Klasifikasi Paket Jaringan Berbasis Analisis Statistik dan Neural Network

Distributed Denial-of-Service (DDoS) is one of network attack technique which increased every year, especially in both of intensity and volume. DDoS attacks are still one of the world's major Internet threats and become a major problem of cyber-world security. Research in this paper aims to establish a new approach on network packets classification, which can be a basis for framework development on Distributed Denial-of-Service (DDoS) attack detection systems. The proposed approach to solving the problem on network packet classification is by combining statistical data quantification methods with neural network methods. Based on the test, it is found that the average percentage of neural network classification accuracy against network data packet is 92.99%

Penguatan Ekonomi Lokal Pada Pelaku UMKM Berbasis Digital Di Desa Winduaji Kabupaten Brebes

In the industrial era 4.0, technological knowledge, especially information technology is very important. UMKM are micro-enterprises that should have used the information access for the economic welfare of a region, but vice versa. Current problems with UMKM include lack of capital and knowledge of information technology. Winduaji village is one of the villages with UMKM actors with minimal information technology knowledge. The method of implementation is the method of discussion with the format of Training regarding identifying problems to the use of technology media. This training activity was attended by 56 participants consisting of village officials, UMKM actors, and tourism conscious reservoirs. As a result, all participants showed great interest in using social media marketing continuously

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure

The Application of Modified K-Nearest Neighbor Algorithm for Classification of Groundwater Quality Based on Image Processing and pH, TDS, and Temperature Sensors

The limited availability of water in remote areas makes rural communities pay less attention to the water quality they use. Water quality analysis is needed to determine the level of groundwater quality used using the Modified K-Nearest Neighbor Algorithm to minimize exposure to a disease. The data used in this study was images combined with sensor data obtained from pH (Potential of Hydrogen), TDS (Total Dissolved Solids) sensors and Temperature Sensors. The test used the Weight voting value as the highest class majority determination and was evaluated using the K-Fold Cross Validation and Multi Class Confusion Matrix algorithms, obtaining the highest accuracy value of 78% at K-Fold = 2, K-Fold = 9, and K- Fold = 10. Meanwhile, the results of testing the effect of the K value obtained the highest accuracy value at K = 5 of 67.90% with a precision value of 0.32, 0.37 recall, and 0.33 F1-Score. From the results of the tests carried out, it can be concluded that most of the water conditions are suitable for use

Impact of Feature Selection Methods on Machine Learning-based for Detecting DDoS Attacks : Literature Review

Cybersecurity attacks are becoming increasingly sophisticated and increasing with the development of technology so that they present threats to both the private and public sectors, especially Denial of Service (DoS) attacks and their variants which are often known as Distributed Denial of Service (DDoS). One way to minimize this attack is by using traditional mitigation solutions such as human-assisted network traffic analysis techniques but experiencing some limitations and performance problems. To overcome these limitations, Machine Learning (ML) has become one of the main techniques to enrich, complement and enhance the traditional security experience. The way ML works are based on the process of data collection, training and output. ML is influenced by several factors, one of which is feature engineering. In this study, we focus on the literature review of several recent studies which show that the feature selection process greatly impacts the level of accuracy of this ML. Datasets such as KDD, UNSW-NB15 and others also affect the level of accuracy of ML. Based on this literature review, this study can observe several feature engineering strategies with relevant impacts that can be chosen to improve ML solutions on DDoS attacks

Block-hash of blockchain framework against man-in-the-middle attacks

Payload authentication is vulnerable to Man-in-the-middle (MITM) attack. Blockchain technology offers methods such as peer to peer, block hash, and proof-of-work to secure the payload of authentication process. The implementation uses block hash and proof-of-work methods on blockchain technology and testing is using White-box-testing and security tests distributed to system security practitioners who are competent in MITM attacks. The analyisis results before implementing Blockchain technology show that the authentication payload is still in plain text, so the data confidentiality has not minimize passive voice. After implementing Blockchain technology to the system, white-box testing using the Wireshark gives the result that the authentication payload sent has been well encrypted and safe enough. The percentage of security test results gets 95% which shows that securing the system from MITM attacks is relatively high. Although it has succeeded in securing the system from MITM attacks, it still has a vulnerability from other cyber attacks, so implementation of the Blockchain needs security improvisation

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%

Pengembangan Perangkat Lunak Untuk Deteksi DDoS Berbasis Neural Network

System security issues are a vital factor that needs to be considered in the operation of systems and networks, which will later be used for disaster mitigation and preventing attacks on the network. Distributed Denial of Services (DDoS) is a form of attack carried out by individuals or groups to damage data through servers or malware in the form of flooding packets, therefore it can paralyze the network system used. Network security is a factor that must be maintained and considered in an information system. DDoS can take the form of Ping of Death, flood, Remote control attack, User Data Protocol (UDP) flood, and Smurf Attack. This study aims to develop software to detect DDoS attacks based on network traffic logs. The software has been tested and run according to the neural network algorithm. This software was developed with an interface that makes it easier for users to detect the source IP whether the IP is carrying out a DDoS attack or normal.Masalah keamanan sistem merupakan faktor vital yang perlu dipertimbangkan dalam pengoperasian system dan jaringan, yang nantinya untuk mitigasi bencana dan mencegah serangan pada jaringan. Distributed Denial of Services (DDoS) adalah sebuah bentuk serangan yang dilakukan oleh individu atau kelompok untuk merusak data melalui server atau malware dalam bentuk membanjiri paket sehingga dapat melumpuhkan sistem jaringan yang digunakan. Keamanan jaringan merupakan faktor yang harus dijaga dan dipertimbangkan dalam sebuah sistem informasi. DDoS bisa berbentuk Ping of Death, flood, Remote serangan, banjir User Data Protocol (UDP), dan Serangan Smurf. Penelitian ini bertujuan untuk mengembangkan perangkat lunak untuk mendeteksi adanya serangan DDoS berdasarkan log trafik jaringan. Perangkat lunak telah diuji dan berjalan sesuai algoritma neural network. Perangkat lunak ini dikembangkan dengan tampilan antarmuka yang memudahkan pengguna dalam mendeteksi IP sumber apakah IP tersebut melakukan serangan DDoS atau normal

Analysis of e-learning readiness level of public and private universities in Central Java, Indonesia

The development of information technology has reached into various fields, such as education. The emergence of e-learning is one manifestation of information and communication technology (ICT) in education. Until recently, only a few universities (6%) have implemented e-learning in Indonesia. Those that have implemented e-learning are still not optimally utilized. Some experts have also warned all organizations that will adopt e-learning to be concerned with thorough preparation to avoid overruns in costs. There is a method that consists of factors to measure the level of readiness of tertiary institutions towards the implementation of e-learning. The level of readiness is obtained through the distribution of questionnaires using 5 Likert scales. This research proposed a framework that produces four factors from the university, which covers the lecturer’s characteristics, e-learning facilities, learning environment, learning management, and four factors from the student’s side, namely, self-learning, motivation, learner’s control, student’s characteristic. The measurement results show the level of readiness for e-learning implementation in tertiary institutions in Central Java Province reaches level 3 or ready but needs a few improvements. Improvements that must be made includes (1) Designing exciting learning content through interactive multimedia; (2) Increasing the frequency of e-workshops or e-training related to technological developments, especially to e-learning; (3) encouraging students to be more active in discussions and giving opinions; (4) Developing plans related to infrastructure such as servers related to their capacities; (5) strengthening the role of IT units in serving e-learning users