Statistical evaluation of spectral methods for anomaly detection in static networks

2019 ◽  
Vol 7 (3) ◽  
pp. 319-352 ◽  
Author(s):  
Tomilayo Komolafe ◽  
A. Valeria Quevedo ◽  
Srijan Sengupta ◽  
William H. Woodall
Keyword(s):  
Social Networks ◽  
Performance Evaluation ◽  
Anomaly Detection ◽  
Spectral Methods ◽  
Ribonucleic Acid ◽  
Statistical Evaluation ◽  
Statistical Properties ◽  
Counter Terrorism ◽  
Wide Range ◽  
Algorithmic Techniques

AbstractThe topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on the statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks.

scholarly journals An efficient hybrid system for anomaly detection in social networks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Md. Shafiur Rahman ◽  
Sajal Halder ◽  
Md. Ashraf Uddin ◽  
Uzzal Kumar Acharjee
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Anomaly Detection ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Security And Privacy ◽  
Support Vector ◽  
The Social ◽  
Wide Range

AbstractAnomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system.

scholarly journals A flexible framework for anomaly Detection via dimensionality reduction

2021 ◽  
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A. Bassett ◽  
M. Kunz
Keyword(s):  
Anomaly Detection ◽  
Dimensionality Reduction ◽  
Dimensional Space ◽  
High Dimensions ◽  
Detection Algorithms ◽  
Latent Space ◽  
Wide Range ◽  
Flexible Framework ◽  
Online Anomaly Detection ◽  
Python Package

AbstractAnomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package that implements the general framework with a wide range of built-in options. This approach identifies the primary prototypes in the data with anomalies detected by their large distances from the prototypes, either in the latent space or in the original, high-dimensional space. DRAMA is tested on a wide variety of simulated and real datasets, in up to 3000 dimensions, and is found to be robust and highly competitive with commonly used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning, and highly unbalanced datasets. Besides, DRAMA naturally provides clustering of outliers for subsequent analysis.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

SYSTEMC IMPLEMENTATION AND PERFORMANCE EVALUATION OF A DECOUPLED GENERAL-PURPOSE MATRIX PROCESSOR

2010 ◽  
Vol 20 (02) ◽  
pp. 103-121 ◽  
Author(s):  
MOSTAFA I. SOLIMAN ◽  
ABDULMAJID F. Al-JUNAID
Keyword(s):  
Performance Evaluation ◽  
Matrix Multiplication ◽  
General Purpose ◽  
System Level ◽  
Memory Latency ◽  
Single Chip ◽  
Wide Range ◽  
Matrix Unit ◽  
And Performance ◽  
Vector Matrix

Technological advances in IC manufacturing provide us with the capability to integrate more and more functionality into a single chip. Today's modern processors have nearly one billion transistors on a single chip. With the increasing complexity of today's system, the designs have to be modeled at a high-level of abstraction before partitioning into hardware and software components for final implementation. This paper explains in detail the implementation and performance evaluation of a matrix processor called Mat-Core with SystemC (system level modeling language). Mat-Core is a research processor aiming at exploiting the increasingly number of transistors per IC to improve the performance of a wide range of applications. It extends a general-purpose scalar processor with a matrix unit. To hide memory latency, the extended matrix unit is decoupled into two components: address generation and data computation, which communicate through data queues. Like vector architectures, the data computation unit is organized in parallel lanes. However, on parallel lanes, Mat-Core can execute matrix-scalar, matrix-vector, and matrix-matrix instructions in addition to vector-scalar and vector-vector instructions. For controlling the execution of vector/matrix instructions on the matrix core, this paper extends the well known scoreboard technique. Furthermore, the performance of Mat-Core is evaluated on vector and matrix kernels. Our results show that the performance of four lanes Mat-Core with matrix registers of size 4 × 4 or 16 elements each, queues size of 10, start up time of 6 clock cycles, and memory latency of 10 clock cycles is about 0.94, 1.3, 2.3, 1.6, 2.3, and 5.5 FLOPs per clock cycle; achieved on scalar-vector multiplication, SAXPY, Givens, rank-1 update, vector-matrix multiplication, and matrix-matrix multiplication, respectively.

Axial Turbine Performance Evaluation. Part B—Optimization With and Without Constraints

1968 ◽  
Vol 90 (4) ◽  
pp. 349-359 ◽  
Author(s):  
O. E. Balje´ ◽  
R. L. Binsley
Keyword(s):  
Reynolds Number ◽  
Performance Evaluation ◽  
Reference Value ◽  
Tip Clearance ◽  
Strong Function ◽  
Turbine Performance ◽  
Wide Range ◽  
Leakage Area ◽  
Partial Admission ◽  
Specific Speed

The maximum obtainable efficiency and associated geometry have been calculated based on the use of generalized loss correlations from Part A and are presented for full and partial admission turbines over a wide range of specific speeds. The calculated effects of varying values of Reynolds number, tip clearance, and trailing edge thickness on turbine performance are presented. Because of the anticipated difficulty in fabricating some of the optimum geometries calculated, the effects of using nonoptimum values of geometric parameters on attainable efficiency have also been investigated. The derating factor for machine Reynolds number is shown to be a strong function of specific speed, varying from 0.96 at a specific speed of 100, to 0.6 at a specific speed of 3, when Reynolds number is 105 compared to a reference value of 106. The derating factor for tip clearance is shown to be similar to what would be expected if the clearance area were considered as a leakage area. The use of blade heights, blade numbers, rotor exit angles, and degrees of reaction varying from the optimum by 25 percent produce maximum derating factors of 0.99, 0.98, 0.985, and 0.97, respectively, when compared to full optimum values.

Extreme Response Prediction for Fixed Offshore Structures by Monte Carlo Time Simulation Technique

2016 ◽  
Author(s):  
M. K. Abu Husain ◽  
N. I. Mohd Zaki ◽  
M. B. Johari ◽  
G. Najafian
Keyword(s):  
Monte Carlo ◽  
Statistical Properties ◽  
Simulation Technique ◽  
Random Wave ◽  
Wave Loading ◽  
Offshore Structure ◽  
Sampling Variability ◽  
Time Simulation ◽  
Wide Range ◽  
Extreme Response

For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structure is essential for safe and economical design of these structures. Many different techniques have been introduced for evaluation of statistical properties of response. In each case, sea-states are characterised by an appropriate water surface elevation spectrum, covering a wide range of frequencies. In reality, the most versatile and reliable technique for predicting the statistical properties of the response of an offshore structure to random wave loading is the time domain simulation technique. To this end, conventional time simulation (CTS) procedure or commonly called Monte Carlo time simulation method is the best known technique for predicting the short-term and long-term statistical properties of the response of an offshore structure to random wave loading due to its capability of accounting for various nonlinearities. However, this technique requires very long simulations in order to reduce the sampling variability to acceptable levels. In this paper, the effect of sampling variability of a Monte Carlo technique is investigated.

scholarly journals A fine desintegration of plants suitable for composite biofuels production

2012 ◽  
Vol 49 (No. 1) ◽  
pp. 7-11 ◽  
Author(s):  
J. Souček ◽  
I. Hanzlíková ◽  
P. Hutla
Keyword(s):  
Energy Consumption ◽  
Technological Process ◽  
Material Properties ◽  
Statistical Evaluation ◽  
Raw Materials ◽  
Correct Choice ◽  
Input And Output ◽  
Input Material ◽  
Wide Range ◽  
Average Size

In case of pressed composite biofuels production the important part of the production process is the input row materials disintegration. In dependence on disintegrated material properties, disintegration device, grinding stage and technological process there is in practice necessary for disintegration of culm materials 0.5–7% and of wooden species even 0.75–10% of total energetical content of material. A wide range of these figures means that in this sphere of raw materials adaptation can be reached relative high savings through correct choice of technological process and device. The authors of the paper have measured energy consumption of fine disintegration of lignocellulose materials in dependence on particles size and moisture. By the realized measurement of different average size of both input and output particles and consequent statistical evaluation was proved the fiducial energy consumption increase at higher stage of disintegration and higher moisture of the input material. All measurements were carried-out for the grinding mill ŠK 300 and the output particles size was limited by the exchange sieves mesh dimension.

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