Detecting malware based on dns graph mining

Author: iarh

August undefined, 2024

WebDetecting malicious domains in DNS trafﬁc originating from end hosts in real-time is a crucial step for preventing these vulnerable hosts from being compromised by a wide spectrum of cyber attacks. On the other hand, cyber attackers have devised intel-ligent mechanisms such as DNS based domain ﬂuxing [6] WebOct 1, 2015 · A DNS graph mining-based malware detection approach that is efficient and effective in detecting malwares and inferring graph nodes' reputation scores using …

Detecting Malware Based on DNS Graph Mining - SAGE Journals

WebMar 26, 2024 · Table 2 shows the detection results of five machine learning methods, where MBGINet-FCG and MBGINet-CFG denote the effects of MBGINet on two levels of graph features, and the remaining three models are baseline methods. The grayscale image (GI) method is derived from [], which detects cryptocurrency mining attacks in browsers … WebApr 11, 2024 · Some researchers construct relationship connection graph models between domain names based on DNS traffic to detect whether an unknown domain name is benign or malicious, like (Manadhata et al., 2014, Tran et al., 2024, Li et al., 2013, Peng et al., 2024). Such methods aim to construct relationships between different domain names at … dwight newsome

Encrypted Malware Traffic Detection via Graph-based Network …

WebAbstract. Malware remains a major threat to nowadays Internet. In this paper, we propose a DNS graph mining-based malware detection approach. A DNS graph is composed of … WebDetecting Malware Based on DNS Graph Mining FutaiZou,1 SiyuZhang,2 WeixiongRao,3 andPingYi1 ... based on DNS graph. The purpose of mining malware is … WebThe above laws mean that the message delivery mechanism of BP algorithm ideally suits for malware mining based on DNS graph. The purpose of mining malware is to let the … dwight newspaper

Discovering malware based on co-clustering host-domain graphs

Detecting Malicious Domains via Graph Inference SpringerLink

WebGMAD: Graph-based Malware Activity Detection by DNS traffic analysis. Computer Communications 49 (2014), 33–47. Google Scholar Digital Library; Kai Lei, Qiuai Fu, … WebMar 11, 2024 · While many threats were analyzed, the report found cryptomining generated the most malicious DNS traffic out of any individual category. When placed inside victims' environments, cryptomining malware abuses computing resources to mine for digital currencies like bitcoin, which can be profitable to threat actors. "While cryptomining is … dwight nichols obituaryWebDetecting Malware Based on DNS Graph Mining @article{Zou2015DetectingMB, title={Detecting Malware Based on DNS Graph Mining}, author={Futai Zou and Siyu … crystal kaswell tempting

"WebNov 11, 2024 · As shown in Table 3, the precision rate of our model is 97.3%, the recall rate is 87.8%, and the false negative rate is 12.3%. It shows that our algorithm can detect … " - Detecting malware based on dns graph mining

Detecting malware based on dns graph mining

Heterogeneous Provenance Graph Learning Model Based APT Detection

WebMay 16, 2024 · The malicious use of DNS became widely known by the late 2000s detection of a botnet that generated domain names dynamically. While the botnet used a traditional worm-like propagation to spread, it had a centralized command and control unit to which the bots connected with their daily routines for seeking out the pseudo-random … WebDetecting Malware Based on DNS Graph Mining. Futai Zou, Siyu Zhang, Weixiong Rao and Ping Yi. International Journal of Distributed Sensor Networks, 2015, vol. 11, issue …

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WebFeb 7, 2024 · In this section, we present our design of MalShoot. MalShoot is a lightweight method for identifying malicious domains using passive DNS database. It consists of three modules: 1. Representation Module: The representation module is designed for representing every individual domain name in PDNS database as a low-dimensional vector through … WebIn this paper, we propose a DNS graph mining-based malware detection approach. A DNS graph is composed of DNS nodes, which represent server IPs, client IPs, and …

WebHeterogeneous Provenance Graph Learning Model Based APT Detection DONG Chengyu, LYU Mingqi, CHEN Tieming, ZHU Tiantian ... in 1982,Ph.D,associated professor,is a member of China Computer Federation.His main research interests include data mining and ubiquitous computing. Supported by: Joint Funds of the National … WebJul 9, 2024 · 5 Conclusion. This study proposes a new method for mining malicious domain based on two relationship domains-clients to do multi-confirmations algorithm and …

WebDetecting Malware Based on DNS Graph Mining @article{Zou2015DetectingMB, title={Detecting Malware Based on DNS Graph Mining}, author={Futai Zou and Siyu Zhang and Weixiong Rao and P. Yi}, journal={International Journal of Distributed Sensor Networks}, year={2015}, volume={11} } Futai Zou, Siyu Zhang, +1 author P. Yi; … WebYADAV ET AL. : DETECTING ALGORITHMICALLY GENERATED DOMAIN-FLUX ATTACKS WITH DNS TRAFFIC ANALYSIS 1 Detecting Algorithmically Generated Domain-Flux Attacks with DNS Traffic Analysis Sandeep Yadav, Student Member, IEEE, Ashwath Kumar Krishna Reddy, A.L. Narasimha Reddy, Fellow, IEEE, and Supranamaya Ranjan …

WebBotnet Detection Based On Machine Learning Techniques Using DNS Query Data (PDF) Botnet Detection Based On Machine Learning Techniques Using DNS Query Data quynh nguyen - Academia.edu Academia.edu no longer supports Internet Explorer.

Web境外组织对我国政府、军事及其它重要信息系统的高级可持续性攻击和窃密行为给我国国家安全带来了巨大的潜在危害，近年来先后发生了多起危害严重的网络窃密事件。现有技术由于监测面小、数据关联度不够、分析不够精细等原因，在抵御国家级攻击时表现不能令人满意。 crystal kaswell websiteWebIshikura et al., in , proposed a DNS tunneling detection method based on the cache-property-aware features. The proposed approach used the cache miss count to characterize the DNS tunneling traffic. Based on the selected feature, two filters have been introduced to detect DNS tunneling: a long short-term memory (LSTM) and a rule-based filter. crystal kathleen coffmanWebGMAD: Graph-based Malware Activity Detection by DNS traffic analysis. Computer Communications 49 (2014), 33–47. Google Scholar Digital Library; Kai Lei, Qiuai Fu, Jiake Ni, 2024. ... Detecting malware based on DNS graph mining. International Journal of Distributed Sensor Networks 11, 10 (2015), 102687. Google Scholar; Cited By View all. … crystal katana tool hobby lobbyWebLee J. and Lee H. 2014. GMAD: Graph-based malware activity detection by DNS traffic analysis. Computer Communications 49 (2014), 33--47. ... Futai Zou, Siyu Zhang, Weixiong Rao, and Ping Yi. 2015. Detecting malware based on DNS graph mining. International Journal of Distributed Sensor Networks 2015 (2015). Google Scholar Digital Library; … dwight nicholasWebJun 15, 2024 · The goal of Ringer is to discover domains involved in malicious activities by analyzing passive DNS traffic (traces). As shown in the Fig. 1, the system architecture of Ringer consists of three modules: preprocessing, graph construction and dynamic GCN.In order to better describe our research, we introduce some notations listed in Table 1.. 4.1 … dwight nicholsonWebMay 16, 2016 · Detecting Malware Based on DNS Graph Mining. Show details Hide details. ... Hu and Dullien conducted similarity analysis based on the flow graph of calls from malicious codes as part of ... This study focused on the area needed to use the existing technology of detecting the malware variation and classifying groups in an actual … dwight nickleson ministryWebThis study focused on HTTPS-enabled phishing websites to construct and analyze DNS graphs of domain names and IP addresses ofphishing websites using Certificate Transparency (CT) logs, and examined the differences between benign and phishing website in terms of the number of nodes per component and average node degree. The … dwight nothstein obituary