No abstract

Malware is one of the major concerns in computer security. The availability of easy to use malware toolkits and internet popularity has led to the increase in number of malware attacks. Currently signature based malware detection techniques are widely used. However, malware authors use packing techniques to create new variants of existing malwares which defeat signature based malware detection. So, it is very important to identify packed malware and unpack it before analysis. Dynamic unpacking runs the packed executable and provides an unpacked version based on the system. This technique requires dedicated hardware and is computationally expensive. As each individual packer uses its own unpacking algorithm it is important to have a prior knowledge about the packer used, in order to assist in reverse engineering. In this paper, we propose an efficient framework for packer identification problem using Byte plot and Markov plot. First packed malware is converted to Byte plot and Markov plot. Later Gabor and wavelet based features are extracted from Byte plot and Markov plot. We used SVMs (Support Vector Machine) in our analysis. We performed our experiments on nine different packers and we obtained about 95 % accuracy for nine of the packers. Our results show features extracted from Markov plot outper-B Kesav Kancherla formed features extracted from Byte plot by about 3 %. We compare the performance of Markov plot with PEID (Signature based PE identification tool). Our results show Markov plot produced better accuracy when compared to PEID. We also performed multi class classification using Random Forest and achieved 81 % accuracy using Markov plot based features.

No abstract

No abstract

In this study we propose an early lung cancer detection methodology using nucleus based features. First the sputum samples from patients are labeled with Tetrakis Carboxy Phenyl Porphine (TCPP) and fluorescent images of these samples are taken. TCPP is a porphyrin that is able to assist in labeling lung cancer cells by increasing numbers of low density lipoproteins coating on the surface of cancer. We study the performance of well know machine learning techniques in the context of lung cancer detection on Biomoda dataset. We obtained an accuracy of 81% using 71 features related to shape, intensity and color in our previous work. By adding the nucleus segmented features we improved the accuracy to 87%. Nucleus segmentation is performed by using Seeded region growing segmentation method. Our results demonstrate the potential of nucleus segmented features for detecting lung cancer.