Improving I/O performance through an in-kernel disk simulator

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.Peer ReviewedPostprint (author's final draft

Space-Efficient Predictive Block Management

With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes

myTrustedCloud: Trusted cloud infrastructure for security-critical computation and data managment

Copyright @ 2012 IEEECloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure - Eucalyptus - allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications

Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools

The majority of Ethical Hacking (EH) tools utilised in penetration testing are developed by practitioners within the industry or underground communities. Similarly, academic researchers have also contributed to developing security tools. However, there appears to be limited awareness among practitioners of academic contributions in this domain, creating a significant gap between industry and academia’s contributions to EH tools. This research paper aims to survey the current state of EH academic research, primarily focusing on research-informed security tools. We categorise these tools into process-based frameworks (such as PTES and Mitre ATT&CK) and knowledge-based frameworks (such as CyBOK and ACM CCS). This classification provides a comprehensive overview of novel, research-informed tools, considering their functionality and application areas. The analysis covers licensing, release dates, source code availability, development activity, and peer review status, providing valuable insights into the current state of research in this field

Do you Trust your Device? Open Challenges in IoT Security Analysis

Several critical contexts, such as healthcare, smart cities, drones, transportation, and agriculture, nowadays rely on IoT, or more in general embedded, devices that require comprehensive security analysis to ensure their integrity before deployment. Security concerns are often related to vulnerabilities that result from inad- equate coding or undocumented features that may create significant privacy issues for users and companies. Current analysis methods, albeit dependent on complex tools, may lead to superficial assessments due to compatibility issues, while authoritative entities struggle with specifying feasible firmware analysis requests for manufacturers within operational contexts. This paper urges the scientific community to collaborate with stakeholders—manufacturers, vendors, security analysts, and experts—to forge a cooperative model that clari- fies manufacturer contributions and aligns analysis demands with operational constraints. Aiming at a modular approach, this paper highlights the crucial need to refine security analysis, ensuring more precise requirements, balanced expectations, and stronger partnerships between vendors and analysts. To achieve this, we propose a threat model based on the feasible interactions of actors involved in the security evaluation of a device, with a particular emphasis on the responsibilities and necessities of all entities involved

Automatic Discovery and Patching of Buffer and Integer Overflow Errors

We present Targeted Automatic Patching (TAP), an automatic buffer and integer overflow discovery and patching system. Starting with an application and a seed input that the application processes correctly, TAP dynamically analyzes the execution of the application to locate target memory allocation sites and statements that access dynamically or statically allocated blocks of memory. It then uses targeted error-discovery techniques to automatically generate inputs that trigger integer and/or buffer overflows at the target sites. When it discovers a buffer or integer overflow error, TAP automatically matches and applies patch templates to generate patches that eliminate the error. Our experimental results show that TAP successfully discovers and patches two buffer and six integer overflow errors in six real-world applications