Biblio

Vehicular Fog Computing (VFC) has been proposed to address the security and response time issues of Vehicular Ad Hoc Networks (VANETs) in latency-sensitive vehicular network environments, due to the frequent interactions that VANETs need to have with cloud servers. However, the anonymity protection mechanism in VFC may cause the attacker to launch Sybil attacks by fabricating or creating multiple pseudonyms to spread false information in the network, which poses a severe security threat to the vehicle driving. Therefore, in this paper, we summarize different types of Sybil attack detection mechanisms in VFC for the first time, and provide a comprehensive comparison of these schemes. In addition, we also summarize the possible impacts of different types of Sybil attacks on VFC. Finally, we summarize challenges and prospects of future research on Sybil attack detection mechanisms in VFC.

Vulnerability detection has always been an essential part of maintaining information security, and the existing work can significantly improve the performance of vulnerability detection. However, due to the differences in representation forms and deep learning models, various methods still have some limitations. In order to overcome this defect, We propose a vulnerability detection method VDBWGDL, based on weight graphs and deep learning. Firstly, it accurately locates vulnerability-sensitive keywords and generates variant codes that satisfy vulnerability trigger logic and programmer programming style through code variant methods. Then, the control flow graph is sliced for vulnerable code keywords and program critical statements. The code block is converted into a vector containing rich semantic information and input into the weight map through the deep learning model. According to specific rules, different weights are set for each node. Finally, the similarity is obtained through the similarity comparison algorithm, and the suspected vulnerability is output according to different thresholds. VDBWGDL improves the accuracy and F1 value by 3.98% and 4.85% compared with four state-of-the-art models. The experimental results prove the effectiveness of VDBWGDL.

With the continuous development of Internet technology, people pay more and more attention to private security. In particular, third-party tracking is a major factor affecting privacy security. So far, the most effective way to prevent third-party tracking is to create a blacklist. However, blacklist generation and maintenance need to be carried out manually which is inefficient and difficult to maintain. In order to generate blacklists more quickly and accurately in this era of big data, this paper proposes a machine learning system MFTrackerDetector against third-party tracking. The system is based on the theory of structural hole and only detects third-party trackers. The system consists of two subsystems, DMTrackerDetector and DFTrackerDetector. DMTrackerDetector is a JavaScript-based subsystem and DFTrackerDetector is a Flash-based subsystem. Because tracking code and non-tracking code often call different APIs, DMTrackerDetector builds a classifier using all the APIs in JavaScript as features and extracts the API features in JavaScript through dynamic analysis. Unlike static analysis method, the dynamic analysis method can effectively avoid code obfuscation. DMTrackerDetector eventually generates a JavaScript-based third-party tracker list named Jlist. DFTrackerDetector constructs a classifier using all the APIs in ActionScript as features and extracts the API features in the flash script through static analysis. DFTrackerDetector finally generates a Flash-based third-party tracker list named Flist. DFTrackerDetector achieved 92.98% accuracy in the Flash test set and DMTrackerDetector achieved 90.79% accuracy in the JavaScript test set. MFTrackerDetector eventually generates a list of third-party trackers, which is a combination of Jlist and Flist.

An improved algorithm of the Analytic Hierarchy Process (AHP) is proposed in this paper, which is realized by constructing an improved judgment matrix. Specifically, rough set theory is used in the algorithm to calculate the weight of the network metric data, and then the improved AHP algorithm nine-point systemic is structured, finally, an improved AHP judgment matrix is constructed. By performing an AHP operation on the improved judgment matrix, the weight of the improved network metric data can be obtained. If only the rough set theory is applied to process the network index data, the objective factors would dominate the whole process. If the improved algorithm of AHP is used to integrate the expert score into the process of measurement, then the combination of subjective factors and objective factors can be realized. Based on the aforementioned theory, a new network attack metrics system is proposed in this paper, which uses a metric structure based on "attack type-attack attribute-attack atomic operation-attack metrics", in which the metric process of attack attribute adopts AHP. The metrics of the system are comprehensive, given their judgment of frequent attacks is universal. The experiment was verified by an experiment of a common attack Smurf. The experimental results show the effectiveness and applicability of the proposed measurement system.

The IoT industry has developed rapidly in recent years, which has attracted the attention of security researchers. However, the researchers are hampered by the wide variety of IoT device operating systems and their hardware architectures. Especially for the lightweight IoT devices, many manufacturers do not provide the device firmware images, embedded firmware source code or even the develop documents. As a result, it hinders traditional static analysis and dynamic analysis techniques. In this paper, we propose a novel dynamic analysis framework, called FIoT, which aims at finding memory corruption vulnerabilities in lightweight IoT device firmware images. The key idea is dynamically run the binary code snippets through symbolic execution with carrying out a fuzzing test. Specifically, we generate code snippets through traversing the control-flow graph (CFG) in a backward manner. We improved the CFG recovery approach and backward slice approach for better performance. To reduce the influence of the binary firmware, FIoT leverages loading address determination analysis and library function identification approach. We have implemented a prototype of FIoT and conducted experiments. Our results show that FIoT can complete the Fuzzing test within 40 seconds in average. Considering 170 seconds for static analysis, FIoT can load and analyze a lightweight IoT firmware within 210 seconds in total. Furthermore, we illustrate the effectiveness of FIoT by applying it over 115 firmware images from 17 manufacturers. We have found 35 images exist memory corruptions, which are all zero-day vulnerabilities.

Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.