Onem, Ismail Melih
An Intrusion Detection System classifies activities at an unwanted intention and can log or prevent activities that are marked as intrusions. Intrusions occur when malicious activity and unwanted behaviour gain access to or affect the usability of a computer resource. During the last years, anomaly discovery has attracted the attention of many researchers to overcome the disadvantage of signature-based IDSs in discovering novel attacks, and KDDCUP'99 is the mostly widely used data set for the evaluation of these systems. Difficulty is discovering unwanted behaviour in network traffic after they have been subject to machine learning methods and processes. The goal of this research is using the SVM machine learning model with different kernels and different kernel parameters for classification unwanted behaviour on the network with scalable performance. The SVM model enables flexible, flow-based method for detecting unwanted behaviour and illustrates its use in the context of an incident, and can forward the design and deployment of improved techniques for security scanning. Although scalability and performance are major considerations and results also are targeted at minimizing false positives and negatives. The classification matured in this paper is used for improving SVM computational efficiency to detect intrusions in each category, and enhanced model is presented experimental results based on an implementation of the model tested against real intrusions.


Anomaly detection from personal usage patterns in web applications
Vural, Gürkan; Yöndem (Turhan), Meltem; Department of Computer Engineering (2006)
The anomaly detection task is to recognize the presence of an unusual (and potentially hazardous) state within the behaviors or activities of a computer user, system, or network with respect to some model of normal behavior which may be either hard-coded or learned from observation. An anomaly detection agent faces many learning problems including learning from streams of temporal data, learning from instances of a single class, and adaptation to a dynamically changing concept. The domain is complicated by ...
Information-Theoretic Feature Selection for Human Micro-Doppler Signature Classification
Tekeli, Burkan; Gurbuz, Sevgi Zubeyde; Yüksel Turgut, Ayşe Melda (2016-05-01)
Micro-Doppler signatures can be used not only to recognize different targets, such as vehicles, helicopters, animals, and people, but also to classify varying activities, e.g., walking, running, creeping, and crawling. For this purpose, a plethora of features have been proposed in the literature; however, dozens of features are not required to achieve high classification performance. The topic of feature selection has been under addressed in micro-Doppler studies. Moreover, the optimal feature set is not st...
Visual detection and tracking of moving objects
Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2007-06-13)
In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted fo...
Object tracking for surveillance applications using thermal and visible band video data fusion
Beyan, Çiğdem; Temizel, Alptekin; Department of Information Systems (2010)
Individual tracking of objects in the video such as people and the luggages they carry is important for surveillance applications as it would enable deduction of higher level information and timely detection of potential threats. However, this is a challenging problem and many studies in the literature track people and the belongings as a single object. In this thesis, we propose using thermal band video data in addition to the visible band video data for tracking people and their belongings separately for ...
Guldogan, M. B.; Gustafsson, F.; Orguner, Umut; Bjorklund, S.; Petersson, H.; Nezirovic, A. (2011-05-27)
Monitoring and tracking human activities around restricted areas is an important issue in security and surveillance applications. The movement of different parts of the human body generates unique micro-Doppler features which can be extracted effectively using joint time-frequency analysis. In this paper, we describe the simultaneous tracking of both location and micro-Doppler features of a human using particle filters (PF). The results obtained using the data from a 77 GHz radar prove the successful usage ...
Citation Formats
I. M. Onem, “UNWANTED BEHAVIOUR DETECTION AND CLASSIFICATION IN NETWORK TRAFFIC,” 2010, p. 122, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64046.