Biblio
The wireless communication has become very vast, important and easy to access nowadays because of less cost associated and easily available mobile devices. It creates a potential threat for the community while accessing some secure information like banking passwords on the unsecured network. This proposed research work expose such a potential threat such as Rogue Access Point (RAP) detection using soft computing prediction tool. Fuzzy logic is used to implement the proposed model to identify the presence of RAP existence in the network.
Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.
Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.
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.
Mobile Ad hoc Network (MANET) is the collection of mobile devices which could change the locations and configure themselves without a centralized base point. Mobile Ad hoc Networks are vulnerable to attacks due to its dynamic infrastructure. The routing attacks are one among the possible attacks that causes damage to MANET. This paper gives a new method of risk aware response technique which is combined version the Dijkstra's shortest path algorithm and Destination Sequenced Distance Vector (DSDV) algorithm. This can reduce black hole attacks. Dijkstra's algorithm finds the shortest path from the single source to the destination when the edges have positive weights. The DSDV is an improved version of the conventional technique by adding the sequence number and next hop address in each routing table.
With the rapid development of mobile internet, mobile devices are requiring more complex authorization policy to ensure an secure access control on mobile data. However mobiles have limited resources (computing, storage, etc.) and are not suitable to execute complex operations. Cloud computing is an increasingly popular paradigm for accessing powerful computing resources. Intuitively we can solve that problem by moving the complex access control process to the cloud and implement a fine-grained access control relying on the powerful cloud. However the cloud computation may not be trusted, a crucial problem is how to verify the correctness of such computations. In this paper, we proposed a public verifiable cloud access control scheme based on Parno's public verifiable computation protocol. For the first time, we proposed the conception and concrete construction of verifiable cloud access control. Specifically, we firstly design a user private key revocable Key Policy Attribute Based Encryption (KP-ABE) scheme with non-monotonic access structure, which can be combined with the XACML policy perfectly. Secondly we convert the XACML policy into the access structure of KP-ABE. Finally we construct a security provable public verifiable cloud access control scheme based on the KP-ABE scheme we designed.
Nowadays, mobile devices have become one of the most popular instruments used by a person on its regular life, mainly due to the importance of their applications. In that context, mobile devices store user's personal information and even more data, becoming a personal tracker for daily activities that provides important information about the user. Derived from this gathering of information, many tools are available to use on mobile devices, with the restrain that each tool only provides isolated information about a specific application or activity. Therefore, the present work proposes a tool that allows investigators to obtain a complete report and timeline of the activities that were performed on the device. This report incorporates the information provided by many sources into a unique set of data. Also, by means of an example, it is presented the operation of the solution, which shows the feasibility in the use of this tool and shows the way in which investigators have to apply the tool.