Browsing by Author "Pedrycz, Witold"

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Applications of sketches in network traffic measurement: A survey
(2022-06) Han, Hui; Yan, Zheng; Jing, Xuyang; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big-volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.
Data collection for attack detection and security measurement in Mobile Ad Hoc Networks: A survey
(2018-03-01) Liu, Gao; Yan, Zheng; Pedrycz, Witold
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Mobile Ad Hoc Network (MANET) is becoming one type of major next generation wireless networks. Nevertheless, it easily suffers from various attacks due to its specific characteristics. In order to evaluate and measure the security of MANET in real time and make this network react accordingly, a promising alternative is to integrate detection mechanisms that play a role of the second line of defense to detect attacks in MANETs. We note that in most attack detection mechanisms, it is essential and crucial to collect the data related to security for further analysis. If security-related data collection is untrustworthy, attack detection and security measurement might be impacted and disabled. Unfortunately, few existing studies concern security-related data collection in attack detection for the purpose of trustworthy security measurement. The literature lacks a thorough survey on security-related data collection for attack detection and security measurement in MANETs. In this paper, we propose a number of requirements for trustworthy security-related data collection, and then review detection mechanisms in MANETs that were published in recent 20 years. In particular, we employ the proposed requirements as a set of criteria to evaluate the existing work about security-related data collection. Based on the survey and evaluation, we identify a number of open issues and point out future research directions.
Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving
(2024-09) Azam, Shoaib; Munir, Farzeen; Kyrki, Ville; Kucner, Tomasz Piotr; Jeon, Moongu; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Learning contextual and spatial environmental representations enhances autonomous vehicle’s hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67 m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA’s Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions.
ExtendedSketch: Fusing Network Traffic for Super Host Identification with a Memory Efficient Sketch
(2021-09-09) Jing, Xuyang; Yan, Zheng; Han, Hui; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Sketches have been widely applied to identify super hosts in an efficient and accurate way. However, most sketches cannot flexibly balance memory usage and accuracy in host cardinality estimation. In order to solve this issue, we propose a novel extensible and reversible sketch, named ExtendedSketch, to achieve accurate super host identification with high memory efficiency. The core idea of ExtendedSketch is to monitor low-cardinality hosts with small-sized counters while dynamically extend the size of counters when monitoring high-cardinality hosts by applying an adaptive extension strategy. Such the strategy can adaptively increase counter size according to network traffic status at runtime, which not only ensures the accuracy of high-cardinality host estimation but also avoids unnecessary memory consumption. We perform theoretical analysis and conduct a series of experimental evaluations on ExtendedSketch based on real world network traffic. Experimental results show that under same memory usage, compared to the state-of-the-art, ExtendedSketch achieves 1.47.5 times smaller error rate in measuring host cardinality with 1.926.7 times better accuracy on super host identification and 95215 times faster speed on abnormal address reconstruction. Its advance in accuracy and efficiency demonstrates the practical significance of ExtendedSketch for super host identification.
A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering
(2022-05-01) Jing, Xuyang; Yan, Zheng; Shen, Yinghua; Pedrycz, Witold; Yang, Ji
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Learning a proper distance for clustering from prior knowledge falls into the realm of semisupervised fuzzy clustering. Although most existing learning methods take prior knowledge (e.g., pairwise constraints) into account, they pay little attention to local knowledge of data, which, however, can be utilized to optimize the distance. In this article, we propose a novel distance learning method, which learns from the Group-level information, for semisupervised fuzzing clustering. We first present a new format of constraint information, called Group-level constraints, by elevating the pairwise constraints (must-links and cannot-links) from point level to Group level. The Groups, generated around data points contained in the pairwise constraints, carry not only the local information of data (the relation between close data points) but also more background information under some given limited prior knowledge. Then, we propose a novel method to learn a distance by using the Group-level constraints, namely, Group-based distance learning, in order to optimize the performance of fuzzy clustering. The distance learning process aims to pull must-link Groups as close as possible while pushing cannot-link Groups as far as possible. We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations. The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by using the neural network, which can explicitly provide nonlinear mappings. Experimental results based on both synthetic and real-world datasets show that the proposed methods yield much better performance compared to other distance learning methods using pairwise constraints.
Network traffic classification for data fusion: A survey
(2021-08) Zhao, Jingjing; Jing, Xuyang; Yan, Zheng; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Traffic classification groups similar or related traffic data, which is one main stream technique of data fusion in the field of network management and security. With the rapid growth of network users and the emergence of new networking services, network traffic classification has attracted increasing attention. Many new traffic classification techniques have been developed and widely applied. However, the existing literature lacks a thorough survey to summarize, compare and analyze the recent advances of network traffic classification in order to deliver a holistic perspective. This paper carefully reviews existing network traffic classification methods from a new and comprehensive perspective by classifying them into five categories based on representative classification features, i.e., statistics-based classification, correlation-based classification, behavior-based classification, payload-based classification, and port-based classification. A series of criteria are proposed for the purpose of evaluating the performance of existing traffic classification methods. For each specified category, we analyze and discuss the details, advantages and disadvantages of its existing methods, and also present the traffic features commonly used. Summaries of investigation are offered for providing a holistic and specialized view on the state-of-art. For convenience, we also cover a discussion on the mostly used datasets and the traffic features adopted for traffic classification in the review. At the end, we identify a list of open issues and future directions in this research field.
A reversible sketch-based method for detecting and mitigating amplification attacks
(2019-09-15) Jing, Xuyang; Zhao, Jingjing; Zheng, Qinghua; Yan, Zheng; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Amplification attacks bring serious threats to network security due to their characteristics of anonymity and amplification. How to detect amplification attacks attracts more and more attention. However, as the age of networking for big data is coming, traditional amplification attack detection methods become inefficient due to the impact of big-volume network traffic that swamp significant signals of attacks. The premise of accurate effective attack detection is efficiently processing big-volume traffic. In this paper, we propose a meaningful work that applies sketch technique to detect and mitigate amplification attacks. This step enables the detection method to handle big-volume network traffic. We use a Chinese Reminder Theorem based Reversible Sketch to directly collect network traffic and then monitor the abrupt changes in one-to-one mapping between request packets and response packets to identify amplification attack traffic. The detection mechanism is robust and efficient since it does not need to record complicated traffic features and makes full use of the basic characteristic of amplification attacks. We examine the performance of our method through a series of experiments conducted on simulation and real world traffic. The results denote that the method can accurately detect and mitigate amplification attacks.
Security Data Collection and Data Analytics in the Internet: A Survey
(2019-01-01) Jing, Xuyang; Yan, Zheng; Pedrycz, Witold
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Attacks over the Internet are becoming more and more complex and sophisticated. How to detect security threats and measure the security of the Internet arises a significant research topic. For detecting the Internet attacks and measuring its security, collecting different categories of data and employing methods of data analytics are essential. However, the literature still lacks a thorough review on security-related data collection and analytics on the Internet. Therefore, it becomes a necessity to review the current state of the art in order to gain a deep insight on what categories of data should be collected and which methods should be used to detect the Internet attacks and to measure its security. In this paper, we survey existing studies about security-related data collection and analytics for the purpose of measuring the Internet security. We first divide the data related to network security measurement into four categories: 1) packet-level data; 2) flow-level data; 3) connection-level data; and 4) host-level data. For each category of data, we provide a specific classification and discuss its advantages and disadvantages with regard to the Internet security threat detection. We also propose several additional requirements for security-related data analytics in order to make the analytics flexible and scalable. Based on the usage of data categories and the types of data analytic methods, we review current detection methods for distributed denial of service flooding and worm attacks by applying the proposed requirements to evaluate their performance. Finally, based on the completed review, a list of open issues is outlined and future research directions are identified.
SuperSketch: A Multi-Dimensional Reversible Data Structure for Super Host Identification
(2022-07) Jing, Xuyang; Han, Hui; Yan, Zheng; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Facing big network traffic data, effective data compression becomes crucially important and urgently needed for estimating host cardinalities and identifying super hosts. However, the current literature confronts several challenges: incapability of simultaneously measuring various types of host cardinalities and inability to efficiently reconstruct super host addresses. To address these challenges, in this paper, we propose a novel sketch data structure, named SuperSketch, to simultaneously measure multiple types of host cardinalities with the purpose of efficiently identifying super hosts. SuperSketch has two significant characteristics: multi-dimensionality and reversibility. The multi-dimensionality makes SuperSketch capable of simultaneously measuring Source Cardinality, Destination Cardinality and Destination Port Cardinality. The reversibility allows SuperSketch to accurately and quickly reconstruct the original addresses of super hosts once they are identified. We conduct both theoretical analysis and performance evaluation based on real-world network traffic. Experimental results show that SuperSketch achieves outstanding performance for multi-cardinality measurement, super host identification and host address reconstruction.
A survey on machine learning for data fusion
(2020-05-01) Meng, Tong; Jing, Xuyang; Yan, Zheng; Pedrycz, Witold
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Data fusion is a prevalent way to deal with imperfect raw data for capturing reliable, valuable and accurate information. Comparing with a range of classical probabilistic data fusion techniques, machine learning method that automatically learns from past experiences without explicitly programming, remarkably renovates fusion techniques by offering the strong ability of computing and predicting. Nevertheless, the literature still lacks a thorough review of the recent advances of machine learning for data fusion. Therefore, it is beneficial to review and summarize the state of the art in order to gain a deep insight on how machine learning can benefit and optimize data fusion. In this paper, we provide a comprehensive survey on data fusion methods based on machine learning. We first offer a detailed introduction to the background of data fusion and machine learning in terms of definitions, applications, architectures, processes, and typical techniques. Then, we propose a number of requirements and employ them as criteria to review and evaluate the performance of existing fusion methods based on machine learning. Through the literature review, analysis and comparison, we finally come up with a number of open issues and propose future research directions in this field.
A Survey on Trust Evaluation Based on Machine Learning
(2020-09-28) Wang, Jingwen; Jing, Xuyang; Yan, Zheng; Fu, Yulong; Pedrycz, Witold; Yang, Laurence T.
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Trust evaluation is the process of quantifying trust with attributes that influence trust. It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. In order to overcome these problems and intelligently and automatically evaluate trust, machine learning has been applied into trust evaluation. Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic. In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. Then, we justify a number of requirements that a sound trust evaluation method should satisfy, and propose them as evaluation criteria to assess the performance of trust evaluation methods. Furthermore, we systematically organize existing methods according to application scenarios and provide a comprehensive literature review on trust evaluation from the perspective of machine learning's function in trust evaluation and evaluation granularity. Finally, according to the completed review and evaluation, we explore some open research problems and suggest the directions that are worth our research effort in the future.
A Survey on Trust Models in Heterogeneous Networks
(2022) Wang, Jie; Yan, Zheng; Wang, Haiguang; Li, Tieyan; Pedrycz, Witold
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Heterogeneous networks (HetNets) merge different types of networks into an integrated network system, which has become a hot research area in recent years towards next-generation communication networks. HetNets aim to effectively exploit network resources and provide seamless connectivity for heterogeneous objects. Unlike other networks, HetNets hold such characteristics as heterogeneity, openness, distribution, multi-domain involvement, thus are susceptible to various security threats and attacks. Traditional security approaches are not sufficiently effective in defending against them. With extensive study and practice, researchers found that trust models offer effective measures to enhance the security and reliability of a network system. However, there still lacks a comprehensive survey on the recent advances of trust models in HetNets. In this paper, we fill this gap. We first retrospect the history of HetNets research and introduce important concepts related to trust. Then, we propose a set of criteria that a sound trust model should satisfy, which can also serve as a measure to evaluate the quality of a trust model, i.e., Quality of Trust (QoT). We provide taxonomies of trust models and their applications, and continue with a thorough review on trust models in HetNets. Based on the review, a list of open issues is highlighted, and corresponding future research directions are suggested to advance the research on trustworthy HetNets.