Alcohol and marijuana use while driving-an unexpected crash risk in Pakistani commercial drivers: a cross-sectional survey

Background:A significant proportion of road traffic crashes are attributable to alcohol and marijuana use while driving globally. Sale and use of both substances is illegal in Pakistan and is not considered a threat for road traffic injuries. However literature hints that this may not be the case. We did this study to assess usage of alcohol and marijuana in Pakistani commercial drivers.Methods:A sample of 857 commercial bus and truck drivers was interviewed in October 2008 at the largest commercial vehicle station in Rawalpindi and Islamabad, Pakistan. Time location cluster sampling was used to select the subjects and a structured questionnaire was used to assess the basic demographic profile, substance abuse habits of the drivers while on the road, and reasons for usage of illicit substances while driving were recorded. Self reported information was collected after obtaining informed consent. Chi square and fisher exact tests were used to assess differences between groups and logistic regression was used to identify significant associations between driver characteristics and alcohol and marijuana use.Results:Almost 10% of truck drivers use alcohol while driving on Pakistani roads. Marijuana use is almost 30% in some groups. Statistically different patterns of usage are seen between population subgroups based on age, ethnicity, education, and marital status. Regression analysis shows association of alcohol and marijuana use with road rage and error behaviours, and also with an increased risk of being involved in road crashes. The reported reasons for using alcohol or marijuana show a general lack of awareness of the hazardous nature of this practice among the commercial driver population.Conclusion:Alcohol and marijuana use is highly prevalent in Pakistani commercial drivers. The issue needs to be recognized by concerned authorities and methods such as random breath tests and sobriety check points need to be employed for proper law enforcement

"Nice Boots" - A Large-Scale Analysis of Bootkits and New Ways to Stop Them

Abstract. Bootkits are among the most advanced and persistent tech-nologies used in modern malware. For a deeper insight into their be-havior, we conducted the first large-scale analysis of bootkit technology, covering 2,424 bootkit samples on Windows 7 and XP over the past 8 years. From the analysis, we derive a core set of fundamental properties that hold for all bootkits on these systems and result in abnormalities during the system’s boot process. Based on those abnormalities we de-veloped heuristics allowing us to detect bootkit infections. Moreover, by judiciously blocking the bootkit’s infection and persistence vector, we can prevent bootkit infections in the first place. Furthermore, we present a survey on their evolution and describe how bootkits can evolve in the future

Slick: An Intrusion Detection System for Virtualized Storage Devices

Cloud computing is rapidly reshaping the server administration landscape. The widespread use of virtualization and the increasingly high server consolidation ratios, in particular, have introduced unprecedented security challenges for users, increasing the exposure to intrusions and opening up new opportunities for attacks. Deploying security mechanisms in the hypervisor to detect and stop intrusion attempts is a promising strategy to address this problem. Existing hypervisor-based solutions, however, are typically limited to very specific classes of attacks and introduce exceedingly high performance overhead for production use. In this paper, we present Slick (Storage-Level Intrusion ChecKer), an intrusion detection system (IDS) for virtualized storage devices. Slick detects intrusion attempts by efficiently and transparently monitoring write accesses to critical regions on storage devices. The low-overhead monitoring component operates entirely inside the hypervisor, with no introspection or modifications required in the guest VMs. Using Slick, users can deploy generic IDS rules to detect a broad range of real-world intrusions in a flexible and practical way. Experimental results confirm that Slick is effective at enhancing the security of virtualized servers, while imposing less than 5% overhead in production

System-level support for intrusion recovery

Recovering from attacks is hard and gets harder as the time between the initial infection and its detection increases. Which files did the attackers modify? Did any of user data depend on malicious inputs? Can I still trust my own documents or binaries? When malcode has been active for some time and its actions are mixed with those of benign applications, these questions are impossible to answer on current systems. In this paper, we describe DiskDuster, an attack analysis and recovery system capable of recovering from complicated attacks in a semi-automated manner. DiskDuster traces malcode at byte-level granularity both in memory and on disk in a modified version of QEMU. Using taint analysis, DiskDuster also tracks all bytes written by the malcode, to provide a detailed view on what (bytes in) files derive from malicious data. Next, it uses this information to remove malicious actions at recovery time