Global and local clustering soft assignment for intrusion detection system: a comparative study

Intrusion Detection System (IDS) plays an important role in computer network defence mechanism against malicious objects. The ability of IDS to detect new sophisticated attacks compared to traditional method such as firewall is important to secure the network. Machine Learning algorithm such as unsupervised learning and supervised learning is capable to solve the problem of classification in IDS. To achieve that, KDD Cup 99 dataset is used in experiments. This dataset contains 5 million instances with 5 different categories which are Normal, DOS, U2R, R2L and Probe. With such a large dataset, the learning process consumes a lot of processing times and resources. Clustering is unsupervised learning method that can be used for organizing data by grouping similar features into same group. In literature, many researchers used global clustering approach whereby all input will be combined and clustered to construct a codebook. However, there is an alternative technique namely local clustering approach whereby the input will be split into 5 different categories and clustered independently to construct 5 different codebooks. The main objective of this research is to compare the classification performance between the global and local clustering approaches. For this purpose, the soft assignment approach is used for indexing on KDD input and SVM for classification. In the soft assignment approach, the smallest distance values are used for attack description and RBF kernel for SVM to classify attack. The results show that the global clustering approach outperforms the local clustering approach for binary classification. It gives 83.0% of the KDD Cup 99 dataset. However, the local clustering approach outperforms the global clustering approach on multi-class classification problem. It gives 60.6% of the KDD Cup 99 dataset

Perbandingan Kinerja Wifi Mesh Dan Access Point Sebagai Home Solution Menggunakan Metode Quality Of Service (QoS)

Perkembangan teknologi yang semakin masif hingga saat membuat internet menjadi kebutuhan sehari-hari dan tidak dapat ditinggalkan. Sehingga tidak sedikit orang yang menyewa ISP (Internet Service Provider) supaya perangkat mereka selalu terhubung dengan internet. Dalam penggunaannya sehari-hari, pernah kita jumpai bahwa seseorang yang terhubung dengan access point mengalami yang namanya sinyal jelek atau hilang  yang mengakibatkan koneksi dari perangkat yang digunakan orang tersebut ke internet menjadi lebih lambat atau bahkan tidak terhubung sama sekali. Dari pertanyaan itulah penulis ingin meneliti lebih dalam terkait perbandingan perangkat WiFi Mesh dan access point sehingga para calon pengguna perangkat tersebut diluar sana bisa membedakan dua hal tersebut berdasarkan perbandingan yang dilakukan oleh penulis. Penelitian ini menggunakan metode pengumpulan data berupa studi literatur dan pengujian pada perangkat terkait untuk menganalisis perbandingan kedua perangkat tersebut dari segi Quality of Service (QoS). Hasil pengujian menunjukkan bahwa penggunaan WiFi Mesh sebagai home solution sangat baik dalam mengatasi permasalahan disebutkan diatas. Analisis parameter throughput pada semua pengujian yang dilakukan berdasarkan standard TIPHON, terjadi kenaikan persentase throughput sebesar 5% - 40% pada download dan upload pada saat menggunakan perangkat WiFi Mesh dibandingkan Access Point. Kemudian didapatkan persentase  penurunan throughput dari router mesh ke router mesh lainnya hanya sebesar 1% - 9%. Sedangkan access point bawaan ISP yang mana pada percobaan jarak terdekat mengalami persentase penurunan mulai 23% dan pada jarak terjauh percobaan yang dilakukan terjadi penurunan hingga 95% throughput awal. Kondisi LOS juga mempengaruhi throughput penurunan sebesar 1%-36%. Begitu juga kondisi Non LOS menyebabkan penurunan throughput sebesar 2%-62%. Kata Kunci— wifi mesh, access point, metode Quality of Service (QoS), jaringan wireless, solusi rumahan, solusi is

Multiple Descriptors for Visual Odometry Trajectory Estimation

Visual Simultaneous Localization and Mapping (VSLAM) systems are widely used in mobile robots for autonomous navigation.  One important part in VSLAM is trajectory estimation. Trajectory estimation is a part of the localisation task in VSLAM where a robot needs to estimate the camera pose in order to precisely align the real visited image locations.  The poses are estimated using Visual Odometry Trajectory Estimation (VOTE) by extracting distinctive and trackable keypoints from sequence image locations having been visited by a robot. In the visual trajectory estimation, one of the most popular solutions is arguably PnP-RANSCA function. PnP-RANSAC is a common approach used for estimating the VOTE which uses a feature descriptor such as SURF to extract key-points and match them in pairs based on their descriptors. However, due to the sensor noise and the high fluctuating scenes constitute an inevitable shortcoming that reduces the single visual descriptor performance in extracting the distinctive and trackable keypoints. Thus, this paper proposes a method that uses a random sampling scheme to combine the result of multiple key-points descriptors. The scheme extracts the best keypoints from SIFT, SURF and ORB key-point detectors based on their key-point response value. These keypoints are combined and refined based on Euclidean distances. This combination of keypoints with their corresponding visual descriptors are used in VOTE which reduces the trajectory estimation errors. The proposed algorithm is evaluated on the widely used benchmark dataset KITTI where the three longest sequences are selected, 00 with 4541 images, 02 with 2761 images and 05 with 1101 images. In trajectory estimation experiment, the proposed algorithm can reduce the trajectory error of 44%, 8% and 13% on KITTI dataset for the sequence 00, 02 and 05 respectively based on translational and rotational errors. Also, the proposed algorithm succeeded in reducing the number of keypoints used in VOTE as combined with the state-of-the-art RTAB-Map

Pembangunan Kotak Pembatas 3D dari Beberapa Citra

Kotak pembatas tiga dimensi (3D) banyak digunakan dalam pembangunan aplikasi computer vision 3D, grafika komputer, simulasi medis, permainan komputer dan animasi. Ketepatan pembangunan kotak pembatas 3D sangat berpengaruh terhadap kompleksitas komputasi dan keakuratan algoritma aplikasi yang dikembangkan. Makalah ini memaparkan tentang pembangunan kotak pembatas 3D sejajar sumbu dari beberapa citra. Lima buah citra objek ditangkap dari bagian atas dan sekeliling objek menggunakan lima buah kamera yang telah dikalibrasi sebelumnya. Kelima citra kemudian diproses untuk membangun kotak pembatas 3D berdasarkan parameter kamera intrinsik dan ekstrinsik. Hasil eksperimen menunjukkan bahwa metode pembangunan kotak pembatas 3D yang dikembangkan menghasilkan kotak pembatas yang cukup akurat untuk sepuluh objek produk makanan

Practitioners' perception toward AIS course content offered by Malaysian universities

The purpose this study is to determine the degree of importance of each topic included in the Accounting Information System (AIS) course from the practitioners' point of views. This study was motivated by the increasing progress in information technology which proposed to re-examine the AIS course in order to meet the expectations and requirements of the profession. Three hundred and forty (340) questionnaires were sent to there different groups of companies namely public accounting firms, industry and commerce, and banking and finance. The response rate received is 22.35%. Respondents' opionions on the importance od AIS topics were measured using of five-point Likert Scale

Real world coordinate from image coordinate using single calibrated camera based on analytic geometry

The determination of real world coordinate from image coordinate has many applications in computer vision. This paper proposes the algorithm for determination of real world coordinate of a point on a plane from its image coordinate using single calibrated camera based on simple analytic geometry. Experiment has been done using the image of chessboard pattern taken from five different views. The experiment result shows that exact real world coordinate and its approximation lie on the same plane and there are no significant difference between exact real world coordinate and its approximation

Ensembles of Novel Visual Keywords Descriptors for Image Categorization

Object recognition systems need effective image descriptors to obtain good performance levels. Currently, the most widely used image descriptor is the SIFT descriptor that computes histograms of orientation gradients around points in an image. A possible problem of this approach is that the number of features becomes very large when a dense grid is used where the histograms are computed and combined for many different points. The current dominating solution to this problem is to use a clustering method to create a visual codebook that is exploited by an appearance based descriptor to create a histogram of visual keywords present in an image. In this paper we introduce several novel bag of visual keywords methods and compare them with the currently dominating hard bag-of-features (HBOF) approach that uses a hard assignment scheme to compute cluster frequencies. Furthermore, we combine all descriptors with a spatial pyramid and two ensemble classifiers. Experimental results on 10 and 101 classes of the Caltech-101 object database show that our novel methods significantly outperform the traditional HBOF approach and that our ensemble methods obtain state-of-the-art performance levels

Retinal Blood Vessel Segmentation Using Ensemble of Single Oriented Mask Filters

This paper describes a method on segmentation of blood vessel in retinal images using supervised approach. Blood vessel segmentation in retinal images can be used for analyses in diabetic retinopathy automated screening. It is a very exhausting job and took a very long time to segment retinal blood vessels manually. Moreover these tasks also requires training and skills. The strategy involves the applications of Support Vector Machine to classify each pixel whether it belongs to a vessel or not. Single mask filters which consist of intensity values of normalized green channel have been generated according to the direction of angles. These single oriented mask filters contain the vectors of the neighbourhood of each pixel. Five images randomly selected from DRIVE database are used to train the classifier. Every single oriented mask filters are ranked according to the average accuracy of training images and their weights are assigned based on this rank.  Ensemble approaches that are Addition With Weight and Product With Weight have been used to combine all these single mask filters. In order to test the proposed approach, two standard databases, DRIVE and STARE have been used. The results of the proposed method clearly show improvement compared to other single oriented mask filters

Volume Measurement Algorithm for Food Product with Irregular Shape using Computer Vision based on Monte Carlo Method

Volume is one of important issues in the production and processing of food product. Traditionally, volume measurement can be performed using water displacement method based on Archimedes' principle. Water displacement method is inaccurate and considered as destructive method. Computer vision offers an accurate and nondestructive method in measuring volume of food product. This paper proposes algorithm for volume measurement of irregular shape food product using computer vision based on Monte Carlo method. Five images of object were acquired from five different views and then processed to obtain the silhouettes of object. From the silhouettes of object, Monte Carlo method was performed to approximate the volume of object. The simulation result shows that the algorithm produced high accuracy and precision for volume measurement