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RISS 인기검색어
- 검색키워드
- 저자 : Vinayakumar R. (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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DeepMalNet: Evaluating Shallow and Deep networks for Static PE Malware Detection
Vinayakumar R.,Soman K.P. 한국통신학회 2018 ICT Express Vol.4 No.4
This paper primarily evaluates the efficacy of shallow and deep networks to statically detect malicious windows portable executable (PE) files. This uses recently released, labeled and benchmark data set, EMBER malware benchmark data set. As deep networks are parameterized, the parameters are chosen based on comparing the performance of various network parameters and network topologies over various trials of experiments. The experiments of such chosen efficient configurations of deep models are run up to 1000 epochs with varying learning rates between 0.01 and 0.5. The observed results of deep networks are high compared to the shallow networks.
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Siamese neural network architecture for homoglyph attacks detection
Vinayakumar R.,Soman K.P. 한국통신학회 2020 ICT Express Vol.6 No.1
Primarily an adversary uses homoglyph or spoofing attack approach to obfuscate domain name, file name or process names. This approach facilitates to create domain name, file name or process names which look visually homogeneous to legitimate domain name, file name or process names. This paper introduces Siamese neural network architecture which uses the application of recurrent structures with Keras character level embedding to learn the optimal features by considering an input in the form of raw strings. For comparative study, various recurrent structures are used. The performances obtained by recurrent structures are almost closer. However, the proposed method performed well in comparison to the existing methods such as Edit Distance, Visual Edit Distance and Siamese convolutional neural networks.
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Diabetes detection using deep learning algorithms
Swapna G.,Vinayakumar R.,Soman K.P. 한국통신학회 2018 ICT Express Vol.4 No.4
Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. RR-interval signals known as heart rate variability (HRV) signals (derived from electrocardiogram (ECG) signals) can be effectively used for the non-invasive detection of diabetes. This research paper presents a methodology for classification of diabetic and normal HRV signals using deep learning architectures. We employ long short-term memory (LSTM), convolutional neural network (CNN) and its combinations for extracting complex temporal dynamic features of the input HRV data. These features are passed into support vector machine (SVM) for classification. We have obtained the performance improvement of 0.03% and 0.06% in CNN and CNN-LSTM architecture respectively compared to our earlier work without using SVM. The classification system proposed can help the clinicians to diagnose diabetes using ECG signals with a very high accuracy of 95.7%.
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