An Intrusion Detection System Model in a Local Area Network using Different Machine Learning Classifiers

2021 ◽  
Author(s):  
Asma Aljohani ◽  
Anas Bushnag
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Local Area Network ◽  
Detection System ◽  
Local Area ◽  
System Model ◽  
Area Network ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Implementasi Intrusion Detection System (IDS) Pada Keamanan PC Server Terhadap Serangan Flooding Data

2018 ◽  
Vol 28 (1) ◽  
Author(s):  
Achmad Hambali Hambali ◽  
Siti Nurmiati
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Local Area Network ◽  
Detection System ◽  
Denial Of Service ◽  
Local Area ◽  
Area Network

Flooding Data adalah jenis serangan Denial of Service (DOS) di mana data flooding menyerangkomputer atau server di jaringan lokal atau internet dengan menghabiskan sumber daya yang dimiliki olehkomputer hingga komputer tidak dapat menjalankan fungsinya dengan baik sehingga tidak secara langsungmencegah pengguna lain dari mendapatkan akses ke layanan dari komputer yang diserang. Penelitian ini untukmenganalisis indikasi serangan dan menjaga keamanan sistem dari ancaman banjir data. Untuk itu kitamembutuhkan alat deteksi yang dapat mengenali keberadaan serangan flooding data dengan mengetuk paketdata dan kemudian membandingkannya dengan aturan basis data IDS (berisi paket serangan tanda tangan).Mesin IDS akan membaca peringatan dari IDS (seperti jenis serangan dan penyadap alamat IP) untukmeminimalkan data serangan flooding terhadap LAN (Local Area Network) dan server. Metode pengujian dataserangan banjir dengan menggunakan metode pengujian penetrasi. Tiga sampel uji adalah serangan floodingdata terhadap ICMP, UDP dan protokol TCP menggunakan aplikasi Flooding data. Hasil yang diperolehketika menguji data serangan flooding di mana sensor sensor deteksi dapat mendeteksi semua serangan dansemua sampel serangan dapat dicegah atau disaring menggunakan sistem keamanan jaringan berbasisfirewall.

scholarly journals Genetic algorithm for intrusion detection system in computer network

2020 ◽  
Vol 19 (3) ◽  
pp. 1670
Author(s):  
Hamizan Suhaimi ◽  
Saiful Izwan Suliman ◽  
Afdallyna Fathiyah Harun ◽  
Roslina Mohamad ◽  
Yuslinda Wati Mohamad Yusof ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Local Area Network ◽  
Detection System ◽  
Local Area ◽  
Small Scale ◽  
Area Network ◽  
Network Intrusion

<span>Internet connection nowadays has become one of the essential requirements to execute our daily activities effectively. Among the major applications of wide Internet connections is local area network (LAN) which connects all internet-enabled devices in a small-scale area such as office building, computer lab etc. This connection will allow legit user to access the resources of the network anywhere as long as authorization is acquired. However, this might be seen as opportunities for some people to illegally access the network. Hence, the occurrence of network hacking and privacy breach. Therefore, it is very vital for a computer network administrator to install a very protective and effective method to detect any network intrusion and, secondly to protect the network from illegal access that can compromise the security of the resources in the network. These resources include sensitive and confidential information that could jeopardise someone’s life or sovereignty of a country if manipulated by wrong hands.  In Network Intrusion Detection System (NIDS) framework, apart from detecting unauthorized access, it is equally important to recognize the type of intrusions in order for the necessary precautions and preventive measures to take place. This paper presents the application of Genetic Algorithm (GA) and its steps in performing intrusion detection process. Standard benchmark dataset known as KDD’99 cup was utilized with forty-one distinctive features representing the identity of network connections. Results presented demonstrate the effectiveness of the proposed method and warrant good research focus as it promises exciting discovery in solving similar-patent of problems.   </span>

scholarly journals Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers

2021 ◽  
Vol 1 (2) ◽  
pp. 22-28
Author(s):  
Surafel Mehari Atnafu ◽  
Anuja Kumar Acharya
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Nearest Neighbor ◽  
Detection System ◽  
Machine Learning Techniques ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Learning Techniques

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

scholarly journals Optimal Performance for Intrusion Detection in WirelessLan Network Using Data MiningTechniques

2021 ◽  
Vol 12 (9) ◽  
Author(s):  
K. Raja, Et. al.
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Detection System ◽  
Local Area Networks ◽  
Local Area ◽  
Transport Layer ◽  
Area Network ◽  
Using Data

The objective of this paper is to identify the intruder of the wireless local area network based on the network and transport layer while accessing the internet within organizations and industries. The Intrusion detection system is the security that attempts to identify anomalies attributes who are trying to misuse a network without authorization and those who have legitimate access to the system but are abusing their privileges. The fact of the existing system deals with a firewall to protect and detect the unauthorized person using Wireless Local Area Network. Since the administrator may block or unblock the intruder based on the priority. This paper presents an enhanced framework, to detect and monitor the anomalies in the wireless sensor networks in an organization or an institution. The proposed approach to detect and filter the intruder in the wireless local area networks. Hence optimize the intrusion detection system in the particular organization or industries. The proposed IDS results are compared with the existing Decision Tree, Naive Bayes, and Random Forest algorithms.

scholarly journals Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers

2021 ◽  
pp. 22-28
Author(s):  
Surafel Mehari Atnafu ◽  
◽  
Prof (Dr.) Anuja Kumar Acharya ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Nearest Neighbor ◽  
Detection System ◽  
Machine Learning Techniques ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Learning Techniques

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an efficient Intrusion Detection System face a number of challenges. These challenges are false detection and the data contain high number of features. Currently many researchers use machine learning techniques to overcome the limitation of intrusion detection and increase the efficiency of intrusion detection for correctly identify the packet either the packet is normal or malicious. Many machine-learning techniques use in intrusion detection. However, the question is which machine learning classifiers has been potentially to address intrusion detection issue in network security environment. Choosing the appropriate machine learning techniques required to improve the accuracy of intrusion detection system. In this work, three machine learning classifiers are analyzed. Support vector Machine, Naïve Bayes Classifier and K-Nearest Neighbor classifiers. These algorithms tested using NSL KDD dataset by using the combination of Chi square and Extra Tree feature selection method and Python used to implement, analyze and evaluate the classifiers. Experimental result show that K-Nearest Neighbor classifiers outperform the method in categorizing the packet either is normal or malicious.

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