Computing and Information Systems - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 17
-
ItemOn the effectiveness of isolation-based anomaly detection in cloud data centers(WILEY, 2017-09-25)Summary The high volume of monitoring information generated by large‐scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource‐intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large‐scale environment such as clouds. Isolation‐based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation‐based anomaly detection method, to detect anomalies in large‐scale cloud data centers. We propose a method to code time‐series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time‐series and to be applied online and in real‐time.
-
ItemDiscovering outlying aspects in large datasets(SPRINGER, 2016-11)
-
ItemTraining Robust Models with Random Projection(IEEE, 2016)Regularization plays an important role in machine learning systems. We propose a novel methodology for model regularization using random projection. We demonstrate the technique on neural networks, since such models usually comprise a very large number of parameters, calling for strong regularizers. It has been shown recently that neural networks are sensitive to two kinds of samples: (i) adversarial samples, which are generated by imperceptible perturbations of previously correctly-classified samples - yet the network will misclassify them; and (ii) fooling samples, which are completely unrecognizable, yet the network will classify them with extremely high confidence. In this paper, we show how robust neural networks can be trained using random projection. We show that while random projection acts as a strong regularizer, boosting model accuracy similar to other regularizers, such as weight decay and dropout, it is far more robust to adversarial noise and fooling samples. We further show that random projection also helps to improve the robustness of traditional classifiers, such as Random Forrest and Gradient Boosting Machines.
-
ItemAnalysis and Enhancement of On-demand Routing in Wireless Sensor Networks(ASSOC COMPUTING MACHINERY, 2008)
-
ItemCombining real and virtual graphs to enhance data clustering(IEEE, 2010-11-18)
-
ItemTensor space learning for analyzing activity patterns from video sequences(IEEE, 2007-12-01)
-
ItemProtecting SIP Server from CPU-Based DoS Attacks using History-Based IP Filtering(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2009-10)
-
ItemSurvey of network-based defense mechanisms countering the DoS and DDoS problems(ASSOC COMPUTING MACHINERY, 2007-04)This article presents a survey of denial of service attacks and the methods that have been proposed for defense against these attacks. In this survey, we analyze the design decisions in the Internet that have created the potential for denial of service attacks. We review the state-of-art mechanisms for defending against denial of service attacks, compare the strengths and weaknesses of each proposal, and discuss potential countermeasures against each defense mechanism. We conclude by highlighting opportunities for an integrated solution to solve the problem of distributed denial of service attacks.
-
ItemInformation sharing for distributed intrusion detection systems(ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 2007-08)
-
Item