scholarly journals A Comparative Study of Network Securities on Mobile Transaction

2021 ◽  
Author(s):  
Geoffrey Tyolaha ◽  
Moses Israel
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Human Factors ◽  
Comparative Study ◽  
Mobile Networks ◽  
Detection Methods ◽  
Mobile Payments ◽  
4G Networks ◽  
Mobile Transaction ◽  
Average User

In recent years, the number of mobile transactions has skyrocketed. Because mobile payments are made on the fly, many consumers prefer the method to the traditional local payment approach. The rise in mobile payments has inspired this study into the security of mobile networks in order to instill trust in those who may be involved in the transaction in some way. This report is a precursor to explain and compare some of the most popular wireless networks that enable mobile payments, from a security standpoint, this research presents, explains, and compares some of the most common wireless networks that enable mobile payments. Threat models in 3G with connections to GSM, WLAN, and 4G networks are classified into four categories: attacks on privacy, attacks on integrity, attacks on availability, and assaults on authentication. In addition, we offer classification countermeasures which are divided into three categories: cryptographic methods, human factors, and intrusion detection methods. One of the most important aspects we analyze is the security procedures that each network employs. Since the security of these networks is paramount, it gives hope to subscribers. In summary, the study aims to verify if mobile payments offer acceptable security to the average user.

scholarly journals Congestion Control Through IDA Process for Malicious Node in MANETs

2020 ◽  
Vol 8 (6) ◽  
pp. 1227-1230
Keyword(s):  
Intrusion Detection ◽  
Congestion Control ◽  
Mobile Networks ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Base Station ◽  
Detection Methods ◽  
Network Efficiency ◽  
Remote Communication ◽  
Detection Approach

Mobile Ad Hoc Network is an array of mobile networks that connect without a base station with each other. The networks are automatically established or on request when certain nodes come into the shared mobility region and agree to collaborate for the transmission and exchange of data. Because of its deployment nature, MANETs are more vulnerable to different type of attack. The reliability of the ad hoc mobile network is difficult due to its essential features including complex topology, a flexible format, limited power, limited bandwidth and remote communication. Mechanisms for protection such as encryption or authentication cannot alone identify harmful nodes in ad-hoc networks. Therefore, we propose and implement the Congestion control through Intrusion Detection Approach (IDA), this intrusion detection approach designed specifically for MANET. This tool can be used even in the case of incorrect wrongdoing reports to identify fraudulent nodes. This approach can identify extremely dangerous nodes, and thus improves protection and network efficiency, relative to other detection methods.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

Intrusion Detection Methods Based on Integrated Deep Learning Model

2021 ◽  
pp. 102177
Author(s):  
ZHENDONG WANG ◽  
YAODI LIU ◽  
DAOJING HE ◽  
SAMMY CHAN
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Learning Model ◽  
Detection Methods ◽  
Deep Learning Model

scholarly journals A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Safa Otoum ◽  
Burak Kantarci ◽  
Hussein Mouftah
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Performance Metrics ◽  
Action Learning ◽  
Smart Devices ◽  
Critical Infrastructures ◽  
State Action ◽  
Detection Techniques ◽  
Depth Analysis

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

scholarly journals CBAM: A Contextual Model for Network Anomaly Detection

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 79
Author(s):  
Henry Clausen ◽  
Gudmund Grov ◽  
David Aspinall
Keyword(s):  
Intrusion Detection ◽  
Network Flows ◽  
Concept Drift ◽  
False Positive Rate ◽  
Real Life ◽  
Remote Access ◽  
Detection Methods ◽  
Short Term ◽  
Deep Model ◽  
Network Intrusion

Anomaly-based intrusion detection methods aim to combat the increasing rate of zero-day attacks, however, their success is currently restricted to the detection of high-volume attacks using aggregated traffic features. Recent evaluations show that the current anomaly-based network intrusion detection methods fail to reliably detect remote access attacks. These are smaller in volume and often only stand out when compared to their surroundings. Currently, anomaly methods try to detect access attack events mainly as point anomalies and neglect the context they appear in. We present and examine a contextual bidirectional anomaly model (CBAM) based on deep LSTM-networks that is specifically designed to detect such attacks as contextual network anomalies. The model efficiently learns short-term sequential patterns in network flows as conditional event probabilities. Access attacks frequently break these patterns when exploiting vulnerabilities, and can thus be detected as contextual anomalies. We evaluated CBAM on an assembly of three datasets that provide both representative network access attacks, real-life traffic over a long timespan, and traffic from a real-world red-team attack. We contend that this assembly is closer to a potential deployment environment than current NIDS benchmark datasets. We show that, by building a deep model, we are able to reduce the false positive rate to 0.16% while effectively detecting six out of seven access attacks, which is significantly lower than the operational range of other methods. We further demonstrate that short-term flow structures remain stable over long periods of time, making the CBAM robust against concept drift.

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