Journal ArticleDOI
Host-Based Intrusion Detection System with System Calls: Review and Future Trends
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TLDR
A review of the development of system-call-based HIDS and future research trends is provided, namely, the reduction of the false-positive rate, the improvement of detection efficiency, and the enhancement of collaborative security.Abstract:
In a contemporary data center, Linux applications often generate a large quantity of real-time system call traces, which are not suitable for traditional host-based intrusion detection systems deployed on every single host. Training data mining models with system calls on a single host that has static computing and storage capacity is time-consuming, and intermediate datasets are not capable of being efficiently handled. It is cumbersome for the maintenance and updating of host-based intrusion detection systems (HIDS) installed on every physical or virtual host, and comprehensive system call analysis can hardly be performed to detect complex and distributed attacks among multiple hosts. Considering these limitations of current system-call-based HIDS, in this article, we provide a review of the development of system-call-based HIDS and future research trends. Algorithms and techniques relevant to system-call-based HIDS are investigated, including feature extraction methods and various data mining algorithms. The HIDS dataset issues are discussed, including currently available datasets with system calls and approaches for researchers to generate new datasets. The application of system-call-based HIDS on current embedded systems is studied, and related works are investigated. Finally, future research trends are forecast regarding three aspects, namely, the reduction of the false-positive rate, the improvement of detection efficiency, and the enhancement of collaborative security.read more
Citations
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A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU
Sweta Bhattacharya,Siva Rama Krishnan S,Praveen Kumar Reddy Maddikunta,Rajesh Kaluri,Saurabh Singh,Thippa Reddy Gadekallu,Mamoun Alazab,Usman Tariq +7 more
TL;DR: A hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets and experimental results confirm the fact that the proposed model performs better than the existing machine learning models.
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A Gaussian error correction multi‐objective positioning model with NSGA‐II
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UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats.
TL;DR: UNICORN is presented, an anomaly-based APT detector that effectively leverages data provenance analysis that outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.
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Differential privacy for renewable energy resources based smart metering
TL;DR: Experimental results validate that the DPLM approach provides a desirable solution to protect smart grid user’s privacy by efficient noise addition and peak value protection along with having an error rate of only 1.5%.
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Privacy preserving classification on local differential privacy in data centers
Weibei Fan,Weibei Fan,Jing He,Jing He,Mengjiao Guo,Peng Li,Peng Li,Zhijie Han,Ruchuan Wang,Ruchuan Wang +9 more
TL;DR: Experiments demonstrated that the differential privacy-based classification algorithm proposed in this paper has higher iteration efficiency, better security and feasible accuracy, on the premise of ensuring availability, has reliable privacy protection characteristics and excellent timeliness.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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Caffe: Convolutional Architecture for Fast Feature Embedding
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TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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