Types of Threats and Appropriate Countermeasures for Internet Communications

2021 ◽  
Vol 10 (1) ◽  
pp. 27-37
Author(s):  
Irina-Bristena BACÎŞ
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Configuration Management ◽  
Identity Theft ◽  
Security Threats ◽  
Security Measures ◽  
Protection Measures ◽  
Ip Address ◽  
Network Flooding

Threats can translate into various types of attacks an intruder can take on entities in a network: flooding the target with protocol messages, smurfing (targeted broadcasting of an ICMP protocol-based messaging protocol), distributed attacks that lead to blocking the service for legitimate users, IP address theft and flooding targets with unsolicited emails, identity theft, or fraudulent routing. Against these threats, a variety of security measures can be implemented, such as: configuration management, firewall installation, intrusion detection system installation. Used separately or together, these protection measures can eliminate or even minimize the probability of materializing security threats and preventing attacks on the security features of a system.

scholarly journals PEMANFAATAN INTRUSION DETECTION SYSTEM (IDS) SEBAGAI OTOMATISASI KONFIGURASI FIREWALL BERBASIS WEB SERVICE MENGGUNAKAN ARSITEKTUR REPRESENTATIONAL STATE TRANSFER (REST)

Compiler ◽  
2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Demmy Nanda Awangga ◽  
Haruno Sajati ◽  
Yenni Astuti
Keyword(s):  
Intrusion Detection ◽  
Web Service ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Ip Address ◽  
Final Project ◽  
Representational State Transfer ◽  
State Transfer ◽  
Representational State

Many things can destabilize a computer network connections, both with regard to hardware and software. Therefore, we need a technique for network security, one of them is firewall. The problems that arise in this final project is to build a linux based firewall automation application via web service by using REST (Representational State Transfer) architecture and IDS (Intrusion Detection System). The system buid firewall rules using linux operating system with the help o f 2 pieces o f IDS to detect theactivities of traffic data between the intruder and the server that will be recorded in the IDS database. The system will compare the server with IDS on the router to get the IP address o f the actual intruders, so it will be blocked by the firewall. The applications is used to prevents the ping o f death attack usingweb service and REST protocol so that firewall rules will run automatically.

Performance Analysis of Mail Clients on Low Cost Computer With ELGamal and RSA Using SNORT

2018 ◽  
pp. 332-353
Author(s):  
Sreerama Murthy Kattamuri ◽  
Vijayalakshmi Kakulapati ◽  
Pallam Setty S.
Keyword(s):  
Intrusion Detection ◽  
Low Power ◽  
Intrusion Detection System ◽  
Detection System ◽  
Low Cost ◽  
Raspberry Pi ◽  
Ip Address ◽  
Intrusion Prevention ◽  
Plain Text ◽  
Intrusion Prevention System

An intrusion detection system (IDS) focuses on determining malicious tasks by verifying network traffic and informing the network administrator for restricting the user or source or source IP address from accessing the network. SNORT is an open source intrusion detection system (IDS) and SNORT also acts as an intrusion prevention system (IPS) for monitoring and prevention of security attacks on networks. The authors applied encryption for text files by using cryptographic algorithms like Elgamal and RSA. This chapter tested the performance of mail clients in low cost, low power computer Raspberry Pi, and verified that SNORT is efficient for both algorithms. Within low cost, low power computer, they observed that as the size of the file increases, the run time is constant for compressed data; whereas in plain text, it changed significantly.

scholarly journals A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 583 ◽  
Author(s):  
Muhammad Ashfaq Khan ◽  
Md. Rezaul Karim ◽  
Yangwoo Kim
Keyword(s):  
Intrusion Detection ◽  
Information And Communication Technologies ◽  
Intrusion Detection System ◽  
Large Scale ◽  
Detection System ◽  
Security Threats ◽  
Online Systems ◽  
Network Intrusion ◽  
Lstm Network ◽  
Convolutional Lstm

With the rapid advancements of ubiquitous information and communication technologies, a large number of trustworthy online systems and services have been deployed. However, cybersecurity threats are still mounting. An intrusion detection (ID) system can play a significant role in detecting such security threats. Thus, developing an intelligent and accurate ID system is a non-trivial research problem. Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based on classical machine learning methods that provide less focus on accurate feature selection and classification. Consequently, many known signatures from the attack traffic remain unidentifiable and become latent. Furthermore, since a massive network infrastructure can produce large-scale data, these approaches often fail to handle them flexibly, hence are not scalable. To address these issues and improve the accuracy and scalability, we propose a scalable and hybrid IDS, which is based on Spark ML and the convolutional-LSTM (Conv-LSTM) network. This IDS is a two-stage ID system: the first stage employs the anomaly detection module, which is based on Spark ML. The second stage acts as a misuse detection module, which is based on the Conv-LSTM network, such that both global and local latent threat signatures can be addressed. Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10-fold cross-validation tests.

6LoWPAN: a study on QoS security threats and countermeasures using intrusion detection system approach

2012 ◽  
Vol 25 (9) ◽  
pp. 1189-1212 ◽  
Author(s):  
Anhtuan Le ◽  
Jonathan Loo ◽  
Aboubaker Lasebae ◽  
Mahdi Aiash ◽  
Yuan Luo
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
System Approach ◽  
Detection System ◽  
Security Threats

scholarly journals Analisis Forensik Jaringan Studi Kasus Serangan SQL Injection pada Server Universitas Gadjah Mada

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Resi Utami Putri ◽  
Jazi Eko Istiyanto
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Process Model ◽  
Detection System ◽  
Process Models ◽  
Sql Injection ◽  
Ip Address ◽  
Security Investigation ◽  
Automated Tools

AbstrakForensik jaringan merupakan ilmu keamanan komputer berkaitan dengan investigasi untuk menemukan sumber serangan pada jaringan berdasarkan bukti log, mengidentifikasi, menganalisis serta merekonstruksi ulang kejadian tersebut. Penelitian forensik jaringan dilakukan di Pusat Pelayanan Teknologi Informasi dan Komunikasi (PPTIK) Universitas Gadjah Mada.Metode yang digunakan adalah model proses forensik (The Forensic Process Model) sebuah model proses investigasi forensik digital, yang terdiri dari tahap pengkoleksian, pemeriksaan, analisis dan pelaporan. Penelitian dilakukan selama lima bulan dengan mengambil data dari Intrusion Detection System (IDS) Snort. Beberapa file log digabungkan menjadi satu file log, lalu data dibersihkan agar sesuai untuk penelitian.Berdasarkan hasil penelitian yang telah dilakukan, terdapat 68 IP address  yang melakukan tindakan illegal SQL Injection pada server www.ugm.ac.id. Kebanyakan penyerang menggunakan tools SQL Injection yaitu Havij dan SQLMap sebagai tool otomatis untuk memanfaatkan celah keamanan pada suatu website. Selain itu, ada yang menggunakan skrip Python yaitu berasal dari benua Eropa yaitu di Romania. Kata kunci—forensik jaringan, model proses forensik, SQL injection AbstractNetwork forensic is a computer security investigation to find the sources of the attacks on the network by examining log evidences, identifying, analyzing and reconstructing the incidents. This research has been conducted at The Center of Information System and Communication Service, Gadjah Mada University.The method that used was The Forensic Process Model, a model of the digital investigation process, consisted of collection, examination, analysis, and reporting. This research has been conducted over five months by retrieving data that was collected from Snort Intrusion Detection System (IDS). Some log files were retrieved and merged into a single log file, and then the data cleaned to fit for research.Based on the research, there are 68 IP address was that did illegal action, SQL injection, on server www.ugm.ac.id. Most of attackers using Havij and SQLmap (automated tools to exploit vulnerabilities on a website). Beside that, there was also Python script that was derived from the continent of Europe in Romania. Keywords— Network Forensics, The Forensic Process Models, SQL Injection

scholarly journals Big Data against Security Threats: The SPEAR Intrusion Detection System

2020 ◽  
Author(s):  
Dimitrios Pliatsios ◽  
Panagiotis Sarigiannidis ◽  
Konstantinos Psannis ◽  
Sotirios K. Goudos ◽  
Vasileios Vitsas ◽  
...  
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security Threats

Development of Intrusion Detection System for Security Threats in Internet of Things Using Artificial Neural Network

2019 ◽  
Vol 16 (8) ◽  
pp. 3242-3245
Author(s):  
R. Ramadevi ◽  
N. R. Krishnamoorthy ◽  
D. Marshiana ◽  
Sujatha Kumaran ◽  
N. Aarthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Security Threats ◽  
Data Set ◽  
Artificial Neural

Internet of things (IoT) is a revolutionary technology which changes our life and work. Many industry sectors such as manufacturing, transportation, utilities, health care, consumer electronics and automobiles are invested and adopted towards IoT technology. The major inconvenience with IoT is its safety, as it is prone to attack by hackers. Detection Systems are used to detect these intrusions to protect the information and communication systems. Hence it is essential to design an intrusion detection system for security threats of IoT networks. This paper focuses, on the development of Artificial Neural Network (ANN) based Intrusion Detection System for threat analysis in IoT network. KDD-99 data set with Denial of Service (DoS) type attack is used to train and test three different ANN models. In this research, a Feed Forward Back Propagation (FFBP) network is used to detect the DoS attack. The process of optimization of a FFBP network involves comparison of classification accuracy during both training and testing in terms of true positive and false positive rates. For the data set considered the optimised network has achieved 100% efficiency during both training and testing.

Security Framework Based on SDN

2014 ◽  
Vol 989-994 ◽  
pp. 4690-4693
Author(s):  
Yang Yu ◽  
Yu Nan Wang ◽  
Wei Yang
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Effective Means ◽  
Network System ◽  
Security Threats ◽  
Detection Technology ◽  
Working Principle ◽  
Robust Network

With the growing demand for information, it has a strategic importance for the future of sustainable development how to create a safe and robust network system to ensure the security of important information. Intrusion detection technology can proactively react against intrusion behavior and adjust its strategies in time. So it provides an effective means for network security to minimize or avoid loss when network system is attacked. It is an important part of network security system. This article first explains the current framework and the working principle of SDN. Then it explains the existing security threats of current framework. Next intrusion detection system based on SDN is proposed after the introduction of the intrusion detection system. And we made experiments to verify it. Finally we analyze the lack of the structure and propose some improvements.

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