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检索条件"主题词=Botnet detection"

共 290 条记录，以下是81-90 订阅

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A Novel botnet detection Method Based on Preprocessing Data Packet by Graph Structure Clustering 8

A Novel Botnet Detection Method Based on Preprocessing Data ...

引用

8th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Kong, Xinling Chen, Yonghong Tian, Hui Wang, Tian Cai, Yiqiao Chen, Xin Huaqiao Univ Coll Comp Sci & Technol Xiamen Peoples R China

ISBN: (纸本)9781509051540

botnets are one of the most serious threats in the Internet, and thus the effective detection of the botnet becomes more and more important. In this paper, inspired by IP tracing technology, we propose a novel botnet detection method that can analyze the data packets, based on graph structure clustering. This method analyzes the comprehensive information of packages content and timestamp flow. Such a capability is achieved by improving the HEMST (Hierarchical Euclidean Minimum Spanning Tree) clustering algorithm. It performs a similarity matching process to find the sender of each cluster that is the controlled host in botnet. Experimental results show that the clustering correct rate can reach to 97% which demonstrates the effectiveness of our method, having a better detection rate.

关键词： botnet detection graph structure clustering match IP traceback

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Automatically Generating Payload-based Models for botnet detection

Automatically Generating Payload-based Models for Botnet Det...

引用

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Lee, Chung-Nan Chou, Fred Chen, C. M. Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Informat Management Kaohsiung Taiwan

ISBN: (纸本)9781509018932

In recent years, botnet has become a popular technique for deploying cybercrime because it is hard to be prevented and easily cause devastating loss. Therefore, in this paper, we proposed a novel approach that can automatically generate effective payload-based models purely based on the traffic of actual bot instances instead of signatures hand-tuned by human experts. In the learning phase, we group the packets of the botnet traffic and the benign traffic collected in advance according to their payload size and extract the signatures in the payload in order to generate the payload-based models. We then identify the high quality signatures to reduce the size of models via the information gain ratio and the probability. During the matching phase, the proposed approach uses these payload-based models to check each incoming packet. Moreover, these models can efficiently discriminate the malicious botnet traffic from the benign traffic since it doesn't perform any correlation between different packets. The proposed approach was evaluated with several real-world network traces. Experimental results demonstrate that the proposed approach can detect botnet traffic traces successfully (about 96.4%) with high efficiency and an acceptable low false alarm rate (about 0.9%).

关键词： botnet detection Distributed denial-of-service Payload-based models Signature

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An Active and Dynamic botnet detection Approach to Track Hidden Concept Drift 19th

An Active and Dynamic Botnet Detection Approach to Track Hid...

引用

19th International Conference on Information and Communications Security (ICICS)

作者： Wang, Zhi Tian, Meiqi Jia, Chunfu Nankai Univ Coll Comp & Control Engn Tianjin 300350 Peoples R China

ISBN: (纸本)9783319895000;9783319894997

Nowadays, machine learning has been widely used as a core component in botnet detection systems. However, the assumption of machine learning algorithm is that the underlying botnet data distribution is stable for training and testing, which is vulnerable to well-crafted concept drift attacks, such as mimicry attacks, gradient descent attacks, poisoning attacks and so on. In this paper we present an active and dynamic learning approach to mitigate botnet hidden concept drift attacks. Instead of passively waiting for false negative, this approach could actively find the trend of hidden concept drift attacks using statistical p-values before performance starts to degenerate. And besides periodically retraining, this approach could dynamically reweight predictive features to track the trend of underlying concept drift. We test this approach on the public CTU botnet captures provided by malware capture facility project. The experiment results show that this approach could actively get insights of botnet hidden concept drift, and dynamically evolve to avoid model aging.

关键词： Malware botnet detection Concept drift Model aging horizontal correlation

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SocialBotHunter: botnet detection in Twitter-like Social Networking Services Using Semi-Supervised Collective Classification 16

SocialBotHunter: Botnet Detection in Twitter-like Social Net...

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16th IEEE Int Conf on Dependable, Autonom and Secure Comp/16th IEEE Int Conf on Pervas Intelligence and Comp/4th IEEE Int Conf on Big Data Intelligence and Comp/3rd IEEE Cyber Sci and Technol Congress (DASC/PiCom/DataCom/CyberSciTech)

作者： Dorri, Ali Abadi, Mahdi Dadfarnia, Mahila Tarbiat Modares Univ Dept Comp Engn Tehran Iran Yazd Univ Dept Comp Engn Yazd Iran

ISBN: (纸本)9781538675182

An online social network (OSN) is a social structure made up of a set of users that are interested to communicate with each other in an online environment in order to share information. Social networking services (SNSs) are web-based platforms for building OSNs. SNSs are increasingly threatened by social bots that are fake or compromised user accounts with malicious intent, which mimic the behavior of legitimate users to evade detection. A social botnet refers to a group of social bots under the control of a single botmaster, which collaborate to conduct the same malicious activities. Using social botnets, spammers are now able to flood news and political websites with tens of thousands of comments. In recent years, there has been a growing interest in designing advanced techniques to auto-matically detect social botnets in an SNS. The techniques often rely on either examining the structure of the underlying social graph or analyzing the social behavior of users to discriminate social bots from legitimate users. While all the techniques have shown promising results, they often suffer from slow convergence or inability to detect social bots that mimic the sophisticated behavior of legitimate users. In this paper, we address these shortcomings by presenting SocialBotHunter, a semi-supervised collective classification technique that combines the structural information of the social graph with the information on the social behavior of users in a unified manner, in order to detect social botnets in a Twitter-like SNS. The results of our experiments show that SocialBotHunter is able to accurately detect social bots involved in distributing social spam, also known as social spambots, with a low false positive rate and an acceptable detection time.

关键词： botnet detection collective classification social botnet social networking service social spambot

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BotHook: A Supervised Machine Learning Approach for botnet detection Using DNS Query Data 2nd

BotHook: A Supervised Machine Learning Approach for Botnet D...

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2nd International Conference on Communications and Cyber-Physical Engineering (ICCCE)

作者： Biradar, Anuradha D. Padmavathi, B. GH Raisoni Coll Engn & Management Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9789811387159;9789811387142

As of late, botnets are the most radical of all digital assaults and turning into the key issue in distributed computing. botnets are the system of various traded off PCs or potentially cell phones. These gadgets are contaminated with pernicious code by bot ace and controlled as gatherings. The aggressors utilize these botnets for criminal exercises, for example, Distributed disavowal of administration, click misrepresentation, phishing, spamming, sniffing traffic and spreading new malware. The primary issue is how to identify these botnets? It turns out to be all the more intriguing for the analysts identified with digital security? This rouses us to compose a survey on botnets, its engineering and identification procedures. By checking DNS asks for, one can identify the presence of bots and botnets. Along these lines, We proposes a botnet discovery demonstrate dependent on machine learning using DNS query data and increment its adequacy utilizing machine learning systems.

关键词： Domain name service botnet detection Machine learning Bot

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The Role of Machine Learning in botnet detection 11

The Role of Machine Learning in Botnet Detection

引用

11th International Conference on Internet Technology and Secured Transactions (ICITST)

作者： Miller, Sean Busby-Earle, Curtis Univ West Indies Mona Dept Comp Kingston Jamaica

ISBN: (纸本)9781908320735

Over the past ten to fifteen years botnets have gained the attention of researchers worldwide. A great deal of effort has been given to developing systems that would efficiently and effectively detect the presence of a botnet. This unique problem saw researchers applying machine learning (ML) to solve this problem. In this paper we provide a brief overview the different machine learning ( ML) methods and the part they play in botnet detection. The main aim of this paper is to clearly define the role different ML methods play in botnet detection. A clear understanding of these roles are critical for developing effective and efficient real-time online detection approaches and more robust models.

关键词： machine learning botnet detection cyber-security supervised learning unsupervised learning

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Correlation-Based HTTP botnet detection Using Network Communication Histogram Analysis

Correlation-Based HTTP Botnet Detection Using Network Commun...

引用

IEEE Conference on Applications, Information and Network Security (AINS)

作者： Naseri, Maryam Var Abidin, Wardah Zainal Eslahi, Meisam Univ Technol Malaysia Adv Informat Sch Skudai Malaysia Asia Pacific Univ Technol & Innovat Fac Comp Engn & Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781538607251

The latest generation of botnets use HTTP protocol and port 80 as their communication medium to impersonate themselves as normal web users and avoid current security solutions. In addition, the Botmasters who control the infected devices employ several techniques, such as encryption, code obfuscation, anti-honeypot capabilities and random communication patterns to keep their Bots undetectable as long as possible. However, Bots are designed to be a coordinated form of organized cyberattack in which they conduct the synchronized attacks in the form of groups. Thus, the similarities of cooperative group activities can be used as an effective measure to distinguish Bots from normal users. In this paper, we propose a histogram based behaviour analysis approach to identify the number of web requests and their time gap diversity posed by HTTP Bots. Finally, a correlation based communication histogram analysis approach is designed to detect HTTP botnets based on similarity and correlation of their group activities. The proposed correlation based HTTP botnet detection model was successfully able to detect the HTTP Bots with high accuracy, along with a very low rate of false positive.

关键词： Information Security Correlation-based detection botnet detection HTTP botnets Command and Control Mechanism

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An efficient flow-based botnet detection using supervised machine learning

An efficient flow-based botnet detection using supervised ma...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Stevanovic, Matija Pedersen, Jens Myrup Aalborg Univ Dept Elect Syst Networking & Secur Sect DK-9220 Aalborg Denmark

ISBN: (纸本)9781479923588

botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs to be observed per flow in order to capture the patterns of malicious traffic. The proposed system has been tested through the series of experiments using traffic traces originating from two well-known P2P botnets and diverse non-malicious applications. The results of experiments indicate that the system is able to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates a strong potential of using the proposed approach within a future on-line detection framework.

关键词： botnet botnet detection Traffic analysis Traffic classification Machine learning

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Real-Time botnet detection Using Nonnegative Tucker Decomposition 19

Real-Time Botnet Detection Using Nonnegative Tucker Decompos...

引用

34th ACM/SIGAPP Annual International Symposium on Applied Computing (SAC)

作者： Kanehara, Hideaki Murakami, Yuma Shimamura, Jumpei Takahashi, Takeshi Inoue, Daisuke Murata, Noboru Waseda Univ Shinjuku Ku Tokyo Japan Clwit Inc Tokyo Japan Natl Inst Informat & Commun Technol Tokyo Japan

ISBN: (纸本)9781450359337

This study focuses on darknet traffic analysis and applies tensor factorization in order to detect coordinated group activities, such as a botnet. Tensor factorization is a powerful tool for extracting co-occurrence patterns that is highly interpretable and can handle more variables than matrix factorization. We propose a simple method for detecting group activities from its extracted features. However, tensor factorization requires too high a computational cost to run in real time. To address this problem, we implemented a two-step algorithm in order to achieve fast, memory-efficient factorization. We also utilize nonnegative Tucker decomposition, one of the tensor factorization methods, because it has non-negativity constraints, to avoid physically unreasonable results. Finally, we introduce our prototype implementation of the proposed scheme, with which we demonstrate the effectiveness of the scheme by reviewing several past security incidents.

关键词： Group Activity detection botnet detection Tensor Factorization Darknet Analysis Real-Time Analysis

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Botyacc: Unified P2P botnet detection Using Behavioural Analysis and Graph Analysis

Botyacc: Unified P2P Botnet Detection Using Behavioural Anal...

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19th European Symposium on Research in Computer Security (ESORICS)

作者： Nagaraja, Shishir Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9783319112121;9783319112114

The detection and isolation of peer-to-peer botnets is an ongoing problem. We propose a novel technique for detecting P2P botnets. detection is based on unifying behavioural analysis with structured graph analysis. First, our inference technique exploits a fundamental property of botnet design. Modern botnets use peer-to-peer communication topologies which are fundamental to botnet resilience. Second, our technique extends conventional graph-based detection by incorporating behavioural analysis into structured graph analysis, thus unifying graph-theoretic detection with behavioural detection under a single algorithmic framework. We carried out evaluation over real-world P2P botnet traffic and show that the resulting algorithm can localise the majority of bots with low false-positive rate.

关键词： Traffic analysis botnet detection behavioural analysis graph theory

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