comparative study of several anomaly detection schemes for identifying novel network intrusions. 2)In addition, intrusions very often represent sequence of events and therefore are more suitable to be addressed by some temporal data mining algorithms. Finally, misuse detection algorithms require all data to be labeled, but labeling network connections as normal or intrusive re-quires enormous amount of time for many human experts. All these issues cause building misuse detection models very complex 3)Feature
Data Processing Following the creation of the sample, I applied the KNN, SVM, CDA, and k-means pattern recognition algorithm treatment to every member of the sample of the CDAnalysis program and recorded whether the pattern recognition algorithm correctly identified the class of the exposure. If the pattern recognition algorithm determined the class of the exposure to be the same as the same class from which the Cyranose 320 sampled the exposure, then the classification for the exposure was correct
them from left and right singular value respectively. On the opposite side, the author in this paper \cite{rezaei2014adaptive} proposed a two modified scheme where he had provided lower computational complexity algorithm with retaining MTM-SVD functionality. the first scheme was SWASVD algorithm. The second scheme was 3D higher tensor decomposition where the author benefits from tensor higher order decomposition to compute the estimation power over all OFDM blocks at once. Therefore, the measurements
India bhuvanakceece@gmail.com gunaece2000@gmail.com Abstract-Video editing softwares are easy to use but videos are exposed to tampering. Mostly, video cameras are built in MPEG-4 codec. Therefore, the detection of double compression in MPEG-4 videos as a first step in video forensics research. Markov based features are used to detect double compression artifacts. Keywords—Digital forensics, double compression, Markov statistics,MPEG-4. 1.INTRODUCTION
cite{amamra2012smartphone} classified malicious mobile app detection method based on 3 rules: reference behaviour, analysis approach and malware behaviour representation and divided smartphone malware detection techniques into two main classes: signature-based and anomaly-based. Literature cite{idika2007survey} examined 45 malware detection techniques which are instructive for malicious mobile app detection. In this section, we mainly focus on behavior-based malware detection methods and only review the most related
Introduction Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet
Privacy-Preserving And Truthful Detection Of Packet Dropping Attacks In Wireless Ad-Hoc Networks GRADUATE PROJECT PROPOSAL Submitted to the Faculty of the Department of Computing Sciences Texas A&M University – Corpus Christi Corpus Christi, Texas In Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Science by Sai Krishna Vudepu Fall 2015 Committee Members Dr. Mario A. Garcia ____________________________ Committee Chairperson
intensification efficiency. Therefore, the system performance is improved regarding classification rate and computing time. In this chapter, we define the most important step in the pre-processing and we followed by analyzing the reason of chosen algorithms and its impressions in a Speaker Identification System (SIS). 2.1 Pre-processing Structure gCommonly the pre-processing includes the sampling step, windowing, and a denoising step. At the end of the preprocessing, the compressed and filtered speech
facial expression, body movement, crying sound, vital signs, and cerebral hemodynamic. Each of these indicators is divided based on the underlying algorithm as illustrated in Figure 1. \item We categorize the current works by dividing them, based on the classification tasks, into pain detection and pain intensity estimation. We define pain detection as the task of detecting the presence or absence of pain and pain intensity estimation as the task of
But finding gradual transition is major challenge in the presence of camera and object motion. In this paper, different shot boundary detection technique has studied. The main focused on to differentiated motion from various video effects noise, illumination changes, gradual transition, and abrupt transition. Specially, the paper focuses on dissolve detection in the presence of camera and object motion Keywords: Shot boundary, Gradual transition, Abrupt Transition, Video Retrieval I.INTRODUCTION: