DDoS Attacks Classification using Numeric 
Attribute-based Gaussian Naive Bayes

Fadlil, Abdul; Riadi, Imam; Aji, Sukma

doi:10.14569/ijacsa.2017.080806

Cited by 11 publications

(9 citation statements)

References 6 publications

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“…Sehingga apabila terjadi ancaman dari tindak hacker dapat mencuri informasi data dan serangan tersebut dapat merusak jaringan komputer [1]. Jenis serangan yang sering digunakan oleh hacker adalah (Denial of Services) DoS yang bersifat mengirimkan sejumlah paket melalui internet protocol (IP) secara terus menerus yang dapat mengganggu organisasi dari jaringan komputer dengan tujuan melumpuhkan server [2] [3].…”

Section: Pendahuluanunclassified

Analysis of Digital Evidence on Denial of Service (DoS) Attack Log Based

Fanani

Riadi

2020

Bul. Il. Sar. TE.

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This research is carried out an analysis and investigation of digital log file data retrieval from DoS (Denial of Service) attacks, on internet networks that have been detected by IDS (Intrusion Detection System) and using Wireshark as Tools Analysis Network. The research phase begins with the design of an experimental scenario which is often carried out daily where users access the internet network. The next stage is an attack in the form of ping flood on the target computer connected to the internet network, the final stage of data retrieval which will be analyzed later. Testing research using UAT (User Acceptance Test), to prove that the analysis has been received by the user. The results of research conducted to obtain data in the form of an attacker's IP (Internet Protocol), target IP, protocol type, the port used and the time of the attack. In the UAT test results, the obtained value of 18% of students disagrees, 58% of students agree, and 24% of students strongly agree. This research has conducted an analysis of random data attacks using Wireshark applications received by users.

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Section: Pendahuluanunclassified

Analysis of Digital Evidence on Denial of Service (DoS) Attack Log Based

Fanani

Riadi

2020

Bul. Il. Sar. TE.

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“…Bayes method is used to calculate the probability of an event's occurrence based on the observed observation effect. The Naive Bayes method is a simple probabilistic-based prediction that rely on the application of the Bayes method with a sturdy independence assumption [11] [18]. The equation for the Naive Bayes method is:…”

Section: Naïve Bayesmentioning

confidence: 99%

“…Extraction results are stored with the featureuji file name as test data. The training and testing were carried out using the naïve Bayes Gaussian method [11] with a formula which can be seen in Figure 11. www.ijacsa.thesai.org…”

Section: Training and Testing Process Using Naïve Bayesmentioning

confidence: 99%

“…Machine learning techniques, by validating network data provided to classify with legitimate observations based on anomalies, can be used in the network forensic process [10]. DDoS attacks through computer networks, especially Local Area Networks (LANs) can be detected using multi-classification techniques, which is by combining data mining methods to get better accuracy [11]. Classification using the Neural Network method in analyzing DDoS attacks can provide 99.6% results based on Hidden Neural Network Variations [12].…”

Section: Introductionmentioning

confidence: 99%

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DDoS Classification Using Neural Network and Naïve Bayes Methods for Network Forensics

Yudhana¹,

Riadi²,

Ridho³

2018

ijacsa

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Distributed Denial of Service (DDoS) is a network security problem that continues to grow dynamically and has increased significantly to date. DDoS is a type of attack that is carried out by draining the available resources in the network by flooding the package with a significant intensity so that the system becomes overloaded and stops. This attack resulted in enormous losses for institutions and companies engaged in online services. Prolonged deductions and substantial recovery costs are additional losses for the company due to loss of integrity. The activities of damaging, disrupting, stealing data, and everything that is detrimental to the system owner on a computer network is an illegal act and can be imposed legally in court. Criminals can be punished based on the evidence found with the Forensics network mechanism. DDoS attack classification is based on network traffic activity using the neural network and naïve Bayes methods. Based on the experiments conducted, it was found that the results of accuracy in artificial neural networks were 95.23% and naïve Bayes were 99.9%. The experimental results show that the naïve Bayes method is better than the neural network. The results of the experiment and analysis can be used as evidence in the trial process.

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“…Probabilitas adalah kemungkinan terjadinya suatu peristiwa antara 0 s/d 1 (wahyudi & fadlil, 2013). Klasifikasi Gaussian Naive Bayes dapat digunakan untuk memproses atribut numerik pada layanan jaringan komputer (Fadlil, Riadi, & Aji, 2017). Pada tahap pelatihan dilakukan proses analisis terhadap sampel data yang dapat menjadi repsentasi dokumen.…”

Section: Naïve Bayes Classifierunclassified

Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Pada Angket Mahasiswa

Sunardi¹,

Fadlil²,

Suprianto

2018

SAINTEKBU

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Penelitian ini bertujuan untuk melakukan analisis sentimen berupa data angket mahasiswa untuk mengetahui kepuasan mahasiswa dalam proses pendidikan. Analisis sentimen adalah proses klasifikasi dokumen dan dalam penelitian ini dibagi menjadi tiga bagian, yaitu kelas positif, negatif dan netral. Klasifikasi sangat penting dalam pencarian dokumen bagi pengguna. Proses klasifikasi diawali dengan membagi koleksi dokumen menjadi data latih dan data uji. Data latih digunakan metode naïve bayes classifier sehingga diperoleh model klasifikasi untuk penentuan kelas pada data uji. Metode naïve bayes classifier adalah metode yang berdasarkan probabilitas dan teorema bayesian. Metode ini digunakan untuk mengklasifikasikan hasil data opini mahasiswa yang dituliskan pada form kepuasan angket mahasiswa terhadap dosen sehingga menghasilkan klasifikasi secara otomatis yang diinginkan. Hasil penelitian ini digunakan untuk menentukan klasifikasi dari data angket mahasiswa sehingga data mudah terbaca. Klasifikasi metode ini mempunyai tingkat precision 75%, recall 75% dan akurasi 80%. Kata Kunci: Analisis Sentimen, Naïve Bayes Classifier, Angket Mahasiswa

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DDoS Attacks Classification using Numeric Attribute-based Gaussian Naive Bayes

Cited by 11 publications

References 6 publications

Analysis of Digital Evidence on Denial of Service (DoS) Attack Log Based

Analysis of Digital Evidence on Denial of Service (DoS) Attack Log Based

DDoS Classification Using Neural Network and Naïve Bayes Methods for Network Forensics

Analisis Sentimen Menggunakan Metode Naïve Bayes Classifier Pada Angket Mahasiswa

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