Rootkits affect system security by modifying kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously modify noncontrol data. Most prior techniques for rootkit detection have focused solely on detecting control data modifications and, therefore, fail to detect such rootkits. This paper presents a novel technique to detect rootkits that modify both control and noncontrol data. The main idea is to externally observe the execution of the kernel during an inference phase and hypothesize invariants on kernel data structures. A rootkit detection phase uses these invariants as specifications of data structure integrity. During this phase, violation of invariants indicates an infection. We have implemented Gibraltar, a prototype tool that infers kernel data structure invariants and uses them to detect rootkits. Experiments show that Gibraltar can effectively detect previously known rootkits, including those that modify noncontrol data structures.

Malicious users may reprogram cognitive radios to disregard how their actions may harm other users. To cope with such threats, we propose that cognitive radios must have a trusted domain, which contains an onboard regulation mechanism (ORM) that 'regulates' the radio's behavior by monitoring the radio, and taking appropriate action. We present a scheme for monitoring the link quality, in real-time to assess the proper action to take. Our method promptly prevents misbehavior by taking action in a manner proportional to the level by which the cognitive radio deviates from normal, allowed behavior. We demonstrate a method for the ORM to collect the link state information needed to regulate its cognitive radio. Lastly, we examine the resulting algorithms and study our regulation relative to traditional CSMA and Aloha MAC protocols on the ORBIT testbed using USRP/GNU Radios.

Kernel-level rootkits affect system security by modifying key kernel data structures to achieve a variety of malicious goals. While early rootkits modified control data structures, such as the system call table and values of function pointers, recent work has demonstrated rootkits that maliciously modify non-control data. Prior techniques for rootkit detection fail to identify such rootkits either because they focus solely on detecting control data modifications or because they require elaborate, manually-supplied specifications to detect modifications of non-control data. This paper presents a novel rootkit detection technique that automatically detects rootkits that modify both control and non-control data. The key idea is to externally observe the execution of the kernel during a training period and hypothesize invariants on kernel data structures. These invariants are used as specifications of data structure integrity during an enforcement phase; violation of these invariants indicates the presence of a rootkit. We present the design and implementation of Gibraltar, a tool that uses the above approach to infer and enforce invariants. In our experiments, we found that Gibraltar can detect rootkits that modify both control and non-control data structures, and that its false positive rate and monitoring overheads are negligible.

Abstract Vehicularad hocnetworks (VANETs) represent a promising new communication technology that can facilitate many new forms of automotive applications. Many of the applications that will run on these networks will require a high degree of security and privacy. In this paper, we present a robust and efficient security framework for vehicular networks suited for both inter‐vehicular and vehicle‐to‐infrastructure communication. Our system uses identity‐based cryptography (IBC) to provide authentication, confidentiality, non‐repudiation, and message integrity. It exploits the implicit authentication provided by IBC to significantly reduce the security/privacy‐related communication overhead. Additionally, it provides scalable, user‐customizable security and privacy using short‐lived, authenticated, and unforgeable, pseudonyms. This feature can be used by VANET applications that require quantifiable trust and privacy to provide differentiated service based on various levels of trust and privacy thresholds. Copyright © 2008 John Wiley & Sons, Ltd.

The integrity of kernel code and data is fundamental to the integrity of the computer system. Tampering with the kernel data is an attractive venue for rootkit writers since malicious modifications in the kernel are harder to identify compared to their user-level counterparts. So far however, the pattern followed for tampering is limited to hiding malicious objects in user-space. This involves manipulating a subset of kernel data structures that are related to intercepting user requests or affecting the user's view of the system. Hence, defense techniques are built around detecting such hiding behavior. The contribution of this paper is to demonstrate a new class of stealthy attacks that only exist in kernel space and do not employ any hiding techniques traditionally used by rootkits. These attacks are stealthy because the damage done to the system is not apparent to the user or intrusion detection systems installed on the system and are symbolic of a more systemic problem present throughout the kernel. Our goal in building these attack prototypes was to show that such attacks are not only realistic, but worse; they cannot be detected by the current generation of kernel integrity monitors, without prior knowledge of the attack signature.