Integrity-based kernel malware detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. ^ We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. ^ We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. ^ We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). ^ In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.^

Integrity-Based Kernel Malware Detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.

Dynalog: an automated dynamic analysis framework for characterizing android applications

Android is becoming ubiquitous and currently has the largest share of the mobile OS market with billions of application downloads from the official app market. It has also become the platform most targeted by mobile malware that are becoming more sophisticated to evade state-of-the-art detection approaches. Many Android malware families employ obfuscation techniques in order to avoid detection and this may defeat static analysis based approaches. Dynamic analysis on the other hand may be used to overcome this limitation. Hence in this paper we propose DynaLog, a dynamic analysis based framework for characterizing Android applications. The framework provides the capability to analyse the behaviour of applications based on an extensive number of dynamic features. It provides an automated platform for mass analysis and characterization of apps that is useful for quickly identifying and isolating malicious applications. The DynaLog framework leverages existing open source tools to extract and log high level behaviours, API calls, and critical events that can be used to explore the characteristics of an application, thus providing an extensible dynamic analysis platform for detecting Android malware. DynaLog is evaluated using real malware samples and clean applications demonstrating its capabilities for effective analysis and detection of malicious applications.

Semantic analysis in location privacy preserving

With the increasing use of location-based services, location privacy has recently started raising serious concerns. Location perturbation and obfuscation are most widely used for location privacy preserving. To protect a user from being identified, a cloaked spatial region that contains other k - 1 nearest neighbors of the user is submitted to the location-based service provider, instead of the accurate position. In this paper, we consider the location-aware applications that services are different among regions. In such scenarios, the semantic distance between users should be considered besides the Euclidean distance for searching the neighbors of a user. We define a novel distance measurement that combines the semantic and the Euclidean distance to address the privacy-preserving issue in the aforementioned applications. We also present an algorithm kNNH to implement our proposed method. Moreover, we conduct performance study experiments on the proposed algorithm. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users' privacy.

Time-series pattern based effective noise generation for privacy protection on cloud

Cloud computing is proposed as an open and promising computing paradigm where customers can deploy and utilize IT services in a pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness and virtualization, various malicious service providers may exist in these cloud environments, and some of them may record service data from a customer and then collectively deduce the customer's private information without permission. Therefore, from the perspective of cloud customers, it is essential to take certain technical actions to protect their privacy at client side. Noise obfuscation is an effective approach in this regard by utilizing noise data. For instance, noise service requests can be generated and injected into real customer service requests so that malicious service providers would not be able to distinguish which requests are real ones if these requests' occurrence probabilities are about the same, and consequently related customer privacy can be protected. Currently, existing representative noise generation strategies have not considered possible fluctuations of occurrence probabilities. In this case, the probability fluctuation could not be concealed by existing noise generation strategies, and it is a serious risk for the customer's privacy. To address this probability fluctuation privacy risk, we systematically develop a novel time-series pattern based noise generation strategy for privacy protection on cloud. First, we analyze this privacy risk and present a novel cluster based algorithm to generate time intervals dynamically. Then, based on these time intervals, we investigate corresponding probability fluctuations and propose a novel time-series pattern based forecasting algorithm. Lastly, based on the forecasting algorithm, our novel noise generation strategy can be presented to withstand the probability fluctuation privacy risk. The simulation evaluation demonstrates that our strategy can significantly improve the effectiveness of such cloud privacy protection to withstand the probability fluctuation privacy risk.

Survey in smartphone malware analysis techniques

Smartphone Malware continues to be a serious threat in today's world. Recent research studies investigate the impacts of new malware variant. Historically traditional anti-malware analyses rely on the signatures of predefined malware samples. However, this technique is not resistant against the obfuscation techniques (e.g. polymorphic and metamorphic). While the permission system proposed by Google, requires smartphone users to pay attention to the permission description during the installation time. Nevertheless, normal users cannot comprehend the semantics of Android permissions. This chapter surveys various approaches used in Smartphone malware detection and Investigates weaknesses of existing countermeasures such as signature-based and anomaly-based detection.

Secure and Privacy-aware Data Collection and Processing in Mobile Health Systems

Healthcare systems have assimilated information and communication technologies in order to improve the quality of healthcare and patient's experience at reduced costs. The increasing digitalization of people's health information raises however new threats regarding information security and privacy. Accidental or deliberate data breaches of health data may lead to societal pressures, embarrassment and discrimination. Information security and privacy are paramount to achieve high quality healthcare services, and further, to not harm individuals when providing care. With that in mind, we give special attention to the category of Mobile Health (mHealth) systems. That is, the use of mobile devices (e.g., mobile phones, sensors, PDAs) to support medical and public health. Such systems, have been particularly successful in developing countries, taking advantage of the flourishing mobile market and the need to expand the coverage of primary healthcare programs. Many mHealth initiatives, however, fail to address security and privacy issues. This, coupled with the lack of specific legislation for privacy and data protection in these countries, increases the risk of harm to individuals. The overall objective of this thesis is to enhance knowledge regarding the design of security and privacy technologies for mHealth systems. In particular, we deal with mHealth Data Collection Systems (MDCSs), which consists of mobile devices for collecting and reporting health-related data, replacing paper-based approaches for health surveys and surveillance. This thesis consists of publications contributing to mHealth security and privacy in various ways: with a comprehensive literature review about mHealth in Brazil; with the design of a security framework for MDCSs (SecourHealth); with the design of a MDCS (GeoHealth); with the design of Privacy Impact Assessment template for MDCSs; and with the study of ontology-based obfuscation and anonymisation functions for health data.

A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression

Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti-virus engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti-virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression-based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper-based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute-force search strategies and order of (m-1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m-1). The proposed approach uses multi-linear regression and the p-value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi-collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine-based template approach with complete data and provides a better statistical fitness.