SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

With the rapid growth of interconnected devices in Industrial and Medical Internet of Things IIoT and MIoT ecosystems, ensuring timely and accurate detection of cyber threats has become a critical challenge. This study presents an advanced intrusion detection framework based on a hybrid...

Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape

The expansion of Internet of Things IoT devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the detection performance. Federated learning settings enabled ...

A Lightweight Defense Mechanism against Next Generation of Phishing Emails Using Distilled Attention-Augmented BiLSTM

The current generation of large language models produces sophisticated social-engineering content that bypasses standard text screening systems in business communication platforms. Our proposed solution for mail gateway and endpoint deception detection operates in a privacy-protective manner whil...

PyTorch torch.lstm_cell memory corruption

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Hybrid Ensemble Method for Detecting Cyber-Attacks in Water Distribution Systems Using the BATADAL Dataset

The cybersecurity of Industrial Control Systems that manage critical infrastructure such as Water Distribution Systems has become increasingly important as digital connectivity expands. BATADAL benchmark data is a good source of testing intrusion detection techniques, but it presents several...

New Machine Learning Approaches for Intrusion Detection in ADS-B

With the growing reliance on the vulnerable Automatic Dependent Surveillance-Broadcast ADS-B protocol in air traffic management ATM, ensuring security is critical. This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems IDS...

EUVD-2020-0217

Malware in sbrugna...

Evaluating Explainable AI for Deep Learning-Based Network Intrusion Detection System Alert Classification

A Network Intrusion Detection System NIDS monitors networks for cyber attacks and other unwanted activities. However, NIDS solutions often generate an overwhelming number of alerts daily, making it challenging for analysts to prioritize high-priority threats. While deep learning models promise to...

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection

The challenges derived from the data-intensive nature of machine learning in conjunction with technologies that enable novel paradigms such as V2X and the potential offered by 5G communication, allow and justify the deployment of Federated Learning FL solutions in the vehicular intrusion detectio...

CVE-2020-26270

In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...

Phishing URL Detection Using Bi-LSTM

Phishing attacks threaten online users, often leading to data breaches, financial losses, and identity theft. Traditional phishing detection systems struggle with high false positive rates and are usually limited by the types of attacks they can identify. This paper proposes a deep learning-based...

The Dark Side of Digital Twins: Adversarial Attacks on AI-Driven Water Forecasting

Digital twins DTs are improving water distribution systems by using real-time data, analytics, and prediction models to optimize operations. This paper presents a DT platform designed for a Spanish water supply network, utilizing Long Short-Term Memory LSTM networks to predict water consumption...

SUSE CVE-2025-3001

A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...

PYSEC-2025-195

A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...

PYSEC-2025-195

A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...

AZL-73180 CVE-2025-3001 affecting package pytorch for versions less than 2.2.2-10

A vulnerability classified as critical was found in PyTorch 2.6.0. This vulnerability affects the function torch.lstmcell. The manipulation leads to memory corruption. The attack needs to be approached locally. The exploit has been disclosed to the public and may be used...

Out-of-bounds Write

Overview torch is a Tensors and Dynamic neural networks in Python with strong GPU acceleration Affected versions of this package are vulnerable to Out-of-bounds Write due to the torch.lstmcell function. An attacker can corrupt memory by manipulating the function's input. Note: This is only...

Out-of-bounds Write

Overview Affected versions of this package are vulnerable to Out-of-bounds Write due to the torch.lstmcell function. An attacker can corrupt memory by manipulating the function's input. Note: This is only exploitable if the attacker has local access to the system. Remediation A fix was pushed int...

PyTorch 缓冲区错误漏洞

PyTorch is a Python package open-sourced by PyTorch. PyTorch has a buffer overflow vulnerability that stems from the failure of the function torch.lstmcell to properly validate the length size of the input data, which can be exploited by an attacker to execute arbitrary code on the system or caus...

BIT-TENSORFLOW-2020-26270 CHECK-fail in LSTM with zero-length input in TensorFlow

In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer...