Deris Stiawan
Sriwijaya University
46Publications
1H-index
4Citations
Publications 40
Newest
#1Sharipuddin Sharipuddin (Sriwijaya University)H-Index: 1
#2Benni Purnama (Sriwijaya University)H-Index: 3
Last. Mohd. Yazid Idris (UTM: Universiti Teknologi Malaysia)H-Index: 8
view all 8 authors...
The difficulty of the Intrusion Detection System in heterogeneous networks is significantly affected by devices, protocols, and services, thus the network becomes complex and difficult to identify. Deep learning is one algorithm that can classify data with high accuracy. In this research, we proposed Deep Learning to Intrusion Detection System identification methods in heterogeneous networks to increase detection accuracy. In this paper, we provide an overview of the proposed algorithm, with an ...
Source
The rapid development of deep learning improves the detection and classification of attacks on intrusion detection systems. However, the unbalanced data issue increases the complexity of the architecture model. This study proposes a novel deep learning model to overcome the problem of classifying multi-class attacks. The deep learning model consists of two stages. The pre-tuning stage uses automatic feature extraction with a deep autoencoder. The second stage is fine-tuning using deep neural net...
Source
#1Yesi Novaria Kunang (Sriwijaya University)H-Index: 4
#2Siti Nurmaini (Sriwijaya University)H-Index: 10
Last. Bhakti Yudho SupraptoH-Index: 4
view all 4 authors...
Abstract A network intrusion detection system (NIDS) is a solution that mitigates the threat of attacks on a network. The success of a NIDS depends on the success of its algorithm and the performance of its method in recognizing attacks. We propose a deep learning intrusion detection system (IDS) using a pretraining approach with deep autoencoder (PTDAE) combined with a deep neural network (DNN). Models were developed using hyperparameter optimization procedures. This research provides an altern...
Source
#1Eko Arip Winanto (UTM: Universiti Teknologi Malaysia)H-Index: 1
#2Mohd. Yazid Idris (UTM: Universiti Teknologi Malaysia)H-Index: 8
Last. Mohammad Sulkhan Nurfatih (UTM: Universiti Teknologi Malaysia)
view all 4 authors...
Signature-based Collaborative Intrusion Detection System (CIDS) is highly depends on the reliability of nodes to provide IDS attack signatures. Each node in the network is responsible to provide new attack signature to be shared with other node. There are two problems exist in CIDS highlighted in this paper, first is to provide data consistency and second is to maintain trust among the nodes while sharing the attack signatures. Recently, researcher find that blockchain has a great potential to s...
Source
Malware may disrupt the Internet of Thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This...
Source
#2Deris StiawanH-Index: 3
Last. Ahmad HeryantoH-Index: 2
view all 3 authors...
Banking Trojans are one of the most well-known types of malware because they are designed to measure money directly from the bank accounts of mobile or PC users. Tinba is a small malware which is very difficult to detect because of its small size, smaller than other Trojan that is commonly known. The purpose of this paper is to monitor tinba traffic. Before the blocking stage, the initial stage is by checking the traffic with the Snort Engine, the traffic pattern is unique to the traffic. The da...
#2Deris StiawanH-Index: 3
#2Deris StiawanH-Index: 1
This research is based on the process of visualizing malware capture data into a grayscale image, from the grayscale image there is a repeating pattern, the pattern will be taken using the Gray Level Coocurrence Matrix with angles 0, 45, 90, 135 with attributes of dissimilarity, correlation, homogeneity, contrast, ASM, energy. The features obtained will be carried out by presenting labels with the training data process, where the system will study the training data in detail, after the system le...
#2Deris StiawanH-Index: 3
Last. Ahmad HeryantoH-Index: 2
view all 3 authors...
Malware can also be interpreted as software installed on a computer system without the knowledge of the user or the owner of the system. Malware is also commonly found on Android systems, one type of adware.The method that can be used to analyze malware is by analyzing application program code that is suspected of containing malware, one of which is reverse engineering. By using the reverse engineering method that uses static analysis, the title of the final project is taken, namely "adware malw...
#2Deris StiawanH-Index: 3
Last. Ahmad HeryantoH-Index: 2
view all 3 authors...
The development of technology triggers the development of malicious files called malware. Malware is software that is explicitly designed with the aim of finding weaknesses or even damaging software or operating systems. In this study, the dowgin and benign malware classification was carried out using the Random Forest algorithm method by comparing weka data and spyder programs. The dataset used in this study is the CICAndMal2017 csv (Comma Separated Values) category with the dowgin type in this...
#2Deris StiawanH-Index: 3
Last. Deris StiawanH-Index: 1
view all 2 authors...
Visualization has a function so that we can see the malware in grayscale form which consists of data in the form of a collection of hexadecimal numbers which are converted into decimal. Malware classification is a way to identify and classify malware based on their respective groups. Random forest is one of the many classification methods used for this case. Local Binary pattern used for feature extraction process from existing data. The system is trained and tested using 1000 data from 10 diffe...