A Basis For Intrusion Detection In Distributed Systems Using Kernel Level Data Tainting
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A Basis for Intrusion Detection in Distributed Systems Using Kernel-level Data Tainting
Modern organisations rely intensively on information and communicationtechnology infrastructures. Such infrastructures offer a range of servicesfrom simple mail transport agents or blogs to complex e-commerce platforms,banking systems or service hosting, and all of these depend on distributedsystems. The security of these systems, with their increasing complexity, isa challenge. Cloud services are replacing traditional infrastructures byproviding lower cost alternatives for storage and computational power, butat the risk of relying on third party companies. This risk becomesparticularly critical when such services are used to host privileged companyinformation and applications, or customers' private information. Even in thecase where companies host their own information and applications, the adventof BYOD (Bring Your Own Device) leads to new security relatedissues.In response, our research investigated the characterization and detection ofmalicious activities at the operating system level and in distributedsystems composed of multiple hosts and services. We have shown thatintrusions in an operating system spawn abnormal information flows, and wedeveloped a model of dynamic information flow tracking, based on taintmarking techniques, in order to detect such abnormal behavior. We trackinformation flows between objects of the operating system (such as files,sockets, shared memory, processes, etc.) and network packetsflowing between hosts. This approach follows the anomaly detection paradigm.We specify the legal behavior of the system with respect to an informationflow policy, by stating how users and programs from groups of hosts areallowed to access or alter each other's information. Illegal informationflows are considered as intrusion symptoms. We have implemented this modelin the Linux kernel (the source code is availableat http://www.blare-ids.org), as a Linux Security Module (LSM), andwe used it as the basis for practical demonstrations. The experimentalresults validated the feasibility of our new intrusion detection principles.
Android Malware
Author: Xuxian Jiang
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-06-13
Mobile devices, such as smart phones, have achieved computing and networking capabilities comparable to traditional personal computers. Their successful consumerization has also become a source of pain for adopting users and organizations. In particular, the widespread presence of information-stealing applications and other types of mobile malware raises substantial security and privacy concerns. Android Malware presents a systematic view on state-of-the-art mobile malware that targets the popular Android mobile platform. Covering key topics like the Android malware history, malware behavior and classification, as well as, possible defense techniques.
Malware Detection
Author: Mihai Christodorescu
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-03-06
This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.