PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives.Comment: 8 pages, 2 figures, 11 tables, 2017 IEEE Conference on Dependable and Secure Computin

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets

Inspecting packets to detect intrusions faces challenges when coping with a high volume of network traffic. Packet-based detection processes every payload on the wire, which degrades the performance of network intrusion detection system (NIDS). This issue requires an introduction of a flow-based NIDS that reduces the amount of data to be processed by examining aggregated information of related packets. However, flow-based detection still suffers from the generation of the false positive alerts due to incomplete data input. This study proposed a Conditional Hybrid Intrusion Detection (CHID) by combining the flow-based with packet-based detection. In addition, it is also aimed to improve the resource consumption of the packet-based detection approach. CHID applied attribute wrapper features evaluation algorithms that marked malicious flows for further analysis by the packet-based detection. Input Framework approach was employed for triggering packet flows between the packetbased and flow-based detections. A controlled testbed experiment was conducted to evaluate the performance of detection mechanism’s CHID using datasets obtained from on different traffic rates. The result of the evaluation showed that CHID gains a significant performance improvement in terms of resource consumption and packet drop rate, compared to the default packet-based detection implementation. At a 200 Mbps, CHID in IRC-bot scenario, can reduce 50.6% of memory usage and decreases 18.1% of the CPU utilization without packets drop. CHID approach can mitigate the false positive rate of flow-based detection and reduce the resource consumption of packet-based detection while preserving detection accuracy. CHID approach can be considered as generic system to be applied for monitoring of intrusion detection systems

Botnet detection using ensemble classifiers of network flow

Recently, Botnets have become a common tool for implementing and transferring various malicious codes over the Internet. These codes can be used to execute many malicious activities including DDOS attack, send spam, click fraud, and steal data. Therefore, it is necessary to use Modern technologies to reduce this phenomenon and avoid them in advance in order to differentiate the Botnets traffic from normal network traffic. In this work, ensemble classifier algorithms to identify such damaging botnet traffic. We experimented with different ensemble algorithms to compare and analyze their ability to classify the botnet traffic from the normal traffic by selecting distinguishing features of the network traffic. Botnet Detection offers a reliable and cheap style for ensuring transferring integrity and warning the risks before its occurrence

Botnets and how to automatic detect them: exploring new ways of dealing with botnet classification: Botnets e como detectá-los automaticamente: explorando novas maneiras de lidar com a classificação botnet

Threats such as Botnets have become very popular in the current usage of the Internet, such as attacks like distributed denial of services (DoS) which can cause a significant impact on the use of technology. One way to mitigate such issues can be a focus on using intelligent models that can attempt to identify the existence of Botnets in the network traffic early. Thus, this work aims to evaluate the current state of the art on threats related to Botnets and how intelligent technology has been used in real-world restrictions such as real-time deadlines and increased network traffic. From our findings, we have indications that Botnet detection in real-time still is a more significant challenge because the computation power has not grown at the same rate that Internet traffic. This has pointed out other restrictions that must be considered, like privacy legislation and employing cryptography methods for all communications. In this context, we discuss the following steps to deal with the identified issues

Network Traffic Based Botnet Detection Using Machine Learning

The field of information and computer security is rapidly developing in today’s world as the number of security risks is continuously being explored every day. The moment a new software or a product is launched in the market, a new exploit or vulnerability is exposed and exploited by the attackers or malicious users for different motives. Many attacks are distributed in nature and carried out by botnets that cause widespread disruption of network activity by carrying out DDoS (Distributed Denial of Service) attacks, email spamming, click fraud, information and identity theft, virtual deceit and distributed resource usage for cryptocurrency mining. Botnet detection is still an active area of research as no single technique is available that can detect the entire ecosystem of a botnet like Neris, Rbot, and Virut. They tend to have different configurations and heavily armored by malware writers to evade detection systems by employing sophisticated evasion techniques. This report provides a detailed overview of a botnet and its characteristics and the existing work that is done in the domain of botnet detection. The study aims to evaluate the preprocessing techniques like variance thresholding and one-hot encoding to clean the botnet dataset and feature selection technique like filter, wrapper and embedded method to boost the machine learning model performance. This study addresses the dataset imbalance issues through techniques like undersampling, oversampling, ensemble learning and gradient boosting by using random forest, decision tree, AdaBoost and XGBoost. Lastly, the optimal model is then trained and tested on the dataset of different attacks to study its performance

Revealing the Feature Influence in HTTP Botnet Detection

Botnet are identified as one of most emerging threats due to Cybercriminals work diligently to make most of the part of the users’ network of computers as their target. In conjunction to that, many researchers has conduct a lot of study regarding on the botnets and ways to detect botnet in network traffic. Most of them only used the feature inside the system without mentioning the feature influence in botnet detection. Selecting a significant feature are important in botnet detection as it can increase the accuracy of detection. Besides, existing research focusses more on the technique of recognition rather than uncovering the purpose behind the selection. Therefore, this paper will reveal the influence feature in botnet detection using statistical method. The result obtained showed the accuracy is about 91% which is approximately acceptable to use the influence feature in detecting botnet activity

Botnet Detection Using Graph Based Feature Clustering

Detecting botnets in a network is crucial because bot-activities impact numerous areas such as security, finance, health care, and law enforcement. Most existing rule and flow-based detection methods may not be capable of detecting bot-activities in an efficient manner. Hence, designing a robust botnet-detection method is of high significance. In this study, we propose a botnet-detection methodology based on graph-based features. Self-Organizing Map is applied to establish the clusters of nodes in the network based on these features. Our method is capable of isolating bots in small clusters while containing most normal nodes in the big-clusters. A filtering procedure is also developed to further enhance the algorithm efficiency by removing inactive nodes from bot detection. The methodology is verified using real-world CTU-13 and ISCX botnet datasets and benchmarked against classification-based detection methods. The results show that our proposed method can efficiently detect the bots despite their varying behaviors

BotCap: Machine Learning Approach for Botnet Detection Based on Statistical Features

In this paper, we describe a detailed approach to develop a botnet detection system using machine learning (ML)techniques. Detecting botnet member hosts, or identifying botnet traffic has been the main subject of manyresearch efforts. This research aims to overcome two serious limitations of current botnet detection systems:First, the need for Deep Packet Inspection-DPI and the need to collect traffic from several infected hosts. Toachieve that, we have analyzed several botware samples of known botnets. Based on this analysis, we haveidentified a set of statistical features that may help to distinguish between benign and botnet malicious traffic.Then, we have carried several machine learning experiments in order to test the suitability of ML techniques andalso to pick a minimal subset of the identified features that provide best detection. We have implemented ourapproach in a tool called BotCap whose test results showed its proven ability to detect individually infected hostsin a local network

Multilayer framework for botnet detection using machine learning algorithms

The authors wish to thank Universiti Teknologi Malaysia (UTM) for its support under Research University Grant Vot- 20H04, Malaysia Research University Network (MRUN) Vot 4L876. The authors would like to acknowledge that this work was supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The work was also partially supported by the Specific Research project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic, under Grant 2102-2021. The authors are grateful for the support of student Sebastien Mambou in consultations regarding application aspects. The authors also wish to thank the Ministry of Education Malaysia for the Hadiah Latihan Persekutuan (HLP) scholarship to complete the research.A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. The traditional method of detecting botnets commonly used signature-based analysis unable to detect unseen botnets. The behavior-based analysis seems like a promising solution to the current trends of botnets that keep evolving. This paper proposes a multilayer framework for botnet detection using machine learning algorithms that consist of a ltering module and classi cation module to detect the botnet's command and control server. We highlighted several criteria for our framework, such as it must be structure-independent, protocol-independent, and able to detect botnet in encapsulated technique. We used behavior-based analysis through ow-based features that analyzed the packet header by aggregating it to a 1-s time. This type of analysis enables detection if the packet is encapsulated, such as using a VPN tunnel. We also extend the experiment using different time intervals, but a 1-s time interval shows the most impressive results. The result shows that our botnet detection method can detect up to 92% of the f-score, and the lowest false-negative rate was 1.5%.Universiti Teknologi Malaysia (UTM) through the Research University Vot-20H04Malaysia Research University Network (MRUN) Vot4L876Ministry of Higher Education through the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1Hadiah Latihan Persekutuan (HLP) Scholarship through the Ministry of Education MalaysiaSpecific Research Project (SPEV) by the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republi