Biblio
The development in the web technologies given growth to the new application that will make the voting process very easy and proficient. The E-voting helps in providing convenient, capture and count the votes in an election. This project provides the description about e-voting using an Android platform. The proposed e-voting system helps the user to cast the vote without visiting the polling booth. The application provides authentication measures in order to avoid fraud voters using the OTP. Once the voting process is finished the results will be available within a fraction of seconds. All the casted vote count is encrypted using AES256 algorithm and stored in the database in order to avoid any outbreaks and revelation of results by third person other than the administrator.
The development of mobile internet has brought convenience to people, but the openness and diversity of mobile Internet make it face the security threat of communication privacy data disclosure. In this paper, a trusted android device security communication method based on TrustZone is proposed. Firstly, Elliptic Curve Diffie-Hellman (ECDH) key agreement algorithm is used to make both parties negotiate the session key in the Trusted Execution Environment (TEE), and then, we stored the key safely in the TEE. Finally, TEE completes the encryption and decryption of the transmitted data. This paper constructs a secure communication between mobile devices without a trusted third party and analyzes the feasibility of the method from time efficiency and security. The experimental results show that the method can resist malicious application monitoring in the process of data encryption and ensures the security of the session key. Compared with the traditional scheme, it is found that the performance of the scheme is not significantly reduced.
Increased availability of mobile cameras has led to more opportunities for people to record videos of significantly more of their lives. Many times people want to share these videos, but only to certain people who were co-present. Since the videos may be of a large event where the attendees are not necessarily known, we need a method for proving co-presence without revealing information before co-presence is proven. In this demonstration, we present a privacy-preserving method for comparing the similarity of two videos without revealing the contents of either video. This technique leverages the Similarity of Simultaneous Observation technique for detecting hidden webcams and modifies the existing algorithms so that they are computationally feasible to run under fully homomorphic encryption scheme on modern mobile devices. The demonstration will consist of a variety of devices preloaded with our software. We will demonstrate the video sharing software performing comparisons in real time. We will also make the software available to Android devices via a QR code so that participants can record and exchange their own videos.
Enterprises round the globe have been searching for a way to securely empower AndroidTM devices for work but have spurned away from the Android platform due to ongoing fragmentation and security concerns. Discrepant vulnerabilities have been reported in Android smartphones since Android Lollipop release. Smartphones can be easily hacked by installing a malicious application, visiting an infectious browser, receiving a crafted MMS, interplaying with plug-ins, certificate forging, checksum collisions, inter-process communication (IPC) abuse and much more. To highlight this issue a manual analysis of Android vulnerabilities is performed, by using data available in National Vulnerability Database NVD and Android Vulnerability website. This paper includes the vulnerabilities that risked the dual persona support in Android 5 and above, till Dec 2017. In our security threat analysis, we have identified a comprehensive list of Android vulnerabilities, vulnerable Android versions, manufacturers, and information regarding complete and partial patches released. So far, there is no published research work that systematically presents all the vulnerabilities and vulnerability assessment for dual persona feature of Android's smartphone. The data provided in this paper will open ways to future research and present a better Android security model for dual persona.
Air-gapped networks are isolated from the Internet, since they store and process sensitive information. It has been shown that attackers can exfiltrate data from air-gapped networks by sending acoustic signals generated by computer speakers, however this type of covert channel relies on the existence of loudspeakers in the air-gapped environment. In this paper, we present CD-LEAK - a novel acoustic covert channel that works in constrained environments where loudspeakers are not available to the attacker. Malware installed on a compromised computer can maliciously generate acoustic signals via the optical CD/DVD drives. Binary information can then be modulated over the acoustic signals and be picked up by a nearby Internet connected receiver (e.g., a workstation, hidden microphone, smartphone, laptop, etc.). We examine CD/DVD drives and discuss their acoustical characteristics. We also present signal generation and detection, and data modulation and demodulation algorithms. Based on our proposed method, we developed a transmitter and receiver for PCs and smartphones, and provide the design and implementation details. We examine the channel and evaluate it on various optical drives. We also provide a set of countermeasures against this threat - which has been overlooked.
With the rapid development of the mobile Internet, Android has been the most popular mobile operating system. Due to the open nature of Android, c countless malicious applications are hidden in a large number of benign applications, which pose great threats to users. Most previous malware detection approaches mainly rely on features such as permissions, API calls, and opcode sequences. However, these approaches fail to capture structural semantics of applications. In this paper, we propose AMDroid that leverages function call graphs (FCGs) representing the behaviors of applications and applies graph kernels to automatically learn the structural semantics of applications from FCGs. We evaluate AMDroid on the Genome Project, and the experimental results show that AMDroid is effective to detect Android malware with 97.49% detection accuracy.
The rapid growth of Android malware has posed severe security threats to smartphone users. On the basis of the familial trait of Android malware observed by previous work, the familial analysis is a promising way to help analysts better focus on the commonalities of malware samples within the same families, thus reducing the analytical workload and accelerating malware analysis. The majority of existing approaches rely on supervised learning and face three main challenges, i.e., low accuracy, low efficiency, and the lack of labeled dataset. To address these challenges, we first construct a fine-grained behavior model by abstracting the program semantics into a set of subgraphs. Then, we propose SRA, a novel feature that depicts the similarity relationships between the Structural Roles of sensitive API call nodes in subgraphs. An SRA is obtained based on graph embedding techniques and represented as a vector, thus we can effectively reduce the high complexity of graph matching. After that, instead of training a classifier with labeled samples, we construct malware link network based on SRAs and apply community detection algorithms on it to group the unlabeled samples into groups. We implement these ideas in a system called GefDroid that performs Graph embedding based familial analysis of AnDroid malware using unsupervised learning. Moreover, we conduct extensive experiments to evaluate GefDroid on three datasets with ground truth. The results show that GefDroid can achieve high agreements (0.707-0.883 in term of NMI) between the clustering results and the ground truth. Furthermore, GefDroid requires only linear run-time overhead and takes around 8.6s to analyze a sample on average, which is considerably faster than the previous work.
Mobile systems are always growing, automatically they need enough resources to secure them. Indeed, traditional techniques for protecting the mobile environment are no longer effective. We need to look for new mechanisms to protect the mobile environment from malicious behavior. In this paper, we examine one of the most popular systems, Android OS. Next, we will propose a distributed architecture based on IDS-AM to detect intrusions by mobile agents (IDS-AM).
The presence of robots is becoming more apparent as technology progresses and the market focus transitions from smart phones to robotic personal assistants such as those provided by Amazon and Google. The integration of robots in our societies is an inevitable tendency in which robots in many forms and with many functionalities will provide services to humans. This calls for an understanding of how humans are affected by both the presence of and the reliance on robots to perform services for them. In this paper we explore the effects that robots have on humans when a service is performed on request. We expose three groups of human participants to three levels of service completion performed by robots. We record and analyse human perceptions such as propensity to trust, competency, responsiveness, sociability, and team work ability. Our results demonstrate that humans tend to trust robots and are more willing to interact with them when they autonomously recover from failure by requesting help from other robots to fulfil their service. This supports the view that autonomy and team working capabilities must be brought into robots in an effort to strengthen trust in robots performing a service.
Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.
Mobile crowd sensing (MCS) is a rapidly developing technique for information collection from the users of mobile devices. This technique deals with participants' personal information such as their identities and locations, thus raising significant security and privacy concerns. Accordingly, anonymous authentication schemes have been widely considered for preserving participants' privacy in MCS. However, mobile devices are easy to lose and vulnerable to device capture attacks, which enables an attacker to extract the private authentication key of a mobile application and to further invade the user's privacy by linking sensed data with the user's identity. To address this issue, we have devised a special anonymous authentication scheme where the authentication request algorithm can be obfuscated into an unintelligible form and thus the authentication key is not explicitly used. This scheme not only achieves authenticity and unlinkability for participants, but also resists impersonation, replay, denial-of-service, man-in-the-middle, collusion, and insider attacks. The scheme's obfuscation algorithm is the first obfuscator for anonymous authentication, and it satisfies the average-case secure virtual black-box property. The scheme also supports batch verification of authentication requests for improving efficiency. Performance evaluations on a workstation and smart phones have indicated that our scheme works efficiently on various devices.