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Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. We propose a novel approach where packets are stacked into windows and separately recognised, with a 2D image representation suitable for processing with computer vision models. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.
Microsoft's AI Red Team employs a hacker's mindset to identify and mitigate potential generative AI risks, combining cybersecurity and societal-harm assessments. (Read More)
Berlin, 24. Juli 2024Die NIS2-Richtlinie der EU soll angesichts einer zunehmenden Bedrohung durch Cyberattacken die Cybersicherheit der europäischen Wirtschaft stärken und auf ein einheitlich hohes Niveau
USA News Group Commentary Issued on behalf of Scope AI Corp. VANCOUVER, BC, July 24, 2024 /PRNewswire/ -- USA News Group - The telecom market just witnessed a security breach nightmare
In an era where the internet connects virtually every aspect of our lives, the security of information systems has become paramount. Safeguarding critical databases containing private and commercial
Homomorphic encryption is one of the most secure solutions for processing sensitive information in untrusted environments, and there have been many recent advances toward its efficient implementation for the evaluation of approximated arithmetic as well as linear and arbitrary functions. The TFHE scheme [Chillotti et al., 2016] is the current state-of-the-art for the evaluation of arbitrary functions, and, in this work, we focus on improving its performance. We divide this paper into two parts. First, we review and implement the main techniques to improve performance or error behavior in TFHE proposed so far. Then, we introduce novel improvements to several of them and new approaches to implement some commonly used procedures. We also show which proposals can be suitably combined to achieve better results. We provide a single library containing all the reviewed techniques as well as our original contributions. Among the techniques we introduce, we highlight a new method for multi-value bootstrapping based on blind rotation unfolding and a faster-than-memory seed expansion, which introduces speedups of up to 2 times to basic arithmetic operations.
Die EU-weite Netzwerks- und Informationssicherheitsrichtlinie und ihre österreichische Umsetzung verpflichten zur Absicherung der IT-Lieferkette
It was earlier reported that Google's parent company Alphabet was in advanced talks to acquire the firm. Wiz CEO: 'While we are flattered by offers we have received, we have chosen to continue on our path to building Wiz'
The implementation of cryptographic authentication in medical and healthcare products is critical to patient health and privacy, as well as user security, and applies to a wide range of different diagnostic and monitoring equipment Read the article complete on EO519The post The importance of cryptographic authentication in medical devices appeared first on Elettronica Plus.
The rapid advancement of quantum computing poses a significant threat to manycurrent security algorithms used for secure communication, digitalauthentication, and information encryption. A sufficiently powerful quantumcomputer could potentially exploit vulnerabilities in these algorithms,rendering data in transit insecure. This threat is expected to materializewithin the next 20 years. Immediate transition to quantum-resilientcryptographic schemes is crucial, primarily to mitigate store-now-decrypt-laterattacks and to ensure the security of products with decade-long operationallives. This transition requires a systematic approach to identifying andupgrading vulnerable cryptographic implementations. This work developed aquantum assessment tool for organizations, providing tailored recommendationsfor transitioning their security protocols into a post-quantum world. The workincluded a systematic evaluation of the proposed solution using qualitativefeedback from network administrators and cybersecurity experts. This feedbackwas used to refine the accuracy and usability of the assessment process. Theresults demonstrate its effectiveness and usefulness in helping organizationsprepare for quantum computing threats. The assessment tool is publiclyavailable at (
IoT and quantum computing promises transformative advancements in various technological domains, including bolstering IoT security protocols.
Nachricht: IBM Aktie: Cybersicherheit und KI-Fokus treiben Wachstum - 18.07.24 - News
(Bild: Dall-E / KI-generiert) Die Comforte AG bietet innovative, quantenresistente Tokenisierungs- und Verschlüsselungsfunktionen für hohe Datensicherheit in Unternehmen.
Wireless sensor networks aim to collect environmental data for monitoring and decision-making purposes, often relying on low-power sensor nodes with limited computational resources, which makes it challenging to secure these networks using costly cryptographic primitives. Moreover, the emergence of quantum computers threatens traditional cryptographic schemes, and postquantum cryptographic schemes have been proposed as a solution. This work focuses on studying the behavior and performance of different combinations of postquantum digital signatures and key exchange mechanisms in wireless sensor networks where the number of nodes is large, including CRYSTALS-Dilithium, Falcon, SPHINCS+, CRYSTALS-Kyber, NTRU, and Saber, with a focus on their interaction and impact on network scalability. Simulation models are employed to generate metrics related to network functionality, application quality, and scalability with dynamic node behavior. The findings provide insights into the behavior of different combinations of postquantum schemes in wireless sensor networks and contribute to understanding their suitability and potential challenges in real-world deployments. In particular, the combination of Falcon and CRYSTALS-Kyber seems to be the most promising candidate for deploying secure sensor networks in the future. However, other combinations can present a better performance depending on their interactions with the parameters of the final application.
Eine für Intel Prozessor herausgegebene Sicherheitswarnung hat vom BSI ein Update erhalten. Wie sich betroffene Anwender verhalten sollten, erfahren Sie hier.
The new security classification is valid for three years.
Die Verbreitung Ransomware-as-a-Service und die starke Zunahme von Supply-Chain-Angriffen im vergangenen Jahr führen laut Cyber Security Report von CANCOM zu einer erhöhten Anzahl und gesteigerten Raffinesse von Cyberangriffen. Im Report betonen IT-Security-Experten die Dringlichkeit verbesserter Schutzmaßnahmen und erläutern konkrete Strategien zur Bekämpfung dieser Bedrohungen.
ALBAWABA - Google is closing close on what may be its biggest acquisition to date: the purchase of an Israeli cybersecurity startup for up to $23 billio
(Bild: frei lizenziert) Erfahren Sie, wie Large Language Models die Cyberkriminalität beeinflussen können und welche Risiken sie mit sich bringen. Lesen Sie mehr über die Nutzung von KI in der Cyberwelt.
Fast ein Drittel (31 Prozent) der OT-Unternehmen meldete im vergangenen Jahr mehr als sechs Sicherheitsvorfälle, gegenüber elf Prozent im Vorjahr.
The aim is to integrate commercial equipment into military space operations, including satellites and other hardware.
In response to the escalating frequency and complexity of cyber threats, the imperative need to enhance cybersecurity measures is evident. This study explores the potential of machine learning (ML) algorithms in advancing threat detection and classification by automating the identification of security incidents. The abstract presents a thorough assessment of various ML algorithms, including decision trees, support vector machines, and neural networks, for their efficacy in detecting and categorizing cyber threats. The evaluation encompasses a diverse dataset featuring different cyber-attack scenarios and incorporates multiple features such as network traffic patterns, system logs, and user behavior. Performance metrics, such as training accuracy and testing accuracy, are employed to assess the effectiveness of each algorithm. Furthermore, the study investigates the impact of feature selection techniques and model optimization strategies on algorithm performance. The results underscore the capability of ML algorithms to accurately identify and categorize cyber threats, providing valuable insights into their strengths and limitations. This research contributes to the field of cybersecurity by facilitating the development of practical and robust ML-based solutions, ultimately reinforcing cyber defence mechanisms against evolving threats.