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

Abstract

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.

Recognizing Human Emotion patterns by applying Fast Fourier Transform based on Brainwave Features

“…By looking on the table 2, the results obtained from the values of each frequency from 0-4Hz with a range of 0.25Hz and obtained 12 data from each brainwave that has been processed using FFT, then the results of the table are compared in the form of a line chart in Figure 9. The results of the comparison of brainwaves in table 3 made into the form of a line chart in Figure 12 seen a significant difference between each brainwave that has been processed from raw data into FFT where there is a difference in correlation between each emotion with the average value the highest meanings are in Emotion Focus (7.3783), Sad (4.3242), Shocked (3.4061), Normal (2,104). Table 4 shows the extraction results based on statistical features of the maximum peak signal.…”

“…Humans have the natural ability to use all their senses in receiving messages in a conscious state. Through these senses, humans can feel emotional states when they get a stimulus [1] [2]. Recognizing human emotions directly can be assessed from several criteria, such as facial expressions [3], sounds [4], or body movements [5] [6].…”

Enhancing Network Forensics with Particle Swarm and Deep Learning: The Particle Deep Framework

“…Commonly, network forensic applications have incorporated a number of techniques based on mathematics and machine learning, such as fuzzy logic, naïve bayes classifiers, support vector machines and neural networks [31][32][33][34]. However, contemporary research has proposed deep learning as an alternative as, long training times notwithstanding, deep models tend to outperform other solutions when tasked with processing large volumes of data [35,36,3,14,17].…”

Forensic Analysis of Docker Swarm Cluster using Grr Rapid Response Framework

“…It needs special tools and conditions to meet the requirements to investigate on such data, even there are more strictly procedures involved in investigating on mobile device [12]. Static or persistent data will require static forensics [13], while dynamic data (computer RAM, running processes, log file, registry status, network status of network device) require live forensic, because data is not persistent, and will change periodically or even unconditionally [14]. Live forensics that investigates on network computers is called network forensics [15].…”