Infrastructure for Detecting Android Malware

Delosières, Laurent; García, David Hernández

doi:10.1007/978-3-319-01604-7_38

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…8. 3) Attack detectors [119]- [122] that are designed to detect attacks at edge devices and SFEs, and Honeypots [123] that are also installed at SFEs, or edge devices and servers, and used to attract potential attacks and to inform the controller by sending SPs to the SRE. The SPs will contain an attack detection probability that is included in the goal function that the SRE uses for routing control.…”

Section: )mentioning

confidence: 99%

Self-Aware Networks That Optimize Security, QoS, and Energy

et al. 2020

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Section: )mentioning

confidence: 99%

Self-Aware Networks That Optimize Security, QoS, and Energy

et al. 2020

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“…Karim et al, proposed a taxonomy for mobile botnets [41]. With emergence of mobile malwares, a taxonomy for mobile malware behavioural detection was proposed [42] followed by an android malware attack vectors taxonomy based on attackers modus-operandi [43]. Khattak et al, proposed taxonomies of botnet features and botnet detection and prevention techniques [36].…”

Section: Related Workmentioning

confidence: 99%

“…112 out of 127 Trojan samples in our dataset make use of backdoors for C&C communication via GET/POST HTTP requests (see Appendix II). Other than file upload, backdoors provide other means or advantages to attackers like remote login or even in-depth reconnaissance without users' knowledge [10], [31], [43]. Analysing web traffic with sniffer like wireshark you can observe payloads associated with GET/POST command and identify if data exfiltration is taking place and what kind of data is being exfiltrated.…”

Section: Backdoorsmentioning

confidence: 99%

A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence

Kiwia

Dehghantanha

Choo

et al. 2018

Journal of Computational Science

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Malware such as banking Trojans are popular with financially-motivated cybercriminals. Detection of banking Trojans remains a challenging task, due to the constant evolution of techniques used to obfuscate and circumvent existing detection and security solutions. Having a malware taxonomy can facilitate the design of mitigation strategies such as those based on evolutionary computational intelligence. Specifically, in this paper, we propose a cyber kill chain based taxonomy of banking Trojans features. This threat intelligence based taxonomy providing a stage-by-stage operational understanding of a cyber-attack, can be highly beneficial to security practitioners and the design of evolutionary computational intelligence on Trojans detection and mitigation strategy. The proposed taxonomy is validated by using a real-world dataset of 127 banking Trojans

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“…The malware detector component will be used to detect malicious content. Data relating to identified malicious websites will be provided to the DCI by the honeyclient, which is described in more detail in [8].…”

Section: The Data Collection Infrastructurementioning

confidence: 99%

NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem

Gelenbe

Görbil

Tzovaras

et al. 2013

Information Sciences and Systems 2013

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As a consequence of the growing popularity of smart mobile devices, mobile malware is clearly on the rise, with attackers targeting valuable user information and exploiting vulnerabilities of the mobile ecosystems. With the emergence of large-scale mobile botnets, smartphones can also be used to launch attacks on mobile networks. The NEMESYS project will develop novel security technologies for seamless service provisioning in the smart mobile ecosystem, and improve mobile network security through better understanding of the threat landscape. NEMESYS will gather and analyze information about the nature of cyber-attacks targeting mobile users and the mobile network so that appropriate counter-measures can be taken. We will develop a data collection infrastructure that incorporates virtualized mobile honeypots and a honeyclient, to gather, detect and provide early warning of mobile attacks and better understand the modus operandi of cyber-criminals that target mobile devices. By correlating the extracted information with the known patterns of attacks from wireline networks, we will reveal and identify trends in the way that cyber-criminals launch attacks against mobile devices.

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Infrastructure for Detecting Android Malware

Cited by 8 publications

References 5 publications

Self-Aware Networks That Optimize Security, QoS, and Energy

Self-Aware Networks That Optimize Security, QoS, and Energy

A cyber kill chain based taxonomy of banking Trojans for evolutionary computational intelligence

NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem

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