Real-time distributed malicious traffic monitoring for honeypots and network telescopes
- Hunter, Samuel O, Irwin, Barry V W, Stalmans, Etienne
- Authors: Hunter, Samuel O , Irwin, Barry V W , Stalmans, Etienne
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429660 , vital:72630 , 10.1109/ISSA.2013.6641050
- Description: Network telescopes and honeypots have been used with great success to record malicious network traffic for analysis, however, this is often done off-line well after the traffic was observed. This has left us with only a cursory understanding of malicious hosts and no knowledge of the software they run, uptime or other malicious activity they may have participated in. This work covers a messaging framework (rDSN) that was developed to allow for the real-time analysis of malicious traffic. This data was captured from multiple, distributed honeypots and network telescopes. Data was collected over a period of two months from these data sensors. Using this data new techniques for malicious host analysis and re-identification in dynamic IP address space were explored. An Automated Reconnaissance (AR) Framework was developed to aid the process of data collection, this framework was responsible for gathering information from malicious hosts through both passive and active fingerprinting techniques. From the analysis of this data; correlations between malicious hosts were identified based on characteristics such as Operating System, targeted service, location and services running on the malicious hosts. An initial investigation in Latency Based Multilateration (LBM), a novel technique to assist in host re-identification was tested and proved successful as a supporting metric for host re-identification.
- Full Text:
- Date Issued: 2013
- Authors: Hunter, Samuel O , Irwin, Barry V W , Stalmans, Etienne
- Date: 2013
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429660 , vital:72630 , 10.1109/ISSA.2013.6641050
- Description: Network telescopes and honeypots have been used with great success to record malicious network traffic for analysis, however, this is often done off-line well after the traffic was observed. This has left us with only a cursory understanding of malicious hosts and no knowledge of the software they run, uptime or other malicious activity they may have participated in. This work covers a messaging framework (rDSN) that was developed to allow for the real-time analysis of malicious traffic. This data was captured from multiple, distributed honeypots and network telescopes. Data was collected over a period of two months from these data sensors. Using this data new techniques for malicious host analysis and re-identification in dynamic IP address space were explored. An Automated Reconnaissance (AR) Framework was developed to aid the process of data collection, this framework was responsible for gathering information from malicious hosts through both passive and active fingerprinting techniques. From the analysis of this data; correlations between malicious hosts were identified based on characteristics such as Operating System, targeted service, location and services running on the malicious hosts. An initial investigation in Latency Based Multilateration (LBM), a novel technique to assist in host re-identification was tested and proved successful as a supporting metric for host re-identification.
- Full Text:
- Date Issued: 2013
Geo-spatial autocorrelation as a metric for the detection of fast-flux botnet domains
- Stalmans, Etienne, Hunter, Samuel O, Irwin, Barry V W
- Authors: Stalmans, Etienne , Hunter, Samuel O , Irwin, Barry V W
- Date: 2012
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429799 , vital:72640 , 10.1109/ISSA.2012.6320433
- Description: Botnets consist of thousands of hosts infected with malware. Botnet owners communicate with these hosts using Command and Control (C2) servers. These C2 servers are usually infected hosts which the botnet owners do not have physical access to. For this reason botnets can be shut down by taking over or blocking the C2 servers. Botnet owners have employed numerous shutdown avoidance techniques. One of these techniques, DNS Fast-Flux, relies on rapidly changing address records. The addresses returned by the Fast-Flux DNS servers consist of geographically widely distributed hosts. The distributed nature of Fast-Flux botnets differs from legitimate domains, which tend to have geographically clustered server locations. This paper examines the use of spatial autocorrelation techniques based on the geographic distribution of domain servers to detect Fast-Flux domains. Moran's I and Geary's C are used to produce classifiers using multiple geographic co-ordinate systems to produce efficient and accurate results. It is shown how Fast-Flux domains can be detected reliably while only a small percentage of false positives are produced.
- Full Text:
- Date Issued: 2012
- Authors: Stalmans, Etienne , Hunter, Samuel O , Irwin, Barry V W
- Date: 2012
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429799 , vital:72640 , 10.1109/ISSA.2012.6320433
- Description: Botnets consist of thousands of hosts infected with malware. Botnet owners communicate with these hosts using Command and Control (C2) servers. These C2 servers are usually infected hosts which the botnet owners do not have physical access to. For this reason botnets can be shut down by taking over or blocking the C2 servers. Botnet owners have employed numerous shutdown avoidance techniques. One of these techniques, DNS Fast-Flux, relies on rapidly changing address records. The addresses returned by the Fast-Flux DNS servers consist of geographically widely distributed hosts. The distributed nature of Fast-Flux botnets differs from legitimate domains, which tend to have geographically clustered server locations. This paper examines the use of spatial autocorrelation techniques based on the geographic distribution of domain servers to detect Fast-Flux domains. Moran's I and Geary's C are used to produce classifiers using multiple geographic co-ordinate systems to produce efficient and accurate results. It is shown how Fast-Flux domains can be detected reliably while only a small percentage of false positives are produced.
- Full Text:
- Date Issued: 2012
Remote fingerprinting and multisensor data fusion
- Hunter, Samuel O, Stalmans, Etienne, Irwin, Barry V W, Richter, John
- Authors: Hunter, Samuel O , Stalmans, Etienne , Irwin, Barry V W , Richter, John
- Date: 2012
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429813 , vital:72641 , 10.1109/ISSA.2012.6320449
- Description: Network fingerprinting is the technique by which a device or service is enumerated in order to determine the hardware, software or application characteristics of a targeted attribute. Although fingerprinting can be achieved by a variety of means, the most common technique is the extraction of characteristics from an entity and the correlation thereof against known signatures for verification. In this paper we identify multiple host-defining metrics and propose a process of unique host tracking through the use of two novel fingerprinting techniques. We then illustrate the application of host fingerprinting and tracking for increasing situational awareness of potentially malicious hosts. In order to achieve this we provide an outline of an adapted multisensor data fusion model with the goal of increasing situational awareness through observation of unsolicited network traffic.
- Full Text:
- Date Issued: 2012
- Authors: Hunter, Samuel O , Stalmans, Etienne , Irwin, Barry V W , Richter, John
- Date: 2012
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/429813 , vital:72641 , 10.1109/ISSA.2012.6320449
- Description: Network fingerprinting is the technique by which a device or service is enumerated in order to determine the hardware, software or application characteristics of a targeted attribute. Although fingerprinting can be achieved by a variety of means, the most common technique is the extraction of characteristics from an entity and the correlation thereof against known signatures for verification. In this paper we identify multiple host-defining metrics and propose a process of unique host tracking through the use of two novel fingerprinting techniques. We then illustrate the application of host fingerprinting and tracking for increasing situational awareness of potentially malicious hosts. In order to achieve this we provide an outline of an adapted multisensor data fusion model with the goal of increasing situational awareness through observation of unsolicited network traffic.
- Full Text:
- Date Issued: 2012
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