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 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 Design Of Intrusion Detection System For Dos Attack In 6lowpan And RPL Based IoT Network

2019 ◽  
Vol 8 (11) ◽  
pp. 3840-3844
Keyword(s):  
Intrusion Detection ◽  
Low Power ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Area Network ◽  
Dos Attack ◽  
Resource Constrained ◽  
Ipv6 Protocol

Internet of Things (IoT) is a network spread globally and accommodates maximum things under it. All these things are connected globally using IPv6 protocol which satisfies the need of connecting maximum devices by supporting 2^128 addresses. Because of heavy-weight nature of IPv6 protocol, a compressed version of it known as IPv6 Low Power Personal Area Network (6LoWPAN) protocol is used for a resource-constrained network that communicates over low power and lossy links. In IoT, devices are resource-constrained in terms of low battery power, less processing power, less transceiver power, etc. Also these devices are directly connected to insecure internet hence it is very challenging to maintain security in IoT network. In this paper, we have discussed various attacks on 6LoWPAN and RPL network along with countermeasures to reduce the attacks. DoS attack is one of the severe attacks in IoT which has various patterns of execution. Out of various attacks we have designed Intrusion Detection System (IDS) for Denial of Service (DOS) attack detection using Contiki OS and Cooja simulator.

scholarly journals An Intrusion Detection System for Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Nasim Beigi Mohammadi
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Detection System ◽  
Area Network ◽  
Wormhole Attack ◽  
Detection Systems ◽  
Wireless Mesh ◽  
First Time ◽  
Neighborhood Area Network

Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this thesis, we present the requirements for intrusion detection systems in smart grid, neighborhood area network (NAN) in particular. We propose an intrusion detection system (IDS) that considers the constraints and requirements of the NAN. It captures the communication and computation overhead constraints as well as the lack of a central point to install the IDS. The IDS is distributed on some nodes which are powerful in terms of memory, computation and the degree of connectivity. Our IDS uses an analytical approach for detecting Wormhole attack. We simulate wireless mesh NANs in OPNET Modeler and for the first time, we integrate our analytical model in Maple from MapleSoft with our OPNET simulation model.

scholarly journals Scrutinizing Attacks and Evaluating Performance Appraisal Parameters via Feature Selection in Intrusion Detection System

2021 ◽  
Author(s):  
Navroop Kaur ◽  
Meenakshi Bansal ◽  
Sukhwinder Singh S
Keyword(s):  
Feature Selection ◽  
Performance Evaluation ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Denial Of Service ◽  
Cyber Attacks ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Evaluation Parameters

Abstract In modern times the firewall and antivirus packages are not good enough to protect the organization from numerous cyber attacks. Computer IDS (Intrusion Detection System) is a crucial aspect that contributes to the success of an organization. IDS is a software application responsible for scanning organization networks for suspicious activities and policy rupturing. IDS ensures the secure and reliable functioning of the network within an organization. IDS underwent huge transformations since its origin to cope up with the advancing computer crimes. The primary motive of IDS has been to augment the competence of detecting the attacks without endangering the performance of the network. The research paper elaborates on different types and different functions performed by the IDS. The NSL KDD dataset has been considered for training and testing. The seven prominent classifiers LR (Logistic Regression), NB (Naïve Bayes), DT (Decision Tree), AB (AdaBoost), RF (Random Forest), kNN (k Nearest Neighbor), and SVM (Support Vector Machine) have been studied along with their pros and cons and the feature selection have been imposed to enhance the reading of performance evaluation parameters (Accuracy, Precision, Recall, and F1Score). The paper elaborates a detailed flowchart and algorithm depicting the procedure to perform feature selection using XGB (Extreme Gradient Booster) for four categories of attacks: DoS (Denial of Service), Probe, R2L (Remote to Local Attack), and U2R (User to Root Attack). The selected features have been ranked as per their occurrence. The implementation have been conducted at five different ratios of 60-40%, 70-30%, 90-10%, 50-50%, and 80-20%. Different classifiers scored best for different performance evaluation parameters at different ratios. NB scored with the best Accuracy and Recall values. DT and RF consistently performed with high accuracy. NB, SVM, and kNN achieved good F1Score.

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