scholarly journals Penggunaan Metode Signature Based dalam Pengenalan Pola Serangan di Jaringan Komputer

2021 ◽  
Vol 8 (3) ◽  
pp. 517
Author(s):  
Herri Setiawan ◽  
M. Agus Munandar ◽  
Lastri Widya Astuti
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Test Results ◽  
Web Based ◽  
Security Issues ◽  
Attack Patterns ◽  
Attack Data ◽  
Tools And Techniques

<p class="Abstrak">Masalah keamanan jaringan semakin menjadi perhatian saat ini. Sudah semakin banyak <em>tools</em> maupun teknik yang dapat digunakan untuk masuk kedalam sistem secara ilegal, sehingga membuat lumpuh sistem yang ada. Hal tersebut dapat terjadi karena adanya celah dan tidak adanya sistem keamanan yang melindunginya, sehingga sistem menjadi rentan terhadap serangan. Pengenalan pola serangan di jaringan merupakan salah satu upaya agar serangan tersebut dapat dikenali, sehingga mempermudah administrator jaringan dalam menanganinya apabila terjadi serangan. Salah satu teknik yang dapat digunakan dalam keamanan jaringan<em> </em>karena dapat mendeteksi serangan secara <em>real time</em> adalah <em>Intrusion Detection System</em> (IDS), yang dapat membantu administrator dalam mendeteksi serangan yang datang. Penelitian ini menggunakan metode <em>signatured based </em>dan mengujinya dengan menggunakan simulasi. Paket data yang masuk akan dinilai apakah berbahaya atau tidak, selanjutnya digunakan beberapa <em>rule</em> untuk mencari nilai akurasi terbaik. Beberapa <em>rule</em> yang digunakan berdasarkan hasil <em>training </em>dan uji menghasilakan 60% hasil <em>training </em>dan 50% untuk hasil uji <em>rule</em> 1, 50% hasil <em>training </em>dan 75% hasil uji <em>rule</em> 2, 75% hasil <em>training</em> dan hasil uji rule 3, 25% hasil <em>training </em>dan hasil uji <em>rule </em>4, 50% hasil <em>training</em> dan hasil uji untuk <em>rule</em> 5. Hasil pengujian dengan metode <em>signatured based</em> ini mampu mengenali pola data serangan melaui protokol TCP dan UDP, dan <em>monitoring </em>yang dibuat mampu mendeteksi semua serangan dengan tampilan <em>web base.</em></p><p class="Abstrak"><em><br /></em></p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstract"><em>Network security issues are becoming increasingly a concern these days. There are more and more tools and techniques that can be used to enter the system illegally, thus paralyzing the existing system. This can occur due to loopholes and the absence of a security system that protects it so that the system becomes vulnerable to attacks. The recognition of attack patterns on the network is an effort to make these attacks recognizable, making it easier for network administrators to handle them in the event of an attack. One of the techniques that can be used in network security because of a timely attack is the Intrusion Detection System (IDS), which can help administrators in surveillance that comes. This study used a signature-based method and tested it using a simulation. The incoming data packet will be assessed whether it is dangerous or not, then several rules are used to find the best accuracy value. Some rules used are based on the results of training and testing results in 60% training results and 50% for rule 1 test results, 50% training results and 75% rule 2 test results, 75% training results and rule 3 test results, 25% training results and the result of rule 4 test, 50% of training results and test results for rule 5. The test results with the signature-based method can recognize attack data patterns via TCP and UDP protocols, and monitoring is made to be able to detect all attacks with a web-based display.</em></p><p class="Abstrak"><strong><em><br /></em></strong></p>

scholarly journals Intrusion Detection Based on Big Data Fuzzy Analytics

2021 ◽  
Author(s):  
Farah Jemili ◽  
Hajer Bouras
Keyword(s):  
Big Data ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
High Performance ◽  
Detection System ◽  
Training Dataset ◽  
False Alarms ◽  
Massive Datasets ◽  
Detection Rates

In today’s world, Intrusion Detection System (IDS) is one of the significant tools used to the improvement of network security, by detecting attacks or abnormal data accesses. Most of existing IDS have many disadvantages such as high false alarm rates and low detection rates. For the IDS, dealing with distributed and massive data constitutes a challenge. Besides, dealing with imprecise data is another challenge. This paper proposes an Intrusion Detection System based on big data fuzzy analytics; Fuzzy C-Means (FCM) method is used to cluster and classify the pre-processed training dataset. The CTU-13 and the UNSW-NB15 are used as distributed and massive datasets to prove the feasibility of the method. The proposed system shows high performance in terms of accuracy, precision, detection rates, and false alarms.

Campus Network Security Program Based on Snort Network Security Intrusion Detection System

2012 ◽  
Vol 433-440 ◽  
pp. 3235-3240
Author(s):  
Ling Jia
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Collocation Methods ◽  
Campus Network ◽  
Security Risks ◽  
Network Layer ◽  
Defense Strategies

This paper studies the security problems of campus network and summarizes the current on the current security risks and threats that campus network faces, focusing on analysis of attack-defense strategies on DOS network layer, proposing the security program of campus network which uses firewall as well as network security intrusion detection system snort. This paper analyzes the functional advantages of the program and presents in details the setup deployment and collocation methods of network security intrusion detection system based on snort in the campus network, and its application results are also summarized.

ANALISIS NETWORK SECURITY SNORT METODE INTRUSION DETECTION SYSTEM UNTUK OPTIMASI KEAMANAN JARINGAN KOMPUTER

Jursima ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Parningotan Panggabean
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service

<p><em>Perkembangan teknologi informasi, khususnya jaringan komputer memungkinkan terjadinya pertukaran informasi yang mudah, cepat dan semakin kompleks. Keamanan jaringan komputer harus diperhatikan guna menjaga validitas dan integritas data serta informasi yang berada dalam jaringan tersebut. Masalah yang dihadapi adalah adanya Log Bug yang didapatkan pada komputer server Dinas Lingkungan Hidup Kota Batam yang diindikasikan adanya serangan Denial of Service (DoS) pada komputer tersebut. Berdasarkan masalah diatas maka penulis mencoba membuat sebuah penelitian yang berjudul “Analisis Network Security Snort menggunakan metode  Intrusion Detection System (IDS) untuk Optimasi  Keamanan Jaringan Komputer” dan diharapkan dapat mendeteksi serangan Denial of Service (DoS). Intrusion Detection System (IDS)  adalah sebuah tool, metode, sumber daya yang memberikan bantuan untuk melakukan identifikasi, memberikan laporan terhadap aktivitas jaringan komputer. Aplikasi yang digunakan untuk mendeteksi serangan menggunakan Snort. Snort dapat mendeteksi serangan DoS. Serangan DoS dilakukan dengan menggunakan aplikasi Loic.</em></p>

scholarly journals Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM

2020 ◽  
pp. 522-529
Author(s):  
Sadhana Patidar ◽  
Priyanka Parihar ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Model Performance ◽  
Feature Reduction ◽  
Support Vector ◽  
Learning Tools ◽  
Security Issues ◽  
Proposed Model

Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.

scholarly journals Building attack detection system base on machine learning

2021 ◽  
Vol 6 (2) ◽  
pp. 018-032
Author(s):  
Rasha Thamer Shawe ◽  
Kawther Thabt Saleh ◽  
Farah Neamah Abbas
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Network Security ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Data Set ◽  
Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

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