• Title/Summary/Keyword: anomaly detection

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An Anomaly Detection Framework Based on ICA and Bayesian Classification for IaaS Platforms

  • Wang, GuiPing;Yang, JianXi;Li, Ren
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.8
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    • pp.3865-3883
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    • 2016
  • Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

UFKLDA: An unsupervised feature extraction algorithm for anomaly detection under cloud environment

  • Wang, GuiPing;Yang, JianXi;Li, Ren
    • ETRI Journal
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    • v.41 no.5
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    • pp.684-695
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    • 2019
  • In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

Anomaly behavior detection using Negative Selection algorithm based anomaly detector (Negative Selection 알고리즘 기반 이상탐지기를 이용한 이상행 위 탐지)

  • 김미선;서재현
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2004.05b
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    • pp.391-394
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    • 2004
  • Change of paradigm of network attack technique was begun by fast extension of the latest Internet and new attack form is appearing. But, Most intrusion detection systems detect informed attack type because is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, visibilitys to apply human immunity mechanism are appearing. In this paper, we create self-file from normal behavior profile about network packet and embody self recognition algorithm to use self-nonself discrimination in the human immune system to detect anomaly behavior. Sense change because monitors self-file creating anomaly detector based on Negative Selection Algorithm that is self recognition algorithm's one and detects anomaly behavior. And we achieve simulation to use DARPA Network Dataset and verify effectiveness of algorithm through the anomaly detection rate.

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Normal Behavior Profiling based on Bayesian Network for Anomaly Intrusion Detection (이상 침입 탐지를 위한 베이지안 네트워크 기반의 정상행위 프로파일링)

  • 차병래;박경우;서재현
    • Journal of the Korea Society of Computer and Information
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    • v.8 no.1
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    • pp.103-113
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    • 2003
  • Program Behavior Intrusion Detection Technique analyses system calls that called by daemon program or root authority, constructs profiles. and detectes anomaly intrusions effectively. Anomaly detections using system calls are detected only anomaly processes. But this has a Problem that doesn't detect affected various Part by anomaly processes. To improve this problem, the relation among system calls of processes is represented by bayesian probability values. Application behavior profiling by Bayesian Network supports anomaly intrusion informations . This paper overcomes the Problems of various intrusion detection models we Propose effective intrusion detection technique using Bayesian Networks. we have profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions we had simulation by proposed normal behavior profiling technique using UNM data.

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A Design of FHIDS(Fuzzy logic based Hybrid Intrusion Detection System) using Naive Bayesian and Data Mining (나이브 베이지안과 데이터 마이닝을 이용한 FHIDS(Fuzzy Logic based Hybrid Intrusion Detection System) 설계)

  • Lee, Byung-Kwan;Jeong, Eun-Hee
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.5 no.3
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    • pp.158-163
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    • 2012
  • This paper proposes an FHIDS(Fuzzy logic based Hybrid Intrusion Detection System) design that detects anomaly and misuse attacks by using a Naive Bayesian algorithm, Data Mining, and Fuzzy Logic. The NB-AAD(Naive Bayesian based Anomaly Attack Detection) technique using a Naive Bayesian algorithm within the FHIDS detects anomaly attacks. The DM-MAD(Data Mining based Misuse Attack Detection) technique using Data Mining within it analyzes the correlation rules among packets and detects new attacks or transformed attacks by generating the new rule-based patterns or by extracting the transformed rule-based patterns. The FLD(Fuzzy Logic based Decision) technique within it judges the attacks by using the result of the NB-AAD and DM-MAD. Therefore, the FHIDS is the hybrid attack detection system that improves a transformed attack detection ratio, and reduces False Positive ratio by making it possible to detect anomaly and misuse attacks.

The Decision Method of A Threshold in Sequence-based Anomaly Detection Sensor (순서기반 비정상행위 탐지 센서의 임계치 결정 방법)

  • Kim, Yong-Min;Kim, Min-Su;Kim, Hong-Geun;No, Bong-Nam
    • The KIPS Transactions:PartC
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    • v.8C no.5
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    • pp.507-516
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    • 2001
  • In this paper, we implement sequence-based anomaly detection sensor using SOM and HMM, and analyze what is important information in system call and how a threshold is decided. The new filtering and reduction rules of SOM reduces the input size of HMM. This gives real-time processing to HMM-based anomaly detection sensor. Also, we introduced an anomaly count into the sensor. Due to lessened sensibility, a user easily understand easily the detection information and false-positive was decreased. And the active coordination of the threshold value makes the detection sensor adapt according to the system condition.

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A Study on Traffic Anomaly Detection Scheme Based Time Series Model (시계열 모델 기반 트래픽 이상 징후 탐지 기법에 관한 연구)

  • Cho, Kang-Hong;Lee, Do-Hoon
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.5B
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    • pp.304-309
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    • 2008
  • This paper propose the traffic anomaly detection scheme based time series model. We apply ARIMA prediction model to this scheme and transform the value of the abnormal symptom into the probability value to maximize the traffic anomaly symptom detection. For this, we have evaluated the abnormal detection performance for the proposed model using total traffic and web traffic included the attack traffic. We will expect to have an great effect if this scheme is included in some network based intrusion detection system.

Anomaly Detection in Smart Homes Using Bayesian Networks

  • Saqaeeyan, Sasan;javadi, Hamid Haj Seyyed;Amirkhani, Hossein
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.4
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    • pp.1796-1816
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    • 2020
  • The health and safety of elderly and disabled patients who cannot live alone is an important issue. Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information. In the real world, there is a causal relationship between an occupant's behaviour and the order in which appliances are used in the home. Bayesian networks are appropriate tools for assessing the probability of an effect due to the occurrence of its causes, and vice versa. This paper defines different subsets of random variables on the basis of sensory data from a smart home, and it presents an anomaly detection system based on various models of Bayesian networks and drawing upon these variables. We examine different models to obtain the best network, one that has higher assessment scores and a smaller size. Experimental evaluations of real datasets show the effectiveness of the proposed method.

A Criterion on Profiling for Anomaly Detection (이상행위 탐지를 위한 프로파일링 기준)

  • 조혁현;정희택;김민수;노봉남
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.7 no.3
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    • pp.544-551
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    • 2003
  • Internet as being generalized, intrusion detection system is needed to protect computer system from intrusions synthetically. We propose a criterion on profiling for intrusion detection system using anomaly detection. We present the cause of false positive on profiling and propose anomaly method to control this. Finally, we propose similarity function to decide whether anomaly action or not for user pattern using pattern database.

Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes

  • Jung, Guik;Ha, Hyunsoo;Lee, Sangjun
    • Journal of Information Processing Systems
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    • v.17 no.6
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    • pp.1071-1082
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    • 2021
  • Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.