scholarly journals Online Anomaly Detection System for Mobile Networks

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7232
Author(s):  
Jesús Burgueño ◽  
Isabel de-la-Bandera ◽  
Jessica Mendoza ◽  
David Palacios ◽  
Cesar Morillas ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Mobile Networks ◽  
Performance Indicators ◽  
Performance Metrics ◽  
Detection System ◽  
Mobile Network ◽  
Relevant Information ◽  
Network Capacity ◽  
Detection Systems ◽  
Network Topologies

The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems.

scholarly journals Autonomous UAV Simulator for Research and Development Applied to 5G Networks

2021 ◽  
Author(s):  
Carlos Eduardo Dias Vinagre Neto ◽  
Ailton Pinto de Oliveira ◽  
Felipe Henrique Bastos e Bastos ◽  
Emerson Oliveira Junior ◽  
Aldebaro Klautau
Keyword(s):  
Performance Indicators ◽  
Communication Channel ◽  
Mobile Network ◽  
Network Capacity ◽  
Base Stations ◽  
5G Networks ◽  
Simulation Tools ◽  
Channel Evolution ◽  
Product Delivery ◽  
Intense Research

Unmanned aerial vehicles (UAVs) are being used in many applications,such as surveillance and product delivery. Currently, manyUAVs are controlled by WiFi or proprietary radio technologies.However, it is envisioned that 5G and beyond 5G (B5G) networkscan connect the UAVs and increase the overall security due to improvedcontrol by operators and governments. Soon, UAVs willalso be used as mobile radio base stations to extend reach or improvethe network capacity. All this motivates intense research on5G technologies for supporting UAV-based applications. However,there are currently few simulation tools for testing and investigatingtelecommunication systems that involve UAV solutions. Forinstance, modern 5G networks use multiple antennas that enablebeamforming. A realistic simulation, in this case, requires not onlysupport for beamforming but also for realistic UAV trajectories,which impact the communication channel evolution over time. Toevaluate scenarios with connected UAVs, this paper presents a toolthat simulates flights in a virtual environment, gathers informationabout the channels among UAVs and the mobile network, andcalculates performance indicators regarding the communicationsystem.

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 Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3825 ◽  
Author(s):  
Rony Kumer Saha
Keyword(s):  
Energy Efficiency ◽  
Mobile Networks ◽  
Spectral Efficiency ◽  
Spectrum Sharing ◽  
Performance Metrics ◽  
Interference Management ◽  
Mobile Network ◽  
System Level ◽  
Small Cells ◽  
Generic Model

In this paper, we propose a technique to share the licensed spectrums of all mobile network operators (MNOs) of a country with in-building small cells per MNO by exploiting the external wall penetration loss of a building and introducing the time-domain eICIC technique. The proposed technique considers allocating the dedicated spectrum Bop per MNO only its to outdoor macro UEs, whereas the total spectrum of all MNOs of the country Bco to its small cells indoor per building such that technically any small indoor cell of an MNO can have access to Bco instead of merely Bop assigned only to the MNO itself. We develop an interference management strategy as well as an algorithm for the proposed technique. System-level capacity, spectral efficiency, and energy efficiency performance metrics are derived, and a generic model for energy efficiency is presented. An optimal amount of small indoor cell density in terms of the number of buildings L carrying these small cells per MNO to trade-off the spectral efficiency and the energy efficiency is derived. With the system-level numerical and simulation results, we define an optimal value of L for a dense deployment of small indoor cells of an MNO and show that the proposed spectrum sharing technique can achieve massive indoor capacity, spectral efficiency, and energy efficiency for the MNO. Finally, we demonstrate that the proposed spectrum sharing technique could meet both the spectral efficiency and the energy efficiency requirements for 5G mobile networks for numerous traffic arrival rates to small indoor cells per building of an MNO.

Intrusion Detection Systems Alerts Reduction

2018 ◽  
pp. 255-275 ◽  
Author(s):  
Aymen Akremi ◽  
Hassen Sallay ◽  
Mohsen Rouached
Keyword(s):  
Intrusion Detection ◽  
High Speed ◽  
Detection System ◽  
Search Space ◽  
Relevant Information ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Rules Set ◽  
Intrusion Alerts

Investigators search usually for any kind of events related directly to an investigation case to both limit the search space and propose new hypotheses about the suspect. Intrusion detection system (IDS) provide relevant information to the forensics experts since it detects the attacks and gathers automatically several pertinent features of the network in the attack moment. Thus, IDS should be very effective in term of detection accuracy of new unknown attacks signatures, and without generating huge number of false alerts in high speed networks. This tradeoff between keeping high detection accuracy without generating false alerts is today a big challenge. As an effort to deal with false alerts generation, the authors propose new intrusion alert classifier, named Alert Miner (AM), to classify efficiently in near real-time the intrusion alerts in HSN. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance.

scholarly journals Suspicious Human Activity Detection System

2020 ◽  
Vol 2 (4) ◽  
pp. 216-221
Author(s):  
Pankaj Bhambri ◽  
Sachin Bagga ◽  
Dhanuka Priya ◽  
Harnoor Singh ◽  
Harleen Kaur Dhiman
Keyword(s):  
Artificial Intelligence ◽  
Anomaly Detection ◽  
Detection System ◽  
Behavioral Analysis ◽  
Activity Detection ◽  
Credit Score ◽  
Detection Systems ◽  
Score Card ◽  
Fitness Tracking ◽  
Human Activity Detection

In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.

scholarly journals Explainable AI: Using Shapley Value to Explain Complex Anomaly Detection ML-Based Systems

2020 ◽  
Author(s):  
Jinying Zou ◽  
Ovanes Petrosian
Keyword(s):  
Anomaly Detection ◽  
Shapley Value ◽  
Detection System ◽  
Single Event ◽  
Detection Algorithms ◽  
Detection Systems ◽  
Security Applications ◽  
Sequence Of Events ◽  
The Shapley Value ◽  
Explainable Ai

Generally, Artificial Intelligence (AI) algorithms are unable to account for the logic of each decision they take during the course of arriving at a solution. This “black box” problem limits the usefulness of AI in military, medical, and financial security applications, among others, where the price for a mistake is great and the decision-maker must be able to monitor and understand each step along the process. In our research, we focus on the application of Explainable AI for log anomaly detection systems of a different kind. In particular, we use the Shapley value approach from cooperative game theory to explain the outcome or solution of two anomaly-detection algorithms: Decision tree and DeepLog. Both algorithms come from the machine learning-based log analysis toolkit for the automated anomaly detection “Loglizer”. The novelty of our research is that by using the Shapley value and special coding techniques we managed to evaluate or explain the contribution of both a single event and a grouped sequence of events of the Log for the purposes of anomaly detection. We explain how each event and sequence of events influences the solution, or the result, of an anomaly detection system.

scholarly journals Urban Air Mobility—A 6G Use Case?

2021 ◽  
Vol 2 ◽  
Author(s):  
Shuja Ansari ◽  
Ahmad Taha ◽  
Kia Dashtipour ◽  
Yusuf Sambo ◽  
Qammer H. Abbasi ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Performance Indicators ◽  
Mobile Network ◽  
Key Performance Indicators ◽  
Network Capacity ◽  
Base Station ◽  
Transport Systems ◽  
Urban Air ◽  
Aerial Vehicles ◽  
Aerial Transport

The increasing popularity of Unmanned Aerial Vehicles (UAV) has resulted in exponential growth of the market owing to numerous applications that have been facilitated by advances in battery technology and wireless communications. Given the successes of UAVs thus far, researchers are already gearing towards aerial transport systems that consist of dense deployment of both UAVs and Personal Aerial Vehicles (PAVs) with human passengers. Although the fifth-generation mobile network (5G) key performance indicators have been optimised to support drone use cases for both high data rates and low latency applications, future aerial transport systems will require stricter network key performance indicators to support the expected massive deployment of aerial vehicles taking into account network capacity and distance between the base station and the aerial vehicles, among others. In this article, we present our perspective, vision, architecture, requirements and key performance indicators for future aerial wireless networks supported by 6G for Urban Air Mobility (UAM). Furthermore, we review key enabling technologies and discuss future challenges for incorporating aerial wireless networks in 6G.

Threat Hunting in Windows Using Big Security Log Data

2020 ◽  
pp. 168-188 ◽  
Author(s):  
Mohammad Rasool Fatemi ◽  
Ali A. Ghorbani
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
Sources Of Information ◽  
Detection Systems ◽  
System Logs ◽  
Analysis System ◽  
Anomaly Detection System

System logs are one of the most important sources of information for anomaly and intrusion detection systems. In a general log-based anomaly detection system, network, devices, and host logs are all collected and used together for analysis and the detection of anomalies. However, the ever-increasing volume of logs remains as one of the main challenges that anomaly detection tools face. Based on Sysmon, this chapter proposes a host-based log analysis system that detects anomalies without using network logs to reduce the volume and to show the importance of host-based logs. The authors implement a Sysmon parser to parse and extract features from the logs and use them to perform detection methods on the data. The valuable information is successfully retained after two extensive volume reduction steps. An anomaly detection system is proposed and performed on five different datasets with up to 55,000 events which detects the attacks using the preserved logs. The analysis results demonstrate the significance of host-based logs in auditing, security monitoring, and intrusion detection systems.

scholarly journals A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8320
Author(s):  
Abebe Diro ◽  
Naveen Chilamkurti ◽  
Van-Doan Nguyen ◽  
Will Heyne
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Resource Constraints ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Detection Systems ◽  
Massive Number ◽  
Iot Devices ◽  
Detection Schemes

The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities of the IoT have sparked concerns for wide adoption and applications. Unlike traditional information technology (I.T.) systems, the IoT environment is challenging to secure due to resource constraints, heterogeneity, and distributed nature of the smart devices. This makes it impossible to apply host-based prevention mechanisms such as anti-malware and anti-virus. These challenges and the nature of IoT applications call for a monitoring system such as anomaly detection both at device and network levels beyond the organisational boundary. This suggests an anomaly detection system is strongly positioned to secure IoT devices better than any other security mechanism. In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies.

scholarly journals How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 255
Author(s):  
Josip Lorincz ◽  
Zonimir Klarin
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Mobile Networks ◽  
Access Network ◽  
Mobile Network ◽  
Network Capacity ◽  
Base Stations ◽  
Density Class ◽  
Radio Access ◽  
The Impact

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

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