Analysis of malicious and benign Android applications

Since its establishment, the Android applications market has been infected by a proliferation of malicious applications. Recent studies show that rogue developers are injecting malware into legitimate market applications which are then installed on open source sites for consumer uptake. Often, applications are infected several times. In this paper, we investigate the behavior of malicious Android applications, we present a simple and effective way to safely execute and analyze them. As part of this analysis, we use the Android application sandbox Droidbox to generate behavioral graphs for each sample and these provide the basis of the development of patterns to aid in identifying it. As a result, we are able to determine if family names have been correctly assigned by current anti-virus vendors. Our results indicate that the traditional anti-virus mechanisms are not able to correctly identify malicious Android applications.

Contrasting permission patterns between clean and malicious android applications

The Android platform uses a permission system model to allow users and developers to regulate access to private information and system resources required by applications. Permissions have been proved to be useful for inferring behaviors and characteristics of an application. In this paper, a novel method to extract contrasting permission patterns for clean and malicious applications is proposed. Contrary to existing work, both required and used permissions were considered when discovering the patterns. We evaluated our methodology on a clean and a malware dataset, each comprising of 1227 applications. Our empirical results suggest that our permission patterns can capture key differences between clean and malicious applications, which can assist in characterizing these two types of applications.

Malicious code detection architecture inspired by human immune system

Malicious code is a threat to computer systems globally. In this paper, we outline the evolution of malicious code attacks. The threat is evolving, leaving challenges for attackers to improve attack techniques and for researchers and security specialists to improve detection accuracy. We present a novel architecture for an effective defense against malicious code attack, inspired by the human immune system. We introduce two phases of program execution: Adolescent and Mature Phase. The first phase uses a malware profile matching mechanism, whereas the second phase uses a program profile matching mechanism. Both mechanisms are analogous to the innate immune system

Modeling the propagation and defense study of internet malicious information

 Dr. Wen's research includes modelling the propagation dynamics of malicious information, exposing the most influential people and source identification of epidemics in social networks. His research is beneficial to both academia and industry in the field of Internet social networks.

Mining permission patterns for contrasting clean and malicious android applications

An Android application uses a permission system to regulate the access to system resources and users' privacy-relevant information. Existing works have demonstrated several techniques to study the required permissions declared by the developers, but little attention has been paid towards used permissions. Besides, no specific permission combination is identified to be effective for malware detection. To fill these gaps, we have proposed a novel pattern mining algorithm to identify a set of contrast permission patterns that aim to detect the difference between clean and malicious applications. A benchmark malware dataset and a dataset of 1227 clean applications has been collected by us to evaluate the performance of the proposed algorithm. Valuable findings are obtained by analyzing the returned contrast permission patterns. © 2013 Elsevier B.V. All rights reserved.

Modeling malicious activities in cyber space

Cyber attacks are an unfortunate part of society as an increasing amount of critical infrastructure is managed and controlled via the Internet. In order to protect legitimate users, it is critical for us to obtain an accurate and timely understanding of our cyber opponents. However, at the moment we lack effective tools to do this. In this article we summarize the work on modeling malicious activities from various perspectives, discuss the pros and cons of current models, and present promising directions for possible efforts in the near future.

Malicious node traceback in opportunistic networks using merkle trees

Security is a major challenge in Opportunistic Networks because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a malicious nodes detection mechanism against a special type of packet dropping attack where the malicious node drops one or more packets and then injects new fake packets instead. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, Merkle tree hashing technique. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.

SCLPV: secure certificateless public verification for cloud-based cyber-physical-social systems against malicious auditors

Cyber-physical-social system (CPSS) allows individuals to share personal information collected from not only cyberspace but also physical space. This has resulted in generating numerous data at a user's local storage. However, it is very expensive for users to store large data sets, and it also causes problems in data management. Therefore, it is of critical importance to outsource the data to cloud servers, which provides users an easy, cost-effective, and flexible way to manage data, whereas users lose control on their data once outsourcing their data to cloud servers, which poses challenges on integrity of outsourced data. Many schemes have been proposed to allow a third-party auditor to verify data integrity using the public keys of users. Most of these schemes bear a strong assumption: the auditors are honest and reliable, and thereby are vulnerability in the case that auditors are malicious. Moreover, in most of these schemes, an auditor needs to manage users certificates to choose the correct public keys for verification. In this paper, we propose a secure certificateless public integrity verification scheme (SCLPV). The SCLPV is the first work that simultaneously supports certificateless public verification and resistance against malicious auditors to verify the integrity of outsourced data in CPSS. A formal security proof proves the correctness and security of our scheme. In addition, an elaborate performance analysis demonstrates that the SCLPV is efficient and practical. Compared with the only existing certificateless public verification scheme (CLPV), the SCLPV provides stronger security guarantees in terms of remedying the security vulnerability of the CLPV and resistance against malicious auditors. In comparison with the best of integrity verification scheme achieving resistance against malicious auditors, the communication cost between the auditor and the cloud server of the SCLPV is independent of the size of the processed data, meanwhile, the auditor in the SCLPV does not need to manage certificates.

Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks

Data aggregation in wireless sensor networks is employed to reduce the communication overhead and prolong the network lifetime. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. The goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. But none of them have tried to detect the nodes that inject into the network bogus aggregation results. Moreover, most of them usually have a communication overhead that is (at best) logarithmic per node. In this paper, we propose a secure and energy-efficient data aggregation scheme that can detect the malicious nodes with a constant per node communication overhead. In our solution, all aggregation results are signed with the private keys of the aggregators so that they cannot be altered by others. Nodes on each link additionally use their pairwise shared key for secure communications. Each node receives the aggregation results from its parent (sent by the parent of its parent) and its siblings (via its parent node), and verifies the aggregation result of the parent node. Theoretical analysis on energy consumption and communication overhead accords with our comparison based simulation study over random data aggregation trees.

First-priority relation graph-based malicious users detection in mobile social networks

Mobile social networks (MSNs) consist of many mobile users (individuals) with social characteristics, that provide a variety of data delivery services involving the social relationship among mobile individuals. Because mobile users move around based on their common interests and contact with each other more frequently if they have more social features in common in MSNs. In this paper, we first propose the first-priority relation graph, say FPRG, of MSNs. However, some users in MSNs may be malicious. Malicious users can break the data delivery through terminating the data delivery or tampering with the data. Therefore, malicious users will be detected in the process of looking for the data delivery routing to obtain efficient and reliable data delivery routing along the first-priority relation graph. Secondly, we propose one hamiltonian cycle decomposition of FPRG-based adaptive detection algorithm based on in MSNs under the PMC detection model (the system-level detection model).

Mitigating malicious feedback attacks in trust management systems

In electronic commerce (e-commerce) environment, trust management has been identified as vital component for establishing and maintaining successful relational exchanges between the trading partners. As trust management systems depend on the feedbacks provided by the trading partners, they are fallible to strategic manipulation of the rating attacks. Therefore, in order to improve the reliability of the trust management systems, an approach that addresses feedback-related vulnerabilities is paramount. This paper proposes an approach for identifying and actioning of falsified feedbacks to make trust management systems robust against rating manipulation attacks. The viability of the proposed approach is studied experimentally and the results of various simulation experiments show that the proposed approach can be highly effective in identifying falsified feedbacks.

Malicious node detection in OppNets using hash chain technique

Opportunistic Networks aim to set a reliable networks where the nodes has no end-To-end connection and the communication links often suffer from frequent disruption and long delays. The design of the OppNets routing protocols is facing a serious challenges such as the protection of the data confidentiality and integrity. OppNets exploit the characteristics of the human social, such as similarities, daily routines, mobility patterns and interests to perform the message routing and data sharing. Packet dropping attack is one of the hardest attacks in Opportunistic Networks as both the source nodes and the destination nodes have no knowledge of where or when the packet will be dropped. In this paper, we present a new malicious nodes detection technique against packet faking attack where the malicious node drops one or more packets and instead of them injects new fake packets. We have called this novel attack in our previous works a packet faking attack. Each node in Opportunistic Networks can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our hash chain based defense techniques we have two phases. The first phases is to detect the attack, and the second phases is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. In our simulation, we have achieved a very high node detection accuracy and low false negative rate.

The development of a screening questionnaire for childhood cruelty to animals

Childhood cruelty to animals may be a marker of poor prognosis amongst conduct disordered children. However, other than semistructured interviews with parents or children, there are no screening instruments for this behavior. The aim of this study was to develop such an instrument. In the first phase of the study, a parent-report questionnaire, Children's Attitudes and Behaviors Towards Animals (CABTA) was designed and piloted on 360 elementary school children, enabling community norms and a factor structure for the instrument to be derived. In the second phase, the questionnaire was completed by the parents of a small sample of children (N= 17) to establish its test-retest reliability. In the third phase of the study, the CABTA was completed by the parents of 19 children who had been diagnosed with either a Disruptive Behavioral Disorder or Attention Deficit Hyperactivity Disorder, and the results were compared with the outcome of a semistructured interview with parents regarding their child's behavior toward animals. The results of the various phases of the study indicated that the CABTA consists of two factors, Typical and Malicious Cruelty to animals, and is a reliable and valid tool for detecting childhood cruelty to animals. Possible use and adaptations of the CABTA as a screening instrument in clinical and community samples are discussed.