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IoT botnet attack detection using deep autoencoder and artificial neural networks
( Deris Stiawan ),( Susanto ),( Abdi Bimantara ),( Mohd Yazid Idris ),( Rahmat Budiarto ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.5
As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3-layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.
Penetration Testing and Network Auditing: Linux
( Deris Stiawan ),( Mohd Yazid Idris ),( Abdul Hanan Abdullah ) 한국정보처리학회 2015 Journal of information processing systems Vol.11 No.1
Along with the evolution of Internet and its new emerging services, the quantity and impact of attacks have been continuously increasing. Currently, the technical capability to attack has tended to decrease. On the contrary, performances of hacking tools are evolving, growing, simple, comprehensive, and accessible to the public. In this work, network penetration testing and auditing of the Redhat operating system (OS) are highlighted as one of the most popular OS for Internet applications. Some types of attacks are from a different side and new attack method have been attempted, such as: scanning for reconnaissance, guessing the password, gaining privileged access, and flooding the victim machine to decrease availability. Some analyses in network auditing and forensic from victim server are also presented in this paper. Our proposed system aims confirmed as hackable or not and we expect for it to be used as a reference for practitioners to protecttheir systems from cyber-attacks.
Penetration Testing and Network Auditing: Linux
Stiawan, Deris,Idris, Mohd. Yazid,Abdullah, Abdul Hanan Korea Information Processing Society 2015 Journal of information processing systems Vol.11 No.1
Along with the evolution of Internet and its new emerging services, the quantity and impact of attacks have been continuously increasing. Currently, the technical capability to attack has tended to decrease. On the contrary, performances of hacking tools are evolving, growing, simple, comprehensive, and accessible to the public. In this work, network penetration testing and auditing of the Redhat operating system (OS) are highlighted as one of the most popular OS for Internet applications. Some types of attacks are from a different side and new attack method have been attempted, such as: scanning for reconnaissance, guessing the password, gaining privileged access, and flooding the victim machine to decrease availability. Some analyses in network auditing and forensic from victim server are also presented in this paper. Our proposed system aims confirmed as hackable or not and we expect for it to be used as a reference for practitioners to protect their systems from cyber-attacks.
Particle Swarm Optimization for Gene Selection Using Microarray Data
Mohd Saberi Mohamad,Sigeru Omatu,Safaai Deris,Michifumi Yoshioka 한국멀티미디어학회 2009 한국멀티미디어학회 국제학술대회 Vol.2009 No.-
In order to find and select possible informative genes for cancer classification, recently, many researchers are analyzing micro array data using various computational intelligence methods. However, due to a small number of samples compared to the huge number of genes, irrelevant genes, and noisy genes, most of these methods face difficulties to select the informative genes. In this paper, we propose an improved binary particle swarm optimization to select a small subset of informative genes that is relevant for the cancer classification. Instead of the existing rule of position update in binary particle swarm optimization (BPSO), we modify the rule so that it selects efficiently the small subset from the microarray data. By performing experiments on two different public cancer data sets, we have found that the performance of the proposed method is superior to other related previous works, including BPSO in terms of classification accuracy and the number of selected genes.