Plus: The SEC lets SolarWinds off the hook, Microsoft stops a historic DDoS attack, and FBI documents reveal the agency spied on an immigration activist Signal group in New York City. Eight years after a researcher warned WhatsApp that it was possible to extract user phone numbers en masse from the Meta-owned app, another team of researchers found that they could still do exactly that using a similar technique. The issue stems from WhatsApp's discovery feature, which allows someone to enter a person's phone number to see if they're on the app. By doing this billions of times--which WhatsApp did not prevent--researchers from the University of Vienna uncovered what they're calling "the most extensive exposure of phone numbers" ever . Vaping is a major problem in US high schools.

Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i.e., survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community.

Vehicular Ad Hoc Networks (VANETs) play a key role in Intelligent Transportation Systems (ITS), particularly in enabling real-time communication for emergency vehicles. However, Distributed Denial of Service (DDoS) attacks, which interfere with safety-critical communication channels, can severely impair their reliability. This study introduces a robust and scalable framework to detect DDoS attacks in highway-based VANET environments. A synthetic dataset was constructed using Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban Mobility (SUMO) and further enriched with real-world mobility traces from Germany's A81 highway, extracted via OpenStreetMap (OSM). Three traffic categories were simulated: DDoS, VoIP, and TCP-based video streaming (VideoTCP). The data preprocessing pipeline included normalization, signal-to-noise ratio (SNR) feature engineering, missing value imputation, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Feature importance was assessed using SHapley Additive exPlanations (SHAP). Eleven classifiers were benchmarked, among them XGBoost (XGB), CatBoost (CB), AdaBoost (AB), GradientBoosting (GB), and an Artificial Neural Network (ANN). XGB and CB achieved the best performance, each attaining an F1-score of 96%. These results highlight the robustness of the proposed framework and its potential for real-time deployment in VANETs to secure critical emergency communications.

This paper forecasts future Distributed Denial - of - Service (DDoS) attacks us ing deep learning models. Although several studies address forecasting DDoS attacks, they remain relatively limited compared to detection - focused research . By studying the current trends and forecasting based on newer and updated datasets, mitigation plans against the attacks can be planned and formulated. The methodology used in this research work conforms to the Cross Industry Standard Process for Data Mining (CRISP - DM) model. Leveraging cyberattack data from the COVID - 19 period (2019 - 2020), sourced from Digital Attack Map and compiled by Arbor Networks, the study aims to identify recent attack trends and forecast future activity to support proactive mitigation strategies. The dataset was examined using statistical analysis techniques to identify prevailing patterns, with emphasis on the frequency of attac ks, the duration of attack instances, and the maximum throughput recorded during each incident . Compared to other deep learning models, the LSTM model is proposed for its ability to learn long - term temporal patterns in evolving DDoS traffic. The performanc e of LSTM model was evaluated using Mean Squared Error (MSE) under varying neuron counts and window sizes. While the model demonstrated limited predictive accuracy in terms of absolute values, the visual comparison between the predicted and actual data usi ng line charts revealed close alignment in trend patterns . This suggests that the model captures the underlying temporal dynamics of the data, thereby providing a promising foundation for future model optimization and performance enhancement. Many cyberattack methods are well known, including but not limited to phishing, spoofing, malware infections, ransomware, and Denial - of - Service (DoS) attacks. A DoS attack occurs when an attacker attempts to disable a service, server, or network . Attackers attempt to make services inaccessible by overwhelming the available resources on the hosting server, infrastructure and/or systems. However, DoS can be eas ily track ed, as it could contai n information about the attacker that can be obtained from network traces and attack logs.

One of the most difficult challenges in cybersecurity is eliminating Distributed Denial of Service (DDoS) attacks. Automating this task using artificial intelligence is a complex process due to the inherent class imbalance and lack of sufficient labeled samples of real-world datasets. This research investigates the use of Semi-Supervised Learning (SSL) techniques to improve DDoS attack detection when data is imbalanced and partially labeled. In this process, 13 state-of-the-art SSL algorithms are evaluated for detecting DDoS attacks in several scenarios. We evaluate their practical efficacy and shortcomings, including the extent to which they work in extreme environments. The results will offer insight into designing intelligent Intrusion Detection Systems (IDSs) that are robust against class imbalance and handle partially labeled data.

The distributed denial-of-service (DDoS) attack stands out as a highly formidable cyber threat, representing an advanced form of the denial-of-service (DoS) attack. A DDoS attack involves multiple computers working together to overwhelm a system, making it unavailable. On the other hand, a DoS attack is a one-on-one attempt to make a system or website inaccessible. Thus, it is crucial to construct an effective model for identifying various DDoS incidents. Although extensive research has focused on binary detection models for DDoS identification, they face challenges to adapt evolving threats, necessitating frequent updates. Whereas multiclass detection models offer a comprehensive defense against diverse DDoS attacks, ensuring adaptability in the ever-changing cyber threat landscape. In this paper, we propose a Hybrid Model to strengthen network security by combining the featureextraction abilities of 1D Convolutional Neural Networks (CNNs) with the classification skills of Random Forest (RF) and Multi-layer Perceptron (MLP) classifiers. Using the CIC-DDoS2019 dataset, we perform multiclass classification of various DDoS attacks and conduct a comparative analysis of evaluation metrics for RF, MLP, and our proposed Hybrid Model. After analyzing the results, we draw meaningful conclusions and confirm the superiority of our Hybrid Model by performing thorough cross-validation. Additionally, we integrate our machine learning model with Snort, which provides a robust and adaptive solution for detecting and mitigating various DDoS attacks.

As one of the few Chain-of-Thought (CoT) reasoning models--and notably the first open-source implementation of its kind--DeepSeek-R1 has demonstrated remarkable improvements in the performance of complex reasoning tasks. Experimental results show that DeepSeek-R1 not only achieves CoT reasoning but also significantly reduces computational resource requirements [1]. Furthermore, it has outperformed comparable models, such as ChatGPT-o1, in certain benchmark tests, showcasing exceptional performance advantages. However, while the CoT approach significantly enhances reasoning capabilities, it also brings forth security concerns that warrant attention. Due to the influence of scaling laws, the volume of data used during the training of LLMs has reached unprecedented levels. Although extensive methods have been employed to sanitize the data during collection and filtering [2], technical limitations and resource constraints have resulted in a considerable amount of harmful content remaining in the training data.

Distributed Denial of Service attacks have become a significant threat to industries and governments leading to substantial financial losses. With the growing reliance on internet services, DDoS attacks can disrupt services by overwhelming servers with false traffic causing downtime and data breaches. Although various detection techniques exist, selecting an effective method remains challenging due to trade-offs between time efficiency and accuracy. This research focuses on developing an efficient real-time DDoS detection system using machine learning algorithms leveraging the UNB CICDDoS2019 dataset including various traffic features. The study aims to classify DDoS and non-DDoS traffic through various ML classifiers including Logistic Regression, K-Nearest Neighbors, Random Forest, Support Vector Machine, Naive Bayes. The dataset is preprocessed through data cleaning, standardization and feature selection techniques using Principal Component Analysis. The research explores the performance of these algorithms in terms of precision, recall and F1-score as well as time complexity to create a reliable system capable of real-time detection and mitigation of DDoS attacks. The findings indicate that RF, AdaBoost and XGBoost outperform other algorithms in accuracy and efficiency, making them ideal candidates for real-time applications.

This study focuses on a method for detecting and classifying distributed denial of service (DDoS) attacks, such as SYN Flooding, ACK Flooding, HTTP Flooding, and UDP Flooding, using neural networks. Machine learning, particularly neural networks, is highly effective in detecting malicious traffic. A dataset containing normal traffic and various DDoS attacks was used to train a neural network model with a 24-106-5 architecture. The model achieved high Accuracy (99.35%), Precision (99.32%), Recall (99.54%), and F-score (0.99) in the classification task. All major attack types were correctly identified. The model was also further tested in the lab using virtual infrastructures to generate normal and DDoS traffic. The results showed that the model can accurately classify attacks under near-real-world conditions, demonstrating 95.05% accuracy and balanced F-score scores for all attack types. This confirms that neural networks are an effective tool for detecting DDoS attacks in modern information security systems.

Distributed Denial of Service (DDoS) attacks are a major concern in network security, as they overwhelm systems with excessive traffic, compromise sensitive data, and disrupt network services. Accurately detecting these attacks is crucial to protecting network infrastructure. Traditional approaches, such as single Convolutional Neural Networks (CNNs) or conventional Machine Learning (ML) algorithms like Decision Trees (DTs) and Support Vector Machines (SVMs), struggle to extract the diverse features needed for precise classification, resulting in suboptimal performance. This research addresses this gap by introducing a novel approach for DDoS attack detection. The proposed method combines three distinct CNN architectures: SA-Enabled CNN with XGBoost, SA-Enabled CNN with LSTM, and SA-Enabled CNN with Random Forest. Each model extracts features at multiple scales, while self-attention mechanisms enhance feature integration and relevance. The weighted ensemble approach ensures that both prominent and subtle features contribute to the final classification, improving adaptability to evolving attack patterns and novel threats. The proposed method achieves a precision of 98.71%, an F1-score of 98.66%, a recall of 98.63%, and an accuracy of 98.69%, outperforming traditional methods and setting a new benchmark in DDoS attack detection. This innovative approach addresses critical limitations in current models and advances the state of the art in network security.