scholarly journals Cluster-Based Antiphishing (CAP) Model for Smart Phones

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mohammad Faisal ◽  
Sa’ed Abed
Keyword(s):  
Detection System ◽  
Portable Devices ◽  
Web Pages ◽  
Test Bed ◽  
True Negative ◽  
Phishing Attacks ◽  
Cap Model ◽  
Smartphone Technology ◽  
Clustering Data ◽  
Save Energy

Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.

scholarly journals Hybrid Internal Anomaly Detection System for IoT: Reactive Nodes with Cross-Layer Operation

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Nanda Kumar Thanigaivelan ◽  
Ethiopia Nigussie ◽  
Seppo Virtanen ◽  
Jouni Isoaho
Keyword(s):  
Anomaly Detection ◽  
False Positive ◽  
Detection System ◽  
False Positive Rate ◽  
Security Analysis ◽  
Abnormal Behavior ◽  
Test Bed ◽  
Positive Rate ◽  
Parent Node ◽  
Anomaly Detection System

We present a hybrid internal anomaly detection system that shares detection tasks between router and nodes. It allows nodes to react instinctively against the anomaly node by enforcing temporary communication ban on it. Each node monitors its own neighbors and if abnormal behavior is detected, the node blocks the packets of the anomaly node at link layer and reports the incident to its parent node. A novel RPL control message, Distress Propagation Object (DPO), is formulated and used for reporting the anomaly and network activities to the parent node and subsequently to the router. The system has configurable profile settings and is able to learn and differentiate between the nodes normal and suspicious activities without a need for prior knowledge. It has different subsystems and operation phases that are distributed in both the nodes and router, which act on data link and network layers. The system uses network fingerprinting to be aware of changes in network topology and approximate threat locations without any assistance from a positioning subsystem. The developed system was evaluated using test-bed consisting of Zolertia nodes and in-house developed PandaBoard based gateway as well as emulation environment of Cooja. The evaluation revealed that the system has low energy consumption overhead and fast response. The system occupies 3.3 KB of ROM and 0.86 KB of RAM for its operations. Security analysis confirms nodes reaction against abnormal nodes and successful detection of packet flooding, selective forwarding, and clone attacks. The system’s false positive rate evaluation demonstrates that the proposed system exhibited 5% to 10% lower false positive rate compared to simple detection system.

scholarly journals Support Vector Machine based Stress Detection System to manage COVID-19 pandemic related stress from ECG signal

2021 ◽  
Vol 20 (1) ◽  
pp. 8-16
Author(s):  
Md Fahim Rizwan ◽  
Rayed Farhad ◽  
Md. Hasan Imam
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Health Condition ◽  
Cardiac Patients ◽  
Gaussian Kernel ◽  
Training Dataset ◽  
Support Vector ◽  
Portable Devices ◽  
Stress Detection ◽  
Specialist Doctor

This study represents a detailed investigation of induced stress detection in humans using Support Vector Machine algorithms. Proper detection of stress can prevent many psychological and physiological problems like the occurrence of major depression disorder (MDD), stress-induced cardiac rhythm abnormalities, or arrhythmia. Stress induced due to COVID -19 pandemic can make the situation worse for the cardiac patients and cause different abnormalities in the normal people due to lockdown condition. Therefore, an ECG based technique is proposed in this paper where the ECG can be recorded for the available handheld/portable devices which are now common to many countries where people can take ECG by their own in their houses and get preliminary information about their cardiac health. From ECG, we can derive RR interval, QT interval, and EDR (ECG derived Respiration) for developing the model for stress detection also. To validate the proposed model, an open-access database named "drivedb” available at Physionet (physionet.org) was used as the training dataset. After verifying several SVM models by changing the ECG length, features, and SVM Kernel type, the results showed an acceptable level of accuracy for Fine Gaussian SVM (i.e. 98.3% for 1 min ECG and 93.6 % for 5 min long ECG) with Gaussian Kernel while using all available features (RR, QT, and EDR). This finding emphasizes the importance of including ventricular polarization and respiratory information in stress detection and the possibility of stress detection from short length data(i.e. form 1 min ECG data), which will be very useful to detect stress through portable ECG devices in locked down condition to analyze mental health condition without visiting the specialist doctor at hospital. This technique also alarms the cardiac patients form being stressed too  much which might cause severe arrhythmogenesis.

scholarly journals Towards a Multi-Layered Phishing Detection

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4540
Author(s):  
Kieran Rendall ◽  
Antonia Nisioti ◽  
Alexios Mylonas
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Detection System ◽  
Single Layer ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Feature Sets ◽  
Phishing Attacks ◽  
Production Environments ◽  
Phishing Detection

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art.

A robust intrusion detection system using machine learning techniques for MANET

2020 ◽  
Vol 24 (3) ◽  
pp. 253-260
Author(s):  
N. Ravi ◽  
G. Ramachandran
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Mobile Computing ◽  
Mobile Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Mobile Network ◽  
Machine Learning Techniques ◽  
Test Bed ◽  
Learning Techniques

Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naïve Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.

scholarly journals Distributed and collaborative Web Change Detection system

2015 ◽  
Vol 12 (1) ◽  
pp. 91-114 ◽  
Author(s):  
Víctor Prieto ◽  
Manuel Álvarez ◽  
Víctor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Change Detection ◽  
Search Engines ◽  
Web Site ◽  
Detection System ◽  
Computational Cost ◽  
Web Pages ◽  
Web Page ◽  
Case Scenario ◽  
Worst Case ◽  
The Web

Search engines use crawlers to traverse the Web in order to download web pages and build their indexes. Maintaining these indexes up-to-date is an essential task to ensure the quality of search results. However, changes in web pages are unpredictable. Identifying the moment when a web page changes as soon as possible and with minimal computational cost is a major challenge. In this article we present the Web Change Detection system that, in a best case scenario, is capable to detect, almost in real time, when a web page changes. In a worst case scenario, it will require, on average, 12 minutes to detect a change on a low PageRank web site and about one minute on a web site with high PageRank. Meanwhile, current search engines require more than a day, on average, to detect a modification in a web page (in both cases).

Mobile Electric Power Energy Efficiency Detection System and Test Method

2014 ◽  
Vol 521 ◽  
pp. 268-272
Author(s):  
Song Song Chen ◽  
Ming Zhong ◽  
Hua Guang Yan
Keyword(s):  
Energy Efficiency ◽  
Energy Conservation ◽  
Detection System ◽  
Test Method ◽  
Financial Resources ◽  
Portable Devices ◽  
Service Market ◽  
Efficiency Assessment ◽  
Service Agencies ◽  
Practical Measure

Energy efficiency detection system plays a important role in energy conservation service market, which is applied in lots of enterprises. Mobile detection system can save manpower and financial resources especially for energy conservation service agencies and energy efficiency assessment institutions, which consists of the portable devices, self-organizing communication network, reliable data collection ability, strong data analysis and display tools. The physical architecture, function architecture of the system was designed and key technologies about the system were also specially studied. The test method about the system was designed, which is used to guide the technicians in the field. The thesis results supply a practical measure for the work of the above mentioned related agencies and institutions.

Three-Dimensional Virtual Assembly of Automatic Fuel Measuring Device of Diesel Engine Test Bed Based on VRML

2011 ◽  
Vol 138-139 ◽  
pp. 870-873
Author(s):  
Pei Gang Jiao ◽  
Shao Fu Shan ◽  
Qiu Hua Miao
Keyword(s):  
Diesel Engine ◽  
Three Dimensional ◽  
Virtual Assembly ◽  
Web Pages ◽  
Engineering System ◽  
Engine Test ◽  
Test Bed ◽  
Measuring Device ◽  
Assembly Method ◽  
Language Network

In this paper, three-dimensional automatic fuel measuring device of diesel engine test bed was created. The function of virtual assembly was achieved using virtual reality modeling language. Network virtual assembly can operate, browse and assemble automatic fuel measuring device remotely by embedding virtual assembly in web pages. The virtual assembly method presented in this paper can be easily extended to deal with many other problems in engineering system if control parameters are modified properly.

scholarly journals A DDoS Attack Detection Method Based on Hybrid Heterogeneous Multiclassifier Ensemble Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Jia ◽  
Xiaohong Huang ◽  
Rujun Liu ◽  
Yan Ma
Keyword(s):  
Ensemble Learning ◽  
Detection Method ◽  
Detection System ◽  
Detection Algorithm ◽  
Attack Detection ◽  
True Negative ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Accuracy And Precision ◽  
Ddos Attack Detection

The explosive growth of network traffic and its multitype on Internet have brought new and severe challenges to DDoS attack detection. To get the higher True Negative Rate (TNR), accuracy, and precision and to guarantee the robustness, stability, and universality of detection system, in this paper, we propose a DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning and design a heuristic detection algorithm based on Singular Value Decomposition (SVD) to construct our detection system. Experimental results show that our detection method is excellent in TNR, accuracy, and precision. Therefore, our algorithm has good detective performance for DDoS attack. Through the comparisons with Random Forest, k-Nearest Neighbor (k-NN), and Bagging comprising the component classifiers when the three algorithms are used alone by SVD and by un-SVD, it is shown that our model is superior to the state-of-the-art attack detection techniques in system generalization ability, detection stability, and overall detection performance.

scholarly journals Diagnostic Development for Advanced Power Systems

1980 ◽  
Author(s):  
J. C. F. Wang ◽  
W. L. Flower ◽  
D. R. Hardesty
Keyword(s):  
Power Systems ◽  
Small Angle ◽  
Detection System ◽  
Pulsed Laser ◽  
Diagnostic Techniques ◽  
Particle Sizing ◽  
Gas Temperature ◽  
Test Bed ◽  
Optical Diagnostic ◽  
Scattering Measurements

The high temperatures, pressures, and particulate densities present in coal-fired advanced power systems place severe limitations on conventional probe techniques for thermometry, velocimetry, and gas and particulate analysis. Although laser-based techniques for measuring gas temperature, velocity, and composition have been demonstrated in relatively clean flame gases, little is known regarding their applicability to measurements in the product streams from coal-fired combustors. Hence, a program has been established at Sandia to develop and assess advanced physical sampling and laser-based optical diagnostic techniques. This paper describes some of the techniques under development, including a small-angle near-forward scattering optical arrangement for particle sizing and a system for making Raman-scattering measurements of gas temperature using a pulsed laser and a gated detection system. Also described here is the atmospheric combustor exhaust simulator (ACES) facility being constructed as the test bed for the diagnostic techniques.

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