The rapid usage of the Internet for the last few decades has lead to the deployment of high-speed networks in commercial and educational institutions. As network traffic is increasing, security challenges are also increasing in the high-speed network. Although the Intrusion Detection System (IDS) has a significant role in spotting potential attacks, the heavy traffic flow causes severe technical challenges relating to monitoring and detecting the network activities. Moreover, the devastating nature of the Distributed Denial-of-Service (DDoS) attack draws out as a significant cyber-attack regardless of the emergence of Software Defined Network (SDN) architecture. This paper proposes a novel framework to address the performance issues of IDS and the design issues of SDN about DDoS attacks by incorporating intelligence in the data layer using Data Plane Development Kit (DPDK) in the SDN architecture. This novel framework is named as DPDK based DDoS Detection (D3) framework, since DPDK provides fast packet processing and monitoring in the data plane. Moreover, the statistical anomaly detection algorithm implemented in the data plane as Virtual Network Function (VNF) using DPDK offers fast detection of DDoS attacks. The experimental results of the D3 framework guarantee both efficiency and effect of the novel IDS framework. The publicly available CIC DoS datasets also ensure the detection effect of a single statistical anomaly detection algorithm against the DDoS attack.

With the rise in cybercrime, phishing remains a significant concern as it targets individuals with fake websites, causing victims to disclose their private information. The effective implementation of phishing detection relies on cost efficiency, with the increased feature extraction factor contributing to these costs. This research analyzes a dataset containing 58,645 URLs, examining 111 features of the latest phishing websites dataset to identify the differences between phishing sites and legitimate sites. Astonishingly, using only 14 characteristics, the feedforward model achieved a remarkable accuracy of 94.46%, confirming the efficiency of Machine Learning in phishing detection. Through the exploitation of a multiple classifier collection, including Deep Neural Network (DNN), Wide and Deep, and TabNet, this research advances ongoing efforts to improve the accuracy and efficiency of phishing detection mechanisms and enhance cybersecurity defenses against malicious activities. The methodology introduces a new metric called the ’anti-phishing score,’ which evaluates performance based on false positives and negatives, beyond traditional model accuracy. The model was trained through a robust design of extensive experimentation and hyperparameter-sensitive grid search, ensuring an optimized configuration for phishing detection. Furthermore, the trained model was validated on a new dataset to evaluate its generalizability, enhancing its practical applicability. Through the integration of feature selection principles, advanced algorithmic techniques, and comprehensive evaluation approaches, this research offers a robust approach to phishing detection, considering the evolving nature of cyber threats. The findings provide a beneficial framework for cybersecurity specialists and researchers, enabling more effective preventive measures against phishing attacks.

Real-time global event detection particularly catastrophic events has benefited significantly due to the ubiquitous adoption of social media platforms and advancements in image classification and natural language processing. Social media is a rich repository of multimedia content during disasters, encompassing reports on casualties, infrastructure damage, and information about missing individuals. While previous research has predominantly concentrated on textual or image analysis, the proposed study presents a multimodal middle fusion paradigm that includes Cross-modal attention and Self-attention to improve learning from both image and text modalities. Through rigorous experimentation, we validate the effectiveness of our proposed middle fusion paradigm in leveraging complementary information from both textual and visual sources.The proposed intermediate design outperforms current late and early fusion structures, achieving an accuracy of 91.53% and 91.07% in the informativeness and disaster type recognition categories, respectively. This study is among the few that examine all three tasks in the CrisisMMD dataset by combining textual and image analysis, demonstrating an approximate improvement of about 2% in prediction accuracy compared to similar studies on the same dataset.Additionally, ablation studies indicate that it outperforms the best-selected unimodal classifiers, with a 3-5% increase in prediction accuracies across various tasks. Thus, the method aims to bolster emergency response capabilities by offering more precise insights into evolving events.

Routing Protocol for Low Power and Lossy Networks based networks consists of large number of tiny sensor nodes with limited resources. These nodes are directly connected to the Internet through the border router. Hence these nodes are susceptible to different types of attacks. The possible attacks are rank attack, selective forwarding, worm hole and Denial of service attack. These attacks can be effectively identified by intrusion detection system model. The paper focuses on identification of multiple intrusions by considering the network size as 10, 40 and 100 nodes and adding 10%, 20% and 30% of malicious nodes to the considered network. Experiments are simulated using Cooja simulator on Contiki operating system. Behavior of the network is observed based on the percentage of inconsistency achieved, energy consumption, accuracy and false positive rate. Experimental results show that multiple intrusions can be detected effectively by machine learning techniques.

In this research work, we propose a method for human action recognition based on the combination of structural and temporal features. The pose sequence in the video is considered to identify the action type. The structural variation features are obtained by detecting the angle made between the joints during the action, where the angle binning is performed using multiple thresholds. The displacement vector of joint locations is used to compute the temporal features. The structural variation features and the temporal variation features are fused using a neural network to perform action classification. We conducted the experiments on different categories of datasets, namely, KTH, UTKinect, and MSR Action3D datasets. The experimental results exhibit the superiority of the proposed method over some of the existing state-of-the-art techniques.

The increasing adoption of autonomous and intelligent vehicles within ground transportation systems faces new security challenges. This shift from human-controlled operations opens up a broader attack surface for malicious players. As the interconnected Internet of Things (IoT) become ubiquitous in vehicles, they continuously generate and exchange a large amount of data. This tendency creates vulnerabilities that attackers can exploit using sophisticated techniques, such as Advanced Persistent Threats (APT). Detecting APTs in IoT-enabled vehicular environments is crucial. These APTs demand advanced detection mechanisms. The critical need for vehicular data privacy restricts traditional centralized Machine Learning (ML) approaches. Furthermore, the absence of publicly available APT datasets in the vehicular domain complicates model development and validation, creating a significant gap in cybersecurity capabilities for this evolving vehicular domain. This research proposes a novel Federated Deep Neural Network (FDNN) framework with a privacy-preserving technique to address these concerns. This study presents the key challenges in the APT detection phase and outlines the novel contributions to the body of knowledge. The research questions guiding the investigation are addressed and discussed. The features of the UNSW-NB15, Edge-IIoTset, and CSE-CIC-IDS2018 datasets are aligned with different stages of APT attacks. Using these datasets, the developed framework is analyzed and evaluated. For the mentioned datasets, the framework without privacy-preserving technique shows high APT detection accuracies of 97.32%, 96.81% and 98.06%, respectively. However, with the privacy-preserving technique, the framework shows 95.62%, 96.11% and 95.63% accuracies, respectively. All results with other evaluation metrics, such as Precision, False positive rate, F1 score etc., are tabulated. The developed framework is subjected to “Shapley Additive explanations (SHAP),” analysis to filter the considerably influential features in APT detection. This research establishes the efficacy of a novel framework for detecting APTs in distributed vehicular environments. The framework achieves superior performance by minimizing the number of data and reducing the number of features, which is demonstrated through rigorous experimentation on multiple benchmark datasets. The potential of the developed framework to detect the APTs in the cross-domain is discussed in future works.