NERD: Neural Network for Edict of Risky Data Streams

• URL: http://arxiv.org/abs/2007.07753v1
• Date: Wed, 8 Jul 2020 14:24:48 GMT
• Title: NERD: Neural Network for Edict of Risky Data Streams
• Authors: Sandro Passarelli, Cem G\"undogan, Lars Stiemert, Matthias Schopp, Peter Hillmann
• Abstract summary: Cyber incidents can have a wide range of cause from a simple connection loss to an insistent attack. The developed system is enriched with information by multiple sources such as intrusion detection systems and monitoring tools. It uses over twenty key attributes like sync-package ratio to identify potential security incidents and to classify the data into different priority categories.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cyber incidents can have a wide range of cause from a simple connection loss to an insistent attack. Once a potential cyber security incidents and system failures have been identified, deciding how to proceed is often complex. Especially, if the real cause is not directly in detail determinable. Therefore, we developed the concept of a Cyber Incident Handling Support System. The developed system is enriched with information by multiple sources such as intrusion detection systems and monitoring tools. It uses over twenty key attributes like sync-package ratio to identify potential security incidents and to classify the data into different priority categories. Afterwards, the system uses artificial intelligence to support the further decision-making process and to generate corresponding reports to brief the Board of Directors. Originating from this information, appropriate and detailed suggestions are made regarding the causes and troubleshooting measures. Feedback from users regarding the problem solutions are included into future decision-making by using labelled flow data as input for the learning process. The prototype shows that the decision making can be sustainably improved and the Cyber Incident Handling process becomes much more effective.

Related papers

• TabSec: A Collaborative Framework for Novel Insider Threat Detection [8.27921273043059]
  In the era of the Internet of Things (IoT) and data sharing, users frequently upload their personal information to enterprise databases to enjoy enhanced service experiences. However, the widespread presence of system vulnerabilities, remote network intrusions, and insider threats significantly increases the exposure of private enterprise data on the internet. This paper proposes a novel threat detection framework, TabITD, to address these challenges.
  arXiv  Detail & Related papers  (2024-11-04T04:07:16Z)
• Robustness Verification for Knowledge-Based Logic of Risky Driving Scenes [8.388107085036571]
  We collect 72 accident datasets from Data.gov and organize them by state. We train Decision Tree and XGBoost models on each state's dataset, deriving accident judgment logic.
  arXiv  Detail & Related papers  (2023-12-27T00:13:51Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• A robust statistical framework for cyber-vulnerability prioritisation under partial information in threat intelligence [0.0]
  This work introduces a robust statistical framework for quantitative and qualitative reasoning under uncertainty about cyber-vulnerabilities. We identify a novel accuracy measure suited for rank in variance under partial knowledge of the whole set of existing vulnerabilities. We discuss the implications of partial knowledge about cyber-vulnerabilities on threat intelligence and decision-making in operational scenarios.
  arXiv  Detail & Related papers  (2023-02-16T15:05:43Z)
• Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks [107.8565143456161]
  We investigate the information processing strategies adopted by simple artificial neural networks performing a variety of cognitive tasks. Results show that synergy increases as neural networks learn multiple diverse tasks. randomly turning off neurons during training through dropout increases network redundancy, corresponding to an increase in robustness.
  arXiv  Detail & Related papers  (2022-10-06T15:36:27Z)
• Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities [0.0]
  Intrusion Detection Systems (IDS) have received widespread adoption due to their ability to handle vast amounts of data with a high prediction accuracy. IDSs designed using Deep Learning (DL) techniques are often treated as black box models and do not provide a justification for their predictions. This survey reviews the state-of-the-art in explainable AI (XAI) for IDS, its current challenges, and discusses how these challenges span to the design of an X-IDS.
  arXiv  Detail & Related papers  (2022-07-13T14:31:46Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• Multi-Source Data Fusion for Cyberattack Detection in Power Systems [1.8914160585516038]
  We show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle attacks.
  arXiv  Detail & Related papers  (2021-01-18T06:34:45Z)
• Bias in Multimodal AI: Testbed for Fair Automatic Recruitment [73.85525896663371]
  We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.
  arXiv  Detail & Related papers  (2020-04-15T15:58:05Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.