SNAPS: Semantic network traffic analysis through projection and selection

The thirteen articles in this special section explore the technology of deep packet inspection (DPI). DPI examines the content in packet payloads to search for signatures of network applications, signs of malicious activities, and leaks of sensitive information, rather than just examine packet headers for information such as IP addresses and port numbers. The inspection provides network devices with rich information of application protocol messages in packet payloads, and enables them to make intelligent decisions in packet processing based on the information. The papers are organized into the following four sections: (1) Scalable Algorithms and Architectures for DPI, (2) Network Traffic Analysis with DPI, (3) Network Protocol Identification with DPI, and (4) Network Security Analysis with DPI.

Detection of advanced persistent threat using machine-learning correlation analysis

Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis. Thousands of transaction requests are handled and processed everyday by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog analysis comes to play a crucial role in such situations where Hadoop can be suggested as an efficient solution for processing the Netflow data collected from switches as well as website access-logs during fixed intervals.

Advanced persistent threat (APT) attack is a malicious attack type which has intentional and clear targets. This attack technique has become a challenge for information security systems of organizations, governments, and businesses. The approaches of using machine learning or deep learning algorithms to analyze signs and abnormal behaviors of network traffic for detecting and preventing APT attacks have become popular in recent years. However, the APT attack detection approach that uses behavior analysis and evaluation techniques is facing many difficulties due to the lack of typical data of attack campaigns. To handle this situation, recent studies have selected and extracted the APT attack behaviors which based on datasets are built from experimental tools. Consequently, these properties are few and difficult to obtain in practical monitoring systems. Therefore, although the experimental results show good detection, it does not bring high efficiency in practice. For above reasons, in this paper, a new method based on network traffic analysis using a combined deep learning model to detect APT attacks will be proposed. Specifically, individual deep learning networks such as multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) will also be sought, built and linked into combined deep learning networks to analyze and detect signs of APT attacks in network traffic. To detect APT attack signals, the combined deep learning models are performed in two main stages including (i) extracting IP features based on flow: In this phase, we will analyze network traffic into networking flows by IP address and then use the combined deep learning models to extract IP features by network flow; (ii) classifying APT attack IPs: Based on IP features extracted in a task (i), the APT attack IPs and normal IPs will be identified and classified. The proposal of a combined deep learning model to detect APT attacks based on network traffic is a new approach, and there is no research proposed and applied yet. In the experimental section, combined deep learning models proved their superior abilities to ensure accuracy on all measurements from 93 to 98%. This is a very good result for APT attack detection based on network traffic.

The speed of the advancement of digital technologies has led to a remarkable rise in the scale, complexity, and sophistication of cyber-attacks. Traditional security approaches based on extensive use of static rules and signature-based detections are simply not adequate for contemporary threats such as zero-day exploits, advanced persistent threats (APTs), and adversarial attacks. As a radical shift in approach, deep learning has emerged, providing intelligent, adaptive, and automated features for cyber defenses. This review paper provides an inclusive overview of deep learning technologies to mitigate various forms of cyber-attacks. Various deep learning architectures are investigated in this paper, such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), autoencoders, generative adversarial networks (GANs), and graph neural networks (GNN), and the applications of each technology in intrusion detection, malware classification, phishing detection, anomaly detection, and network traffic analysis. In addition, we discuss mainstream datasets, metrics for evaluation, and methodology as they relate to cyber security. Issues such as limited data, adversarial robustness, and usability, will be critically discussed. We also discuss new directions such as hybrid models, privacy-preserving learning, and federated defenses. By aggregating recent research, this review highlights the promise of deep models to serve as robust defenders against deep and evolving cyber-attacks, which serves as impetus for next-generation intelligent cybersecurity systems.

Advanced Persistent Threats (APTs) are the most sophisticated attacks for modern information systems. Currently, more and more researchers begin to focus on graph-based anomaly detection methods that leverage graph data to model normal behaviors and detect outliers for defending against APTs. However, previous studies of provenance graphs mainly concentrate on system calls, leading to difficulties in modeling network behaviors. Coarse-grained correlation graphs depend on handcrafted graph construction rules and, thus, cannot adequately explore log node attributes. Besides, the traditional Graph Neural Networks (GNNs) fail to consider meaningful edge features and are difficult to perform heterogeneous graphs embedding. To overcome the limitations of the existing approaches, we present a hierarchical approach for APT detection with novel attention-based GNNs. We propose a metapath aggregated GNN for provenance graph embedding and an edge enhanced GNN for host interactive graph embedding; thus, APT behaviors can be captured at both the system and network levels. A novel enhancement mechanism is also introduced to dynamically update the detection model in the hierarchical detection framework. Evaluations show that the proposed method outperforms the state-of-the-art baselines in APT detection.

Designing systems that can infiltrate networks, remain undetected for long periods, and extract sensitive information make Advanced Persistent Threats (APTs) some of the most sophisticated and powerful cyber-attacks. Most APTs circumvent conventional Intrusion Detection Systems (IDS) because of signature-based detection technology that relies on static patterns and is blind to new forms of threats. To bridge this gap, we put forth a comprehensive framework that applies machine learning through the incorporation of Random Forest, Isolation Forest, and Long Short-Term Memory (LSTM) networks aimed at APT detection. Random Forest, as a supervised learning algorithm, will extract known attack patterns while Isolation Forest, as an unsupervised algorithm, will capture anomalous network behavior corresponding to unknown attacks. With LSTM networks, complex multivariate network traffic patterns are analyzed to identify complex attack behaviors. The UNSW-NB15 dataset, which contains various attack scenarios as well as normal network traffic, is used for training and testing the models. The combination of these technologies is deployed on a scalable serverless infrastructure through A WS SageMaker and real-time data streams are processed using A WS Kinesis. The experimental findings show substantial improvements in accuracy, precision, and recall in both batch and real-time detection scenarios. This approach provides a comprehensive and flexible solution to counter evolving cyber threats with enhanced network security.

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs’ performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model’s performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF’s capabilities in intrusion detection and network security solutions.

With the wide use of Cyber-Physical Systems (CPS) in many applications, targets of advanced persistent threats (APTs) have been extended to the IoT and industrial control systems. Provenance graph analysis based on system audit logs has become a promising way for APT detection and investigation. However, we cannot afford to ignore that existing provenance-based APT detection systems lack the process–context information at system runtime, which seriously limits detection performance. In this paper, we proposed ConGraph, an approach for detecting APT attacks using provenance graphs combined with process context; we presented a module for collecting process context to detect APT attacks. This module collects file access behavior, network access behavior, and interactive relationship features of processes to enrich semantic information of the provenance graph. It was the first time that the provenance graph was combined with multiple process–context information to improve the detection performance of APT attacks. ConGraph extracts process activity features from the provenance graphs and submits the features to a CNN-BiLSTM model to detect underlying APT activities. Compared to some state-of-the-art models, our model raised the average precision rate, recall rate, and F-1 score by 13.12%, 25.61%, and 24.28%, respectively.

Abstract: This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats

Risk management is one of the fundamental pillars of IT program management, particularly as organizations navigate the increasingly complex digital landscape of the 21st century. With the growing reliance on digital infrastructures, data, and interconnected systems, the challenges associated with cybersecurity, artificial intelligence (AI) integration, and infrastructure stability are becoming more pronounced. These three components—cybersecurity, AI, and infrastructure stability—are intricately linked, and their balance is essential for ensuring the long-term viability, resilience, and security of an organization’s IT systems. As organizations adopt new technologies and expand their digital footprints, IT managers face the critical task of balancing these elements in order to build robust, future-proof IT programs. The role of cybersecurity in IT program management has evolved significantly over the past few decades. Historically, cybersecurity was often relegated to being a peripheral concern, managed in isolation by dedicated security teams. Traditional approaches primarily focused on perimeter defenses, such as firewalls, intrusion detection systems (IDS), and antivirus software, to keep cyber threats at bay. While these methods provided some degree of protection, they were largely reactive and could only address known threats. As cyber adversaries have become more sophisticated, cyber threats have transcended basic attacks and evolved into more complex and persistent tactics, such as advanced persistent threats (APTs), zero-day exploits, and ransomware attacks. These evolving threats have highlighted the limitations of traditional cybersecurity measures and underscored the need for more proactive, dynamic security solutions. As cyber threats continue to evolve, organizations are increasingly turning to artificial intelligence (AI) to enhance their cybersecurity capabilities. AI, particularly in the form of machine learning (ML) and deep learning (DL), is enabling IT teams to move from traditional, rule-based security systems to more adaptive and intelligent systems that can detect anomalies, predict vulnerabilities, and respond to incidents in real-time. AI-driven cybersecurity solutions offer a range of capabilities, such as threat detection, incident response, and automated remediation, all of which can significantly reduce the time it takes to detect and mitigate security breaches. AI-powered systems can analyze vast amounts of data, identify patterns and anomalies, and learn from past incidents to improve detection accuracy over time. This enables organizations to identify and respond to threats much more quickly and effectively than traditional systems that rely on predefined rules or signatures. The integration of AI into IT program management offers numerous benefits, including improved efficiency, scalability, and accuracy in detecting and responding to cyber threats. However, the implementation of AI also presents new challenges. For one, AI technologies themselves are not immune to vulnerabilities. Adversarial attacks, where malicious actors manipulate AI systems by feeding them false data or exploiting weaknesses in their models, represent a significant risk. AI models can also be vulnerable to bias if not properly trained on diverse, representative datasets. Additionally, AI-driven systems require large amounts of data to function effectively, raising concerns about data privacy and security. Organizations must carefully consider how to manage and protect the vast amounts of sensitive data needed to train AI models, while also ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

Cloud computing has become a cornerstone of modern IT infrastructure, offering scalability and efficiency but also exposing organizations to evolving cyber threats such as data breaches, insider threats, and advanced persistent threats (APTs). Traditional security mechanisms struggle to address these dynamic challenges, necessitating the integration of AI-driven threat detection and prevention strategies. This conceptual paper explores the comparative effectiveness of supervised learning, unsupervised learning, reinforcement learning, and hybrid AI models in cloud security. Supervised learning excels in identifying known attack patterns, while unsupervised learning is crucial for detecting zero-day threats and anomalies. Reinforcement learning enables self-adaptive security measures, and hybrid models offer a comprehensive, multi-layered approach to cloud security. However, AI-driven cybersecurity faces significant challenges, including data privacy risks, bias in threat detection, adversarial AI attacks, and lack of model interpretability. Emerging AI trends such as federated learning, quantum security, and explainable AI (XAI) are shaping the future of cloud security, while regulatory frameworks like GDPR, NIST AI Risk Management, and the EU AI Act play a crucial role in standardizing ethical AI use. This study provides insights into the strengths, weaknesses, and future directions of AI-driven cloud security, offering recommendations for researchers, policymakers, and cybersecurity practitioners to enhance AI resilience against emerging threats.

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.

The deployment of Next Generation Networks (NGNs) across Africa is accelerating digital connectivity and economic growth. However, this transformation is accompanied by escalating cybersecurity risks that challenge the effectiveness of traditional perimeter-based security architectures. This paper presents an Artificial Intelligence (AI)-driven Zero Trust Security (ZTS) framework specifically designed for NGN environments within the African context. Rooted in the "never trust, always verify" principle, the proposed framework leverages machine learning and behavioral analytics to enable continuous authentication, dynamic access control, and real-time threat mitigation. The integration of AI enhances the system’s ability to detect anomalous activities, respond to zero-day attacks, and adapt security policies based on contextual awareness. Recognizing Africa’s unique challenges—including infrastructural constraints, diverse regulatory landscapes, and limited skilled cybersecurity personnel, the model emphasizes scalability, automation, and cost-efficiency. Simulations and case studies demonstrate the proposed solution’s effectiveness in mitigating insider threats, lateral movement, and advanced persistent threats (APTs) within NGN infrastructures. The findings suggest that AI-powered ZTS can serve as a foundational cybersecurity strategy for building resilient, intelligent, and secure digital ecosystems in Africa. Index Terms: Artificial Intelligence (AI), Zero Trust Security (ZTS), Next Generation Networks (NGN), Cybersecurity, Network Access Control, Behavioral Analytics, Advanced Persistent Threats (APTs), African Telecommunications, Context-Aware Security, Dynamic Threat Detection, Identity and Access Management (IAM), Digital Infrastructure Security, Policy-Based Security.

Current network traffic analysis systems heavily rely on Deep Packet Inspection (DPI) techniques, such as Finite Automata (FA), to detect patterns carried by regular expression (regex). However, traditional Finite Automata cannot keep up with the ever-growing speed of the Internet links. Although there are a number of efficient FA compressing mechanisms for DPIs, there is no standardized or common way to evaluate and compare them. In this scenario, this paper proposes a methodology to evaluate and compare automaton models and the data-structures that materialize them. We also adapt state-of-the-art memory layouts to better fit in today's computer architectures. Finally, we apply our methodology to most important automaton models, memory layouts, and well-known signature sets. The results show us that some memory layouts are not efficient for regexes that represent small automata and other ones which fit only with uncompressed automata. Further, we also found out that theoretical studies about memory usage from memory encodings are not as accurate as they should be.