20,843 research outputs found
ROPocop - Dynamic Mitigation of Code-Reuse Attacks
Control-flow attacks, usually achieved by exploiting a buffer-overflow
vulnerability, have been a serious threat to system security for over fifteen
years. Researchers have answered the threat with various mitigation techniques,
but nevertheless, new exploits that successfully bypass these technologies
still appear on a regular basis.
In this paper, we propose ROPocop, a novel approach for detecting and
preventing the execution of injected code and for mitigating code-reuse attacks
such as return-oriented programming (RoP). ROPocop uses dynamic binary
instrumentation, requiring neither access to source code nor debug symbols or
changes to the operating system. It mitigates attacks by both monitoring the
program counter at potentially dangerous points and by detecting suspicious
program flows.
We have implemented ROPocop for Windows x86 using PIN, a dynamic program
instrumentation framework from Intel. Benchmarks using the SPEC CPU2006 suite
show an average overhead of 2.4x, which is comparable to similar approaches,
which give weaker guarantees. Real-world applications show only an initially
noticeable input lag and no stutter. In our evaluation our tool successfully
detected all 11 of the latest real-world code-reuse exploits, with no false
alarms. Therefore, despite the overhead, it is a viable, temporary solution to
secure critical systems against exploits if a vendor patch is not yet
available
UNCOVERING EVIDENCE OF ATTACKER BEHAVIOR ON THE NETWORK
This comprehensive research presents and investigates a diverse assessment of interruption discovery strategies and their job in contemporary online protection. Interruption Recognition Frameworks are taken apart as vital parts in defending computerized foundations, utilizing different techniques, for example, signature-based, peculiarity based, and heuristic-based identification. While signature-based strategies demonstrate strong against known dangers, the review highlights the urgent job of irregularity-based and heuristic-based approaches in countering novel and complex assaults. Different types attract, their characteristics and behaviors has explored in this paper. The mix of AI and Man-made consciousness (computer based intelligence) in recognizing odd exercises arises as an extraordinary power, empowering versatile reactions to developing digital dangers. The exploration fundamentally breaks down the difficulties looked by existing location strategies, including versatility concerns, high bogus positive rates, and the encryption-related obstacles in rush hour gridlock examination. The outcomes and investigation segment approves the viability of proposed models, including group learning strategies and creative techniques, for example, the Solid Methodology in light of Blockchain and Peculiarity based location (SABA). A Convolutional Brain Organization (CNN) model for interruption location in IoT conditions and a cross breed approach joining positioning based channel strategies and NSGA-II exhibit eminent exactnesses. The review\u27s suggestions for network security are significant, prompting proposals for a TTP-driven approach, mix of conduct peculiarities, persistent security mindfulness preparing, standard red group works out, versatile episode reaction plans, and intermittent security reviews. By and large, the examination contributes a nuanced comprehension of assailant\u27s ways of behaving, down to earth procedures for online protection flexibility, and makes way for future investigation into dynamic danger scenes and the human component in network safety
BUGOPTIMIZE: Bugs dataset Optimization with Majority Vote Cluster-Based Fine-Tuned Feature Selection for Scalable Handling
Software bugs are prevalent in the software development lifecycle, posing challenges to developers in ensuring product quality and reliability. Accurate prediction of bug counts can significantly aid in resource allocation and prioritization of bug-fixing efforts. However, the vast number of attributes in bug datasets often requires effective feature selection techniques to enhance prediction accuracy and scalability. Existing feature selection methods, though diverse, suffer from limitations such as suboptimal feature subsets and lack of scalability. This paper proposes BUGOPTIMIZE, a novel algorithm tailored to address these challenges. BUGOPTIMIZE innovatively integrates majority voting cluster-based fine-tuned feature selection to optimize bug datasets for scalable handling and accurate prediction. The algorithm initiates by clustering the dataset using K-means, EM, and Hierarchical clustering algorithms and performs majority voting to assign data points to final clusters. It then employs filter-based, wrapper-based, and embedded feature selection techniques within each cluster to identify common features. Additionally, feature selection is applied to the entire dataset to extract another set of common features. These selected features are combined to form the final best feature set. Experimental results demonstrate the efficacy of BUGOPTIMIZE compared to existing feature selection methods, reducing MAE and RMSE in Linear Regression (MAE: 0.2668 to 0.2609, RMSE: 0.3251 to 0.308) and Random Forest (MAE: 0.1626 to 0.1341, RMSE: 0.2363 to 0.224), highlighting its significant contribution to bug dataset optimization and prediction accuracy in software development while addressing feature selection limitations. By mitigating the disadvantages of current approaches and introducing a comprehensive and scalable solution, BUGOPTIMIZE presents a significant advancement in bug dataset optimization and prediction accuracy in software development environments
LiveSketch: Query Perturbations for Guided Sketch-based Visual Search
LiveSketch is a novel algorithm for searching large image collections using
hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch
search by creating visual suggestions that augment the query as it is drawn,
making query specification an iterative rather than one-shot process that helps
disambiguate users' search intent. Our technical contributions are: a triplet
convnet architecture that incorporates an RNN based variational autoencoder to
search for images using vector (stroke-based) queries; real-time clustering to
identify likely search intents (and so, targets within the search embedding);
and the use of backpropagation from those targets to perturb the input stroke
sequence, so suggesting alterations to the query in order to guide the search.
We show improvements in accuracy and time-to-task over contemporary baselines
using a 67M image corpus.Comment: Accepted to CVPR 201
Technological requirements for solutions in the conservation and protection of historic monuments and archaeological remains
Executive summary: This Study has discovered many achievements associated with European support for
scientific and technological research for the protection and conservation of cultural
heritage. The achievements to date are:
1. Creation of an active research community
2. A body of research of unparalleled and enviable international quality and character
3. Ongoing effectiveness of research beyond initial funding
4. Substantial rate of publication
5. Imaginative tools of dissemination and publication
6. Clear spin-offs and contribution to European competitiveness often going outside
the European cultural heritage area
7. Contribution to emerging European legislation, for example, air quality
management.
The Study has also uncovered important research gaps associated with this field that have
yet to begin to be investigated. It has also discovered the need for continuing fine scale
advancement in areas where researchers have been active for a number of years. The
overall picture is that European research in the field of cultural heritage protection must be
put on a secure footing if it is to maintain its commanding lead over other regions of the
world.
This Study concludes that:
1. It would be invidious to attempt to separate basic and applied research in this area
of research. Like any other scientific endeavour, this field needs to integrate basic
and applied research if it is to continue to thrive.
2. Small, flexible, focused interdisciplinary teams responsive to European needs, must
be sustained, promoted and celebrated as models of sustainability and that what is
proposed under the European Research Area (ERA) for large and complex
research projects, could inflict serious damage on this area of research.
3. Resources cannot be delegated to Member States because of the interdisciplinary
nature of cultural heritage and the need for a co-ordinated pan-European
perspective across this research that helps to define the essential character of
European cultural heritage. National programmes only serve local needs, leading
to loss of strategic output, lessening of competitiveness and risk of duplication.
4. A mechanism needs to be created to help researchers working in this field to
communicate and exchange information with related sectors such as construction,
urban regeneration, land reclamation and agriculture.
5. There is overwhelming agreement over the need for sustainable research funding
for cultural heritage and for an iterative process of exchange among researchers,
decision-makers and end-users in order to maximize benefits from project
inception through to dissemination, audit and review.
For all the reasons mentioned above, the most significant recommendation in this Report is
the identification of the need for a European Panel on the Application of Science for Cultural Heritage (EPASCH)
Automated Plant Disease Recognition using Tasmanian Devil Optimization with Deep Learning Model
Plant diseases have devastating effects on crop production, contributing to major economic loss and food scarcity. Timely and accurate recognition of plant ailments is vital to effectual disease management and keeping further spread. Plant disease classification utilizing Deep Learning (DL) has gained important attention recently because of its potential to correct and affect the detection of plant diseases. DL approaches, particularly Convolutional Neural Networks (CNNs) demonstrate that extremely effective in capturing intricate patterns and features in plant leaf images, allowing correct disease classification. In this article, a Tasmanian Devil Optimization with Deep Learning Enabled Plant Disease Recognition (TDODL-PDR) technique is proposed for effective crop management. The TDODL-PDR technique derives feature vectors utilizing the Multi-Direction and Location Distribution of Pixels in Trend Structure (MDLDPTS) descriptor. Besides, the deep Bidirectional Long Short-Term Memory (BiLSTM) approach gets exploited for the plant disease recognition. Finally, the TDO method can be executed to optimize the hyperparameters of the BiLSTM approach. The TDO method inspired by the foraging behaviour of Tasmanian Devils (TDs) effectively explores the parameter space and improves the model's performance. The experimental values stated that the TDODL-PDR model successfully distinguishes healthy plants from diseased ones and accurately classifies different disease types. The automated TDODL-PDR model offers a practical and reliable solution for early disease detection in crops, enabling farmers to take prompt actions to mitigate the spread and minimize crop losses
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