scholarly journals THE ANALYSIS OF WEB SERVER SECURITY FOR MULTIPLE ATTACKS IN THE TIC TIMOR IP NETWORK

2020 ◽  
Vol 14 (1) ◽  
pp. 103
Author(s):  
Lilia Ervina Jeronimo Guterres ◽  
Ahmad Ashari
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Research Result ◽  
Web Server ◽  
Traffic Monitoring ◽  
Internet Technology ◽  
The Internet ◽  
It Professionals ◽  
Ip Network ◽  
Network Traffic Monitoring

The current technology is changing rapidly, with the significant growth of the internet technology, cyber threats are becoming challenging for IT professionals in the companies and organisations to guard their system. Especially when all the hacking tools and instructions are freely available on the Internet for beginners to learn how to hack such as stealing data and information. Tic Timor IP is one of the organisations involved and engaged in the data center operation. It often gets attacks from the outside networks. A network traffic monitoring system is fundamental to detect any unknown activities happening within a network. Port scanning is one of the first methods commonly used to attack a network by utilizing several free applications such as Angry IP Scan, Nmap and Low Orbit Ion Cannon (LOIC).  On the other hand, the snort-based Intrusion Detection System (IDS) can be used to detect such attacks that occur within the network perimeter including on the web server. Based on the research result, snort has the ability to detect various types of attack including port scanning attacks and multiple snort rules can be accurately set to protect the network from any unknown threats.  

scholarly journals Teler Real-time HTTP Intrusion Detection at Website with Nginx Web Server

2021 ◽  
Vol 5 (3) ◽  
pp. 327
Author(s):  
Agus Tedyyana ◽  
Osman Ghazali
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Crime Rate ◽  
Detection System ◽  
Web Server ◽  
The Internet ◽  
Brute Force ◽  
Web Servers ◽  
Web Based

Web servers and web-based applications are now widely used, but in this case, the crime rate in cyberspace has also increased. Crime in cyberspace can occur due to the exploitation of how a system works. For example, the way HTTP works are exploited to weaken the webserver. Various tools for attacking the internet are also starting to be easy to find, but so are the tools to detect these attacks. One of the useful tools for detecting attacks and sending warnings against threats is based on the weblogs on the webserver. Many have not reviewed Teler as an intrusion detection system on HTTP on web servers because the existing tools are relatively new. Teler detecting the weblog and run on the terminal with rule resources collected from the community. So here, the researcher tries to implement the use of Teler in detecting HTTP intrusions on a Nginx-based web server. Intrusion is carried out in attacks commonly used by attackers, for example, port scanning and directory brute force using the Nmap and OWASP ZAP tools. Then the detection results will be sent via the Telegram bot to the server admin. From the results of the experiments conducted, it has been found that Teler is still classified as being able to send warning notifications with a delay between the time of detection and the time when the alert is received, no more than 3 seconds.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

scholarly journals Detection of Malicious Servers for Preventing Client-Side Attacks

2021 ◽  
Vol 40 (1) ◽  
pp. 230-240
Author(s):  
Khuda Bux ◽  
Muhammad Yousaf ◽  
Akhtar Hussain Jalbani ◽  
Komal Batool
Keyword(s):  
Intrusion Detection System ◽  
Detection System ◽  
Social Engineering ◽  
The Internet ◽  
Malware Analysis ◽  
Passive Detection ◽  
Active Detection ◽  
Client Side ◽  
Current Systems ◽  
Day By Day

The number of client-side attacks is increasing day-by-day. These attacks are launched by using various methods like phishing, drive-by downloads, click-frauds, social engineering, scareware, and ransomware. To get more advantage with less exertion and time, the attackers are focus on the clients, rather than servers which are more secured as compared to the clients. This makes clients as an easy target for the attackers on the Internet. A number of systems/tools have been created by the security community with various functions for detection of client-side attacks. The discovery of malicious servers that launch the client side attacks can be characterized in two types. First to detect malicious servers with passive detection which is often signature based. Second to detect the malicious servers with active detection often with dynamic malware analysis. Current systems or tools have more focus on identifying malicious servers rather than preventing the clients from those malicious servers. In this paper, we have proposed a solution for the detection and prevention of malicious servers that use the Bro Intrusion Detection System (IDS) and VirusTotal API 2.0. The detected malicious link is then blocked at the gateway.

scholarly journals Intrusion Dataset Over Network Traffic of SDN and TCP/IP Network

2021 ◽  
pp. 694-701
Author(s):  
Karan Shingare ◽  
Rohit Nandurkar ◽  
Prashant Shrivastav ◽  
Shailesh Bendale
Keyword(s):  
Intrusion Detection ◽  
Network Topology ◽  
Network Traffic ◽  
Intrusion Detection System ◽  
Detection System ◽  
Host System ◽  
Ip Network ◽  
The World

As the world is moving toward newer technologies and to meet the requirements of the same adapting toward different network topology. SDN is such example of a network which solves many issues or limitations of a traditional TCP/IP network. As majority of workspace is moving towards SDN, many new vulnerabilities are also emerging, and to protect the network and systems on these networks, in this paper we discuss and propose a dataset which would be helpful in training an intrusion detection system over SDN which would also include the intrusion dataset for traditional TCP/IP network too. We generate this data over SDN topology by attacking the host system present in the network, then analyse the generated data using CICFlowmeter which would give us the desired dataset for intrusion detection.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2562
Author(s):  
Georgios Zachos ◽  
Ismael Essop ◽  
Georgios Mantas ◽  
Kyriakos Porfyrakis ◽  
José C. Ribeiro ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Computational Cost ◽  
Healthcare Sector ◽  
The Internet ◽  
Full Potential ◽  
Log Files ◽  
Wide Range ◽  
Internet Of Medical Things

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

Sign in / Sign up

Export Citation Format

Share Document