based on machine learning.

scholarly journals Small-World and Scale-Free Network Models for IoT Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Insoo Sohn
Keyword(s):  
Graph Theory ◽  
Complex Network ◽  
Network Topology ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Massive Number ◽  
Complex Network Models

It is expected that Internet of Things (IoT) revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

Android botnet detection using machine learning models based on a comprehensive static analysis approach

2021 ◽  
Vol 58 ◽  
pp. 102735
Author(s):  
Wadi’ Hijawi ◽  
Ja’far Alqatawna ◽  
Ala’ M. Al-Zoubi ◽  
Mohammad A. Hassonah ◽  
Hossam Faris
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Analysis Approach ◽  
Learning Models ◽  
Botnet Detection ◽  
Machine Learning Models

scholarly journals Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

2021 ◽  
Author(s):  
Roohallah Alizadehsani ◽  
Mohamad Roshanzamir ◽  
Sadiq Hussain ◽  
Abbas Khosravi ◽  
Afsaneh Koohestani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Probability Theory ◽  
Medical Data

Evaluation of invulnerability of scale-free networks using total information of local sub-graph

2018 ◽  
Vol 29 (08) ◽  
pp. 1850075
Author(s):  
Tingyuan Nie ◽  
Xinling Guo ◽  
Mengda Lin ◽  
Kun Zhao
Keyword(s):  
Mutual Information ◽  
Complex Network ◽  
Fundamental Problem ◽  
The Self ◽  
Practical Significance ◽  
Scale Free ◽  
Total Information ◽  
Scale Free Networks ◽  
Influential Nodes ◽  
Definition Of

The quantification for the invulnerability of complex network is a fundamental problem in which identifying influential nodes is of theoretical and practical significance. In this paper, we propose a novel definition of centrality named total information (TC) which derives from a local sub-graph being constructed by a node and its neighbors. The centrality is then defined as the sum of the self-information of the node and the mutual information of its neighbor nodes. We use the proposed centrality to identify the importance of nodes through the evaluation of the invulnerability of scale-free networks. It shows both the efficiency and the effectiveness of the proposed centrality are improved, compared with traditional centralities.

scholarly journals Network-based machine learning and graph theory algorithms for precision oncology

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Wei Zhang ◽  
Jeremy Chien ◽  
Jeongsik Yong ◽  
Rui Kuang
Keyword(s):  
Machine Learning ◽  
Graph Theory ◽  
Precision Oncology
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