scholarly journals Klasifikasi Data Log Intrusion Detection Sistem (Ids) Dengan Decision Tree C4.5

2019 ◽  
Vol 1 (2) ◽  
pp. 143-153
Author(s):  
Thifal Baraas ◽  
Akbar Juliansyah ◽  
Ahmad Ashril Rizal
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Detection System ◽  
The Internet ◽  
Log Data ◽  
Internet Users ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Data Log

Abstrak Browsing atau kegiatan menjelajahi internet menjadi salah satu aktivitas yang sering dilakukan pada zaman kini. Baik anak-anak hingga orang dewasa menjadi pengguna internet. Akan tetapi para pengguna internet tidak mengetahui jika internet juga bisa menjadi ancaman terutama adanya serangan-serangan yang menyerang sistem keamanan jaringan. Untuk mendeteksi adanya aktivitas yang mencurigakan yang melalui jaringan dibutuhkan bantuan dari IDS (Intrusion Detection Sistem). Ketika terjadi banyak serangan yang masuk, IDS tidak bisa menanganinya secara akurat, hal ini mengakibatkan aktivitas normal di dalam jaringan bisa dianggap sebagai serangan dari hacker atau sebaliknya. Data mining adalah prses yang digunakan untuk menemukan hubungan dari data-data untuk mendapatkan sebuah kesimpulan dari data tersebut. Algoritma C4.5 merupakan salah satu algoritma yang digunakan untuk membuat pohon keputusan. Metode pohon keputusan mengubah fakta yang sangat besar menjadi pohon keputusan yang merepresentasikan aturan. Aturan dapat dengan mudah dipahami dengan bahasa alami. Dengan mengklasifikasi data log IDS dengan algoritma C4.5 dapat mengurangi terjadinya kesalahan IDS dalam menentukan aktivitas yang termasuk serangan atau bukan. Hasil penelitian menunjukkan data log IDS dapat diklasifikasikan dengan algoritma C4.5 dengan tingkat akurasi model adalah 96.371% yang membuktikan bahwa model ini dapat digunakan dalam menentukan aktivitas yang termasuk serangan atau bukan. Abstract Browsing or surfing the internet is one of the activities that are often done today. Both children and adults become internet users. However, internet users do not know the internet can also be a threat, especially the attacks that attack the network security system. To detect suspicious activity through the network, assistance from IDS (Intrusion Detection System) is needed. When there are many incoming attacks, IDS cannot handle it accurately, this results in normal activities on the network can be considered as an attack from hackers or vice versa. Data mining is a process used to find relationships from data to get a conclusion from that data. C4.5 algorithm is one algorithm used to make a decision tree. The decision tree method converts very large facts into decision trees that represent rules. Rules can be easily understood with natural language. By classifying the IDS log data with the C4.5 algorithm it can reduce the occurrence of IDS errors in determining which activities are included or not. The results showed the IDS log data can be classified with the C4.5 algorithm with a 96.371% accuracy rate of the model which proves that this model can be used in determining activities that are included as attacks or not.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2020 ◽  
pp. 23-29
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

scholarly journals Prediksi Mahasiswa Berpotensi Non Aktif Menggunakan Data Mining dalam Decision Tree dan Algoritma C4.5

2019 ◽  
Vol 1 (4) ◽  
pp. 40-46
Author(s):  
Nur Yanti Lumban Gaol
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Dropping Out ◽  
Mining Science ◽  
Prediction Rules ◽  
Tertiary Institutions ◽  
Student Data ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Tree Method

Non-active students are students who do not attend the lecture process and do not pay tuition administration fees within two semesters or more. Reports on students who are not active will have an impact on the quantity of tertiary institutions. Students who are not registered in non-active students will potentially be expelled or dropped out. For this reason, this research was conducted to explore information on potentially non-active students by applying data mining science with the Decision Tree method and C4.5 algorithm. The tested data were sourced from Triguna Dharma Medan College of Information and Computer Management (STMIK). The results of the study get prediction rules for student data that are potentially non-active with a very good degree of accuracy. So this research can be used to avoid students dropping out unilaterally.

scholarly journals A Review on Intrusion Detection System Based on Various Learning Techniques

2021 ◽  
pp. 36-42
Author(s):  
Shiladitya Raj ◽  
◽  
Megha Jain ◽  
Megha kamble ◽  
◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Internet Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Learning Approaches ◽  
Internet Users ◽  
Learning Techniques ◽  
Use Of The Internet

In this world of the Internet, security plays an important role as Internet users grow rapidly. Security in the network is one of the modern periods’ main issues. In the last decade, the exponential growth and massive use of the Internet have enabled system security vulnerabilities a critical aspect. Intrusion detection system to track unauthorized access as well as exceptional attacks through secured networks. Several experiments on the IDS have been carried out in recent years. And to know the current state of machine learning approaches to address the issue of intrusion detection. IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner. This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks.

scholarly journals A Review on Intrusion Detection System Based on Various Learning Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 36-42
Author(s):  
Shiladitya Raj ◽  
Megha Jain ◽  
Megha kamble
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Internet Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Learning Approaches ◽  
Internet Users ◽  
Learning Techniques ◽  
Use Of The Internet

In this world of the Internet, security plays an important role as Internet users grow rapidly. Security in the network is one of the modern periods' main issues. In the last decade, the exponential growth and massive use of the Internet have enabled system security vulnerabilities a critical aspect. Intrusion detection system to track unauthorized access as well as exceptional attacks through secured networks. Several experiments on the IDS have been carried out in recent years. And to know the current state of machine learning approaches to address the issue of intrusion detection. IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner. This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks

scholarly journals Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Lixin Zheng ◽  
Shangguang Wang ◽  
Chao Huang ◽  
...  
Keyword(s):  
Big Data ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fog Computing ◽  
Full Dataset ◽  
Security Challenges ◽  
Protection Technology ◽  
Decision Tree Method

Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

The Role of Data Mining in Intrusion Detection Technology

2008 ◽  
pp. 463-473 ◽  
Author(s):  
Amalia Agathou ◽  
Theodoros Tzouramanis
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Real Time ◽  
Computer System ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Past ◽  
Detection Technology

Over the past few years, the Internet has changed computing as we know it. The more possibilities and opportunities develop, the more systems are subject to attack by intruders. Thus, the big question is about how to recognize and handle subversion attempts. One answer is to undertake the prevention of subversion itself by building a completely secure system. However, the complete prevention of breaches of security does not yet appear to be possible to achieve. Therefore these intrusion attempts need to be detected as soon as possible (preferably in real time) so that appropriate action might be taken to repair the damage. This is what an intrusion detection system (IDS) does. IDSs monitor and analyze the events occurring in a computer system in order to detect signs of security problems. However, intrusion detection technology has not yet reached perfection. This fact has provided data mining with the opportunity to make several important contributions and improvements to the field of IDS technology (Julisch, 2002).

scholarly journals Cyber-Attacks Detection Using Novel IDDMS Framework

2020 ◽  
Author(s):  
Gadekar Ganesh Bhivsen ◽  
Udayabhanu N P G ◽  
Dange Bapusaheb Jalindar ◽  
Vengatesan K ◽  
Abhishek Kumar
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
System Framework ◽  
Cyber Terrorism ◽  
Mining System ◽  
Data Mining System

Security of a data system is a significant property, particularly today when PCs are interconnected by means of the internet. Since no system can be totally secure, the opportune and precise detection of intrusions is essential. Cyber security is the region that manages shielding from cyber terrorism. Cyber-attacks incorporate access control infringement, unapproved intrusions, and disavowal of service just as insider risk. For this reason, IDS were planned. The IDS in the mix with DM can give security to the next level. DM is the way toward presenting inquiries and separating designs, frequently already ambiguous from huge amounts of data utilizing design coordinating or other thinking techniques. This Paper gives the IDDMS (Intrusion Detection with Data Mining system) Framework which is a mix of data mining techniques with the Intrusion detection system, this can be utilized in Cyber-security for accomplishing the next level of service.

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