Academic literature on the topic 'Outbound intrusion detection'

The rapid expansion of the use of the Internet of Things (IoT) has encouraged many attackers to exploit the vulnerabilities in these networks to violate data privacy or disrupt service; they are easy targets due to the diversity of devices within the network, which has led to the loss of unified security standards. intrusion detection system (IDS) play a pivotal role in securing IoT networks by monitoring inbound and outbound traffic to these networks and issuing a security alarm when there is an attack; moreover, they respond directly to these security threats to prevent them from harming the network and violating data privacy. To design an IDS capable of performing work with high efficiency, an appropriate dataset must be chosen to train and evaluate the designed model. This dataset works as a fundamental task in the success of these systems because it plays a major role in training the system, feature engineering, evaluating the performance of the model, and other tasks. This paper focused on one of the modern datasets used in training and evaluating IDS models, that is, the CICIOT2023 dataset. The CICIOT2023 dataset is distinguished from other datasets, such as CICIDS2017, UNSW-NB15, and KDD1999. It focuses on the IoT environment, unlike other datasets that focus on data traffic in traditional networks, and it uses a variety of devices and protocols; moreover, it contains modern and complex attacks and a balance between the data of those attacks and normal traffic. This paper discusses the structure of the dataset, the kinds of attacks it contains, the applications and fields in which it is used, the strengths that distinguish it from other datasets, its role in developing cybersecurity research, the most important studies that have been written and dealt with this dataset, and finally, the future visions for developing the dataset.

Attempts at unauthorized intrusion occur quite often, even after the adoption of the necessary security policies and practices for the information network. These are attacks in which an attacker gains access to the system using various hacking techniques. A firewall is a hardware and software-based network security system that uses certain rules to manage incoming and outgoing network packets. The firewall controls access to network resources through a positive management model. There are various traditional firewalls, such as packet filters, program-level gateways, and chip-level gateways, which have their pros and cons. To overcome the shortcomings of the traditional firewall, a new generation of firewalls is introduced. The article presents the study of traditional firewalls and their evolution to a new generation firewall and its benefits. New firewalls still belong to the third generation, but are often referred to as the "next generation" or NGFW. This type combines all previously used approaches with an in-depth review of filtered content and its comparison with a database to identify potentially dangerous traffic. Modern firewalls often have built-in additional security systems: virtual private networks (VPNs), intrusion prevention and detection systems (IPS / IDS), authentication management, application management, and web filtering. Their state-of-the-art technology can filter outbound traffic. This helps reduce the likelihood of data theft by attackers. In addition, an important function of the firewall is to reduce the risk of devices becoming part of a botnet (a malicious network with a large group of devices controlled by cybercriminals).

Because of the fast growing in network system, many categories of intrusion has been discovered that differs from current one and convention firewall and definite rules set and strategies are unable of recognizing this intrusion in real-time. Hence, this demand is requirements of real-times intrusions detection systems (RTs-IDS). The vital aim of this paper is to build an RT-IDSs proficient of classifying intrusion by analyzing the outbound and incoming networks information in real-times. The suggested method contains of deep neural networks (DNNs) trained by use 28 types of the NSL-KDDs datasets. Furthermore, it comprises the machine learning (MLs) pipelines with successive modules for category of data encode and features scaling, that is use before transmit the real-times information to the train DNNs models to create prediction. Composed of the train DNNs models, the MLs pipelines are introduced in the servers that can be access through representation state transfer applications program interface (RESTs API). The DNNs has displayed outstand test performance result realizing around 70% to 96% for f1-score, accuracy, precisions, and recalls. These works comprise a complete practical clarification regarding the implementations and functional of the whole systems. The suggested system usability and efficiency have been increased by its comfort of implementations and remotely accessing. In addition, the proposed model is extremely beneficial for rapidly detects the intrusion by analyze incoming and outbound networks traffics.

A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. NIDS is an intrusion detection system that attempts to discover unauthorized access to a computer network by analyzing traffic on the network for signs of malicious activity. Intrusion detection is an important technology in business sector as well as the research area.  It inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. A signature based IDS will survey the packets on the network and compare them against a database of signatures or attributes from known attackers. In this system the attack log displays the list of attacks to the administrator for Unauthorized action. This system works as an alert device in the event of attacks directed towards an entire network.                   In This Paper we will discuss about the detection and Prevention of Network and the Business world prevent from the unauthorized access. Keywords— Network Intrusion Detection System, Online matching algorithm, Signature Based IDS

A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. NIDS is an intrusion detection system that attempts to discover unauthorized access to a computer network by analyzing traffic on the network for signs of malicious activity. Intrusion detection is an important technology in business sector as well as the research area.  It inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. A signature based IDS will survey the packets on the network and compare them against a database of signatures or attributes from known attackers. In this system the attack log displays the list of attacks to the administrator for Unauthorized action. This system works as an alert device in the event of attacks directed towards an entire network.                 In This Paper we will discuss about the detection and Prevention of Network and the Business world prevent from the unauthorized access. Keywords— Network Intrusion Detection System, Online matching algorithm, Signature Based IDS

AbstractIn recent years, due to the rapid growth in network technology, numerous types of intrusions have been uncovered that differ from the existing ones, and the conventional firewalls with specific rule sets and policies are incapable of identifying those intrusions in real-time. Therefore, that demands the requirement of a real-time intrusion detection system (RT-IDS). The ultimate purpose of this research is to construct an RT-IDS capable of identifying intrusions by analysing the inbound and outbound network data in real-time. The proposed system consists of a deep neural network (DNN) trained using 28 features of the NSL-KDD dataset. In addition, it contains the machine learning (ML) pipeline with sequential components for categorical data encoding and feature scaling, which is used before transmitting the real-time data to the trained DNN model to make predictions. Moreover, a real-time feature extractor, which is a C++ program that sniffs data from the real-time network traffic and derives relevant data related to the features of the NSL-KDD dataset using the sniffed data, is deployed between the gateway router and the local area network (LAN). Together with the trained DNN model, the ML pipeline is hosted in a server that can be accessed via a representational state transfer application programming interface (REST API). The DNN has revealed outstanding testing performance results achieving 81%, 96%, 70% and 81% for accuracy, precision, recall and f1-score accordingly. This research comprises a comprehensive technical explanation concerning the implementation and functionality of the complete system. Moreover, leveraging the extensive explanations provided in this paper, advanced IDSs capable of identifying modern intrusions can be constructed.

This paper describes the design and results of FROID, an outbound intrusion detection system built with agent technology and supported by an attacker-centric ontology. The prototype features a misuse-based detection mechanism that identifies remote attack tools in execution. Misuse signatures composed of attributes selected through entropy analysis of outgoing traffic streams and process runtime data are derived from execution variants of attack programs. The core of the architecture is a mesh of self-contained detection cells organized non-hierarchically that group agents in a functional fashion. The experiments show performance gains when the ontology is enabled as well as an increase in accuracy achieved when correlation cells combine detection evidence received from independent detection cells.

Internet of things (IoT) has transformed greatly the improved way of business through machine-to-machine (M2M) communications. This vast network and its associated technologies have opened the doors to an increasing number of security threats which are dangerous to IoT and 5G wireless networks. The first part of this chapter presents instruction detection system (IDS) which detect the various attacks in 6LoWPAN layer. An IDS is to detect and analyze both inbound and outbound network traffic for abnormal activities. An IPS complements an IDS configuration by proactively inspecting a system's incoming traffic to weed out malicious requests. A typical IPS configuration uses web application firewalls and traffic filtering solutions to secure applications. An IPS prevents attacks by dropping malicious packets, blocking offending IPs and alerting security personnel to potential threats. Machine learning (ML)-based instruction detection and prevention system (IDPS) is proposed and implemented in Contiki simulation environment.

<div class="section abstract"><div class="htmlview paragraph">Automotive researchers and industry experts have extensively documented vulnerabilities arising from unauthorized in-vehicle communication through academic research, industry investigations, sponsored events, and learnings from real-world attacks. While current cybersecurity endeavors in the heavy-duty (HD) vehicle space focus on securing conventional communication technologies such as the controller area network (CAN), there is a notable deficiency in defensive research concerning legacy technologies, particularly those utilized between trucks and trailers. In fact, state-of-the-art attacks on these systems have only come to public attention through official disclosures and public presentations as recently as 2020.</div><div class="htmlview paragraph">To address these risks, this paper introduces a system-wide security concept called Legacy Intrusion Detection System (LIDS) for heavy-duty vehicle applications utilizing the SAE J1708/J1587 protocol stack. LIDS relies on coordinated network gateways at each host and employs specialized J1587 security messages to alert other hosts of anomalies. Each gateway uses configurable busload, access control, and transmission rate parameters to perform signature-based and anomaly-based detection on inbound and outbound network traffic for its host.</div><div class="htmlview paragraph">This paper also presents the development process of the gateway and summarizes the experiments conducted to satisfy the hardware, software, and security requirements imposed by the J1708/J1587 stack and the LIDS concept. Subsequently, we deploy, test, and evaluate LIDS on a retrofitted dual air brake system simulator (DABSS) at CSU's Powerhouse Energy Campus. Under the assumptions presented, the experiments show that LIDS is effective against message spoofing attacks originating from a compromised host or rogue device and flooding attacks from hosts. However, LIDS' effectiveness against flooding attacks from rogue nodes depends on the designer's false positive tolerance. This research builds upon learnings in prior work while incorporating guidelines outlined in SAE J3061. To the best of current knowledge, this publication marks the first presentation of cybersecurity defense research on the SAE J1708/J1587 protocol stack.</div></div>