In this paper, a novel agent-based distributed intrusion detection system (IDS) is proposed, which integrates the desirable features provided by the distributed agent-based design methodology with the high accuracy an...
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ISBN:
(纸本)0769525539;97
In this paper, a novel agent-based distributed intrusion detection system (IDS) is proposed, which integrates the desirable features provided by the distributed agent-based design methodology with the high accuracy and speed response of the principal component classifier (PCC). Experimental results have shown that the PCC lightweight anomaly detection classifier outperforms other existing anomaly detection algorithms such as the KNN and LOF classifiers. In order to assess the performance of the PCC classifier on a real network environment, the relative assumption model together with feature extraction techniques are used to generate normal and anomalous traffic in a LAN testbed. Finally, scalability and response performance of the proposed system are investigated through the simulation of the proposed communication architecture. The simulation results demonstrate a satisfactory linear relationship between the degradation of response performance and the scalability of the system
To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet...
To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.
In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorith...
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ISBN:
(数字)9798350385557
ISBN:
(纸本)9798350385564
In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.
In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity ...
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In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including low training and classification times and low processing power and memory requirements. In addition, C-RSPM is capable of adaptively selecting nonconsecutive principal dimensions from the statistical information of the training data set to achieve an accurate modeling of a representative subspace. Experimental results have shown that the proposed C-RSPM approach outperforms other supervised classification methods such as SIMCA, C4.5 decision tree, decision table (DT), nearest neighbor (NN), KNN, support vector machine (SVM), I-NN best warping window DTW, I-NN DTW with no warping window, and the well-known classifier boosting method AdaBoost with SVM
Intelligent Environments most commonly take a physical form such as homes, offices, hotels, restaurants, shops, that are equipped with advanced networked computer based systems, which enable better or new lifestyles f...
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Intelligent Environments most commonly take a physical form such as homes, offices, hotels, restaurants, shops, that are equipped with advanced networked computer based systems, which enable better or new lifestyles for people. However, Intelligent Environments can also take the form of virtual online spaces such as SecondLife, which can both mimic the real world and provide functionalities which could not be provided in reality, such as advanced simulations and movement. There is the growing trend for people to spend more time in such virtual environments and, to these ends, this work in progress paper reports on a new project, +Spaces which is developing a range of virtual world tools for e-government applications, and presents some of the concepts and technical challenges involved in creating these intelligent virtual spaces for e-government.
Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and t...
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Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and translatability of complex human motions, intelligent motion feedback, and fine-grained motion skill assessment to design next-generation interactive human-machine teaming systems. Motivated by this, we design a wearable sensing framework for assessing the players’ performance and consider a live badminton game as our use case. Generally, the players on the field try to improve their performance by focusing on fast and synchronous coordination of their limbs’ reflex actions to have the ideal body postures to perform the desired shot. Learning the minute dissimilarities and distinctive traits from each limb of the players simultaneously can help assess the players’ performance and specific skillsets during a game. This paper proposes a multi-task learning framework, PerMTL to learn the shared features from each player’s limb. The PerMTL comprises a task-specific regressor output layer that helps to determine the dissimilarities and distinctive traits between the player’s limbs for collective inference in a body sensor network (BSN) environment. We evaluate the PerMTL framework using publicly available Badminton Activity Recognition (BAR) and Daily and Sports Activities (DSA) datasets. Empirical results indicate that PerMTL achieves R 2 Score of ≈ 82% in predicting the players’ performance.
The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively ...
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The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (unsupervised principal component classifier) algorithm is a multiclass unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms
Mobile Ad hoc NETwork (MANET) is a type of wireless network consisting of a set of self-configured mobile hosts that can communicate with each other using wireless links without the assistance of any fixed infrastruct...
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Mobile Ad hoc NETwork (MANET) is a type of wireless network consisting of a set of self-configured mobile hosts that can communicate with each other using wireless links without the assistance of any fixed infrastructure. This has made possible to create a distributed mobile computing application and has also brought several new challenges in distributed algorithm design. Checkpointing is a well explored fault tolerance technique for the wired and cellular mobile networks. However, it is not directly applicable to MANET due to its dynamic topology, limited availability of stable storage, partitioning and the absence of fixed infrastructure. In this paper, we propose an adaptive, coordinated and non-blocking checkpointing algorithm to provide fault tolerance in cluster based MANET, where only minimum number of mobile hosts in the cluster should take checkpoints. The performance analysis and simulation results show that the proposed scheme performs well compared to works related.
Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS)...
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Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.
Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust ...
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ISBN:
(纸本)9781479919611
Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset. In this paper, we proposed a correntropy supervised NMF (CSNMF) to simultaneously overcome aforementioned deficiencies. In particular, CSNMF maximizes the correntropy between the data matrix and its reconstruction in low-dimensional space to inhibit outliers during learning the subspace, and narrows the minimizes the distances between coefficients of any two samples with the same class labels to enhance the subsequent classification performance. To solve CSNMF, we developed a multiplicative update rules and theoretically proved its convergence. Experimental results on popular face image datasets verify the effectiveness of CSNMF comparing with NMF, its supervised variants, and its robustified variants.
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