DroidCollector: A Honeyclient for Collecting and Classifying Android Applications

Delosières, Laurent; Sánchez, Antonio Bernardo

doi:10.1007/978-3-319-09465-6_19

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Clearly, if security is a critical issue this scaling factor will be set to a much larger value than the usually measured values of energy consumption per packet, and of delay per packet. The parameters S(f, e) and T (f, e) can be easily set by the arrival to the SRE from a node e that is equipped with an attack detection mechanism such as the ones described in [122] and [125]. The SP will bring to the SRE the probability of an attack on node e, P N A (e).…”

Section: B Incorporating Security In the Goal Functionmentioning

confidence: 99%

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

et al. 2020

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Section: B Incorporating Security In the Goal Functionmentioning

confidence: 99%

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

et al. 2020

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“…For example, in [15], the authors correlated the characteristics of static analysis with those of dynamic analysis. It used deep belief networks to characterize malware; In [16], the authors used permissions and system calls to model neural networks; In [17], the authors proposed a system for Android malware detection using convolutional neural network, which used the original opcode sequence of the application as a feature; In [18], the detection system used various classifiers, including deep neural networks. It allowed to enter various information, such as intentions, permissions, system commands, and API calls.…”

Section: Machine Learningmentioning

confidence: 99%

Android malware detection based on sensitive patterns

et al. 2023

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In recent years, the rapid increase in the number and type of Android malware has brought great challenges and pressure to malware detection systems. As a widely used method in android malware detection, static detecting has been a hot topic in academia and industry. However, in order to improve the accuracy of detection, the existing static detecting methods sacrifice the excessively high analysis complexity and time cost. Moreover, the correlation between static features leads to redundancy of a large amount of data. Therefore, this paper proposes a static detecting method of Android malware based on sensitive pattern. It uses an improved FP-growth algorithm to mine frequent combinations of sensitive permissions and API calls in malicious apps and benign apps, which avoids the generation of redundant information. In addition, this paper adopts multi-layered gradient boosting decision trees algorithm to train the detection model. And a dual similarity combination method is proposed to measure the similarity between different sensitive patterns. The experimental results show that our proposed detection method has high accuracy and great generalization ability.

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“…Furthermore, while work in [21,33] focuses on a general defensive approach against DoS attacks in future networks, signalling storm specific research can roughly be categorised in the following groups: problem definition and attacks classification [5,30,31,41]; measurements in real operating networks [11,40]; modelling and simulation [1,27]; impact of attacks on energy consumption [10,12]; attacks detection and mitigation, using counters [19,20,38], change-point detection techniques [32,42], IP packet analysis [28], randomisation in RRC's functions [45], software changes in the mobile terminal [8,34], monitoring terminal's bandwidth usage [39], and detection using techniques from Artificial Intelligence [2]. As we look to the future, such as the Internet of Things (IoT), various forms of attacks will also have to be considered [6,9].…”

Section: Introductionmentioning

confidence: 99%

Signalling Attacks in Mobile Telephony

Pavloski

2018

Communications in Computer and Information Science

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Abstract. Many emerging Internet of Things devices, gateways and networks, rely either on mobile networks or on Internet Protocols to support their connectivity. However it is known that both types of networks are susceptible to different types of attacks that can significantly disrupt their operations. In particular 3rd and 4th generation mobile networks experience signalling related attacks, such as signalling storms, that have been a common problem in the last decade. This paper presents a generic model of a mobile network that includes different end user behaviours, including possible attacks to the signalling system. We then suggest two attack detection mechanisms, and evaluate them by analysis and simulation based on the generic mobile network model. Our findings suggest that mobile networks can be modified to be able to automatically detect attacks. Our results also suggest that attack mitigation can be carriedout both via the signalling system and on a "per mobile terminal" basis.

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DroidCollector: A Honeyclient for Collecting and Classifying Android Applications

Cited by 6 publications

References 5 publications

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

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

Android malware detection based on sensitive patterns

Signalling Attacks in Mobile Telephony

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