Electric Vehicle (EV) charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation. Therefore, this paper addresses these challenges by proposing a novel anomaly-resilient federated learning framework that simultaneously preserves data privacy, detects cyber-attacks, and maintains trustworthy demand prediction accuracy under adversarial conditions. The proposed framework integrates three key innovations: LSTM autoencoder-based distributed anomaly detection deployed at each federated client, interpolation-based anomalous data mitigation to preserve temporal continuity, and federated Long Short-Term Memory (LSTM) networks that enable collaborative learning without centralized data aggregation. The framework is validated on real-world EV charging infrastructure datasets combined with real-world DDoS attack datasets, providing robust validation of the proposed approach under realistic threat scenarios. Experimental results demonstrate that the federated approach achieves superior performance compared to centralized models, with 15.2% improvement in R2 accuracy while maintaining data locality. The integrated cyber-attack detection and mitigation system produces trustworthy datasets that enhance prediction reliability, recovering 47.9% of attack-induced performance degradation while maintaining exceptional precision (91.3%) and minimal false positive rates (1.21%). The proposed architecture enables enhanced EV infrastructure planning, privacy-preserving collaborative forecasting, cybersecurity resilience, and rapid recovery from malicious threats across distributed charging networks.

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.

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes various types of DDoS attacks, including volumetric, protocol, and application layer attacks, and discusses the characteristics, impact, and potential targets of each type. It also examines the existing techniques used for DDoS attack detection, such as packet filtering, intrusion detection systems, and machine learning-based approaches, and their strengths and limitations. Moreover, the study explores the prevention techniques employed to mitigate DDoS attacks, such as firewalls, rate limiting , CPP and ELD mechanism. It evaluates the effectiveness of each approach and its suitability for different types of attacks and environments. In conclusion, this study provides a comprehensive overview of the different types of DDoS attacks, their detection, and prevention techniques. It aims to provide insights and guidelines for organizations and individuals to enhance their cybersecurity posture and protect against DDoS attacks.

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.

--In cloud-native systems, Kubernetes clusters with interdependent services often face challenges to their operational resilience due to poor workload management issues such as resource blocking, bottlenecks, or continuous pod crashes. These vulnerabilities are further amplified in adversarial scenarios, such as Distributed Denial-of-Service attacks (DDoS). Conventional Horizontal Pod Autoscaling (HPA) approaches struggle to address such dynamic conditions, while reinforcement learning-based methods, though more adaptable, typically optimize single goals like latency or resource usage, neglecting broader failure scenarios. We propose decomposing the overarching goal of maintaining operational resilience into failure-specific sub-goals delegated to collaborative agents, collectively forming an HPA Multi-Agent System (MAS). We introduce an automated, four-phase online framework for HPA MAS design: 1) modeling a digital twin built from cluster traces; 2) training agents in simulation using roles and missions tailored to failure contexts; 3) analyzing agent behaviors for explainability; and 4) transferring learned policies to the real cluster . Experimental results demonstrate that the generated HPA MASs outperform three state-of-the-art HPA systems in sustaining operational resilience under various adversarial conditions in a proposed complex cluster . Cloud-native critical systems are increasingly reliant on Kubernetes to orchestrate and manage interdependent services [1]. HP A is a widely adopted mechanism to dynamically adjust the number of pods based on resource usage, enabling systems to handle highly dynamic workloads [2]. However, failures such as pod crashes, resource contention, and bottlenecks can severely jeopardize the performance of all of the cluster's functionalities we globally refer to as operational resilience [3]. Worse, these failures may be exploited by attackers to degrade performance or induce outages, as seen in adversarial contexts like DDoS attacks [4].

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any cur rent or future media. Caroline Panggabean Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka carolinepgabean@gmail.com Sincy John Departement of CSE (AIM) JAIN (Deemed - to - be University) Bangalore, Karnataka sincyjohn@jainuniversity.ac.in Chandrasekar Venkatachalam Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka chandrasekar.v@jainuniversity.ac.in Renuka Devi P Departement of CSE (AIML) JAIN (Deemed - to - be University) Bangalore, Karnataka renukadevi.p@jainuniversity.ac.in Priyanka Shah Departement of CSE (AI) JAIN (Deemed - to - be University) Bangalore, Karnataka priyankashah8324@gmail.com Shanmugavalli Venkatachalam Department of CSE KSR College of Engineering Namakkal, Tamil N adu drvshanmugavalli@gmail.com Abstract -- Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing Machine (NTM) for enhanced intrusion detection. Trained on UNSW - NB15 and BoT - IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long - term pattern recognition.

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.