FP-tree and SVM for Malicious Web Campaign Detection

Kruczkowski, Michał; Niewiadomska-Szynkiewicz, Ewa; Kozakiewicz, Adam

doi:10.1007/978-3-319-15705-4_19

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Anomalies in traffic flows can be detected in several ways, most often with machine learning and data mining. Machine learning is more common in systems for supporting computer emergency response teams during threat detection [ 17 , 18 ]. Data mining, as presented by Buczak and Guven [ 19 ] or Dua and Du [ 20 ], creates new possibilities for detecting new types of threats and contributes to improving the efficiency and flexibility of anomaly-based IDSs.…”

Section: Related Workmentioning

confidence: 99%

Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

Amanowicz

Jankowski²

2021

Sensors

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The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.

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Section: Related Workmentioning

confidence: 99%

Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

Amanowicz

Jankowski²

2021

Sensors

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“…In addition to misinformation, divisive information which creates polarized groups is counter to what the political system or a democratic nation needs to thrive [35]. Previous campaign detection has been focused on spam [10] and malware [33,20] in order to protect computer information systems. The most relevant work for campaign detection on social media is by Varol and collaborators [38,15].…”

Section: Related Literaturementioning

confidence: 99%

“Senator, We Sell Ads”: Analysis of the 2016 Russian Facebook Ads Campaign

Dutt

Deb

Ferrara

2018

Advances in Data Science

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One of the key aspects of the United States democracy is free and fair elections that allow for a peaceful transfer of power from one President to the next. The 2016 US presidential election stands out due to suspected foreign influence before, during, and after the election. A significant portion of that suspected influence was carried out via social media. In this paper, we look specifically at 3,500 Facebook ads allegedly purchased by the Russian government. These ads were released on May 10, 2018 by the US Congress House Intelligence Committee. We analyzed the ads using natural language processing techniques to determine textual and semantic features associated with the most effective ones. We clustered the ads over time into the various campaigns and the labeled parties associated with them. We also studied the effectiveness of Ads on an individual, campaign and party basis. The most effective ads tend to have less positive sentiment, focus on past events and are more specific and personalized in nature. The more effective campaigns also show such similar characteristics. The campaigns' duration and promotion of the Ads suggest a desire to sow division rather than sway the election.

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“…In order to identify malicious web campaigns, Kruczkowski et al [54] constructed a FP-SVM system which employed the FP-growth algorithm and the SVM method. This system could be successfully used to analyze a huge amount of dynamic, heterogenous, unstructured and imbalanced network data.…”

Section: Fp-growthmentioning

confidence: 99%

Machine Learning for Analyzing Malware

Liu

Zeng

Yan

et al. 2017

JCSM

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The Internet has become an indispensable part of people's work and life, but it also provides favorable communication conditions for malwares. Therefore, malwares are endless and spread faster and become one of the main threats of current network security. Based on the malware analysis process, from the original feature extraction and feature selection to malware analysis, this paper introduces the machine learning algorithms such as classification, clustering and association analysis, and how to use these machine learning algorithms to effectively analyze the malware and its variants.

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FP-tree and SVM for Malicious Web Campaign Detection

Cited by 7 publications

References 11 publications

Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

“Senator, We Sell Ads”: Analysis of the 2016 Russian Facebook Ads Campaign

Machine Learning for Analyzing Malware

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