GenTune-VDB: Workload-Generative, Cross-Family Auto-Tuning for Vector Databases

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Cybersecurity has become major progress in the digital era. Contraction is an important component of the cyber analysts’ management of information technology, as several government organizations and commercial enterprises are moving to dispersed systems. A cyber security analyst is most importantly responsible for protecting the network against damage. Attacks on networks are becoming more complex and sophisticated every day. The number of connected workplaces leads to heavy traffic, more security attack vectors, security breaches and raises more issues than the cyber area can handle by using human intervention while there is not enough sizable automation. Network Intrusion, thus, becomes the biggest concern of this generation. Intrusion Detection and Prevention System (IDPS) has become a vital complement to almost all organizations' security infrastructure. This chapter includes three network monitoring tools concentrating on the immediate impact and output of cyber assaults on the network. This helps to better comprehend the many directions in the area of network monitoring and cybersecurity research. In general, this chapter aims to study the different network monitoring techniques that can be used in support of various servers and network devices to better understands the effect of cyber-attacks on the network and monitor it through tools such as cacti, weather-map and smokeping. It also sheds light on techniques like artificial intelligence, machine learning, neural networks, fuzzy logic, next-generation firewall and how they can be coupled with Intrusion Detection System (IDS) to detect attacks on private networks.KeywordsIDPSNetwork monitoring toolsCyber-attacks monitoringCybersecurityNext generation firewall

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

High-performance clusters have been growing rapidly in scale. Most of these clusters deploy a high-speed interconnect, such as Infini-Band, to achieve higher performance. Most scientific applications executing on these clusters use the Message Passing Interface (MPI) as the parallel programming model. Thus, the MPI library has a key role in achieving application performance by consuming as few resources as possible and enabling scalable performance. State-of-the-art MPI implementations over InfiniBand primarily use the Reliable Connection (RC) transport due to its good performance and attractive features. However, the RC transport requires a connection between every pair of communicating processes, with each requiring several KB of memory. As clusters continue to scale, memory requirements in RC-based implementations increase. The connection-less Unreliable Datagram (UD) transport is an attractive alternative, which eliminates the need to dedicate memory for each pair of processes.In this paper we present a high-performance UD-based MPI design. We implement our design and compare the performance and resource usage with the RC-based MVAPICH. We evaluate NPB, SMG2000, Sweep3D, and sPPM up to 4K processes on an 9216-core InfiniBand cluster. For SMG2000, our prototype shows a 60% speedup and seven-fold reduction in memory for 4K processes. Additionally, based on our model, our design has an estimated 30 times reduction in memory over MVAPICH at 16K processes when all connections are created. To the best of our knowledge, this is the first research work that presents a high-performance MPI design over InfiniBand that is completely based on UD and can achieve near identical or better application performance than RC.

In a public cloud computing environment, consumers cannot always just depend on the cloud provider's security infrastructure. They may need to monitor and protect their virtual existence by implementing their own intrusion detection capabilities along with other security technologies within the cloud fabric. Intrusion Detection as a Service (IDSaaS) targets security of the infrastructure level of a public cloud (IaaS) by providing intrusion detection technology that is highly elastic, portable and fully controlled by the cloud consumer. A prototype of IDSaaS is described.

Data Warehouse (DW) systems maintain sensitive and crucial information, which is integrated from various heterogenous sources of organization. With the ever increasing deployment and usage of networks, these systems are becoming more vulnerable to malicious attacks. With the increased number of attacks, intrusion detection has become vital part of Information Security. In this paper, we have proposed a model for analyzing and detecting anomalous events based on user behavior analysis through usage patterns, user profiles and session management. After monitoring the events in the system, if any intrusion activity occurs, then alerts are issued to system administrators. Since a user profile is not necessarily fixed but rather it evolves with changing time, so a dynamic user behavior modeling is represented as a sequence of events and combination of fact and dimension tables accessed by the users. In this way, DW systems may be protected by the malicious attacks.

To classify sensor data correctly and quickly has a very sound impact on areas such as performance monitoring, user behavior analysis, and user accounting and intrusion detection in IoT (Internet of things). This work is an approach to reorganize the features in a dataset of 114 features depending on the relevancy and non-redundancy of an attribute or feature. Thus having a minimal set of 30 features can create a linear SVM classifier model to detect TCP attack in IoT botnet attack traffic.KeywordsFeature selectionIoTBotnetTCPSVM

As computer and network intrusions become more and more of a concern, the need for better capabilities to assist in the detection and analysis of intrusions also increases. We propose a methodology for analyzing network and computer log information visually based on the analysis of user behavior. Each user's behavior is the key to determining their intent and overriding goals, whether they attempt to hide their actions or not. Proficient hackers will attempt to hide their ultimate goal, which hinders the reliability of log file analysis. Visually analyzing the user's behavior, however, is much more adaptable and difficult to counteract. This paper will discuss how user behavior can be exhibited within the visualization techniques, the capabilities provided by the environment, typical characteristics users should look out for (i.e., how unusual behavior exhibits itself), and exploration paradigms effective for identifying the meaning behind the user's behavior.

Attack-aware Security Function Management

There are currently many threats to network security. This is especially true for telecom operators and telecommunication service providers, which are a key link in the data transmission infrastructure for any company. To ensure the protection of their infrastructure and cloud services provided to end-users, telecom operators have to use non-trivial solutions. At the same time, the accuracy of defining attacks by security systems is not the least. In the framework of this study, an approach was developed and attack detection was modeled based on the analysis of state chains of network nodes. The proposed approach allows the comparison of events occurring in the network with events recorded by intrusion detection systems. In our study, we solve the problem of formalizing a typical attack profile in a network of telecommunication service providers by constructing a sequence of transitions of states of network nodes and the time of the state change of individual devices under study. The study covers the most popular types of attacks. To formalize the rules for classifying states, the study uses a decision tree algorithm to build a chain of security events. In the experimental part of the study, the accuracy of the classification of known types of attacks recorded in security event logs using ROC analysis was assessed. The results obtained made it possible to evaluate the effectiveness of the developed model for recognizing network attacks in the infrastructure of telecommunication service providers. The experimental results show fairly high accuracy in determining the popular type of attack. This will also help in the future to reduce the response time to security incidents in a large network, due to earlier detection of illegitimate behavior.

ARTINALI#: An Efficient Intrusion Detection Technique for Resource-Constrained Cyber-Physical Systems

If the packet characteristics acquired are sufficiently detailed, packet analysis is a common forensic approach in network forensics that can replay the whole network traffic for a specified time period. This can be used to find evidence of illicit online activity such as data breaches, unauthorized website access, malware infection, and infiltration attempts, as well as to reproduce image files, documents, email attachments, and other material sent over the network. This paper covers a comprehensive assessment of the usage of packet analysis, including deep packet inspection, in network forensics, as well as a discussion of AI-powered packet analysis methodologies with sophisticated network traffic classification and pattern recognition. Keywords: Cybersecurity; Network Security; Traffic Analysis; Deep Inspection; Intrusion Detection; Network Forensics

NTLFlowLyzer: Towards generating an intrusion detection dataset and intruders behavior profiling through network and transport layers traffic analysis and pattern extraction

Robust adaptive multivariate Hotelling's T2 control chart based on kernel density estimation for intrusion detection system

As cyberspace continues to expand and diversify, protecting networked systems from malicious activity remains a fundamental challenge across science and engineering. Intrusion detection systems (IDSs) play a critical role in maintaining the security and reliability of modern digital infrastructure. Many existing anomaly-based intrusion detection systems rely on traditional machine learning techniques, such as clustering and decision trees, to identify deviations from normal traffic behavior. While effective in static settings, these approaches often struggle to adapt to evolving threats and complex, nonlinear patterns in network traffic, and they frequently require significant manual model design. The problem addressed in this paper is how to design an intrusion detection approach that can autonomously adapt its internal structure to better capture evolving patterns in network traffic. Here we show that a neuroevolutionary approach based on Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) can evolve neural network architectures that improve anomaly detection without manual architectural tuning. Building on prior work using NEAT for intrusion detection, this study investigates HyperNEAT’s ability to exploit geometric relationships among network features through Compositional Pattern Producing Networks (CPPNs). Using the NSL-KDD dataset and three distinct fitness formulations, we evaluate HyperNEAT’s effectiveness in detecting anomalous traffic and compare its performance to traditional machine learning–based IDS methods. The results quantify the extent to which geometry-aware neuroevolution improves detection capability. More broadly, this work demonstrates the potential of neuroevolutionary algorithms to serve as adaptive, self-organizing alternatives to manually designed IDS models. By reducing reliance on predefined architectures, HyperNEAT offers a promising direction for building intrusion detection systems that can respond more effectively to emerging and unknown cyber threats.