VANET Intrusion Investigation Based Forensics Technology: A New Framework

AlKhanafseh, Mohammad; Surakhi, Ola

doi:10.1109/etcea57049.2022.10009698

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Network forensic analysis centers on the monitoring of network traffic and the investigation of the sources of potential attacks. This process involves the collection and analysis of data related to network activities and events, intending to identify and mitigate security breaches or unauthorized access [23,24].…”

Section: Computer Forensics Domainsmentioning

confidence: 99%

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

AlKhanafseh,

Surakhi

2024

Preprint

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The adoption of technology and digital tools across various user groups has brought about a heightened risk of cyber-attacks targeting devices and shared data. These elevated levels of cybercrime necessitate thorough investigations to address victims’ inquiries about the nature, causes, affected data, and impacted resources of the attack. Digital forensics serves as a crucial means to answer these queries. Additionally, it enables the formulation of strategies and delineation of roles essential for fortifying future defenses against such attacks. Within digital forensics, digital evidence holds pivotal importance in the investigative process as it serves as a cornerstone in legal proceedings to identify perpetrators. Numerous research endeavors focus on devising solutions to safeguard evidence, including centralized storage centers or cloud computing. These efforts aim to create frameworks integrating diverse security levels for sharing and preserving evidence effectively. In this paper, a new framework is proposed that seeks to amalgamate novel technologies with forensic practices to address challenges in the preservation phase of digital forensics. Strategies include segmenting evidence into smaller components, leveraging steganography to support preservation, and employing blockchain technology to ensure the integrity of collected evidence.

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Section: Computer Forensics Domainsmentioning

confidence: 99%

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

AlKhanafseh,

Surakhi

2024

Preprint

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“…One application where machine learning methods are widely used is intrusion detection systems (IDSs) [1,2]. IDS is employed to monitor the network traffic and identify any unauthorized efforts to access that network through the analysis of incoming and outgoing actions, with the aim of detecting indications of potentially harmful actions [3].…”

Section: Introductionmentioning

confidence: 99%

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

et al. 2023

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An intrusion detection system (IDS) plays a critical role in maintaining network security by continuously monitoring network traffic and host systems to detect any potential security breaches or suspicious activities. With the recent surge in cyberattacks, there is a growing need for automated and intelligent IDSs. Many of these systems are designed to learn the normal patterns of network traffic, enabling them to identify any deviations from the norm, which can be indicative of anomalous or malicious behavior. Machine learning methods have proven to be effective in detecting malicious payloads in network traffic. However, the increasing volume of data generated by IDSs poses significant security risks and emphasizes the need for stronger network security measures. The performance of traditional machine learning methods heavily relies on the dataset and its balanced distribution. Unfortunately, many IDS datasets suffer from imbalanced class distributions, which hampers the effectiveness of machine learning techniques and leads to missed detection and false alarms in conventional IDSs. To address this challenge, this paper proposes a novel model-based generative adversarial network (GAN) called TDCGAN, which aims to improve the detection rate of the minority class in imbalanced datasets while maintaining efficiency. The TDCGAN model comprises a generator and three discriminators, with an election layer incorporated at the end of the architecture. This allows for the selection of the optimal outcome from the discriminators’ outputs. The UGR’16 dataset is employed for evaluation and benchmarking purposes. Various machine learning algorithms are used for comparison to demonstrate the efficacy of the proposed TDCGAN model. Experimental results reveal that TDCGAN offers an effective solution for addressing imbalanced intrusion detection and outperforms other traditionally used oversampling techniques. By leveraging the power of GANs and incorporating an election layer, TDCGAN demonstrates superior performance in detecting security threats in imbalanced IDS datasets.

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A New Data Balancing Approach based Generative Adversarial Network for Network Intrusion Detection System

Jamoos¹,

García²,

AlKhanafseh³

et al. 2023

Preprint

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The IDS serves as a security system that maintains constant surveillance over network traffic and host systems in order to identify any security breaches or potentially concerning activities. Recently. the rise in cyber-attacks has driven the necessity for the development of automated and intelligent network intrusion detection systems. These systems are designed to learn the typical patterns of network traffic, allowing them to identify any deviations from normal behaviour, which can be classified as anomalous or malicious. Machine learning methods are widely used to exhibit a satisfactory effectiveness in detecting malicious payloads in the network traffic. While the volume of the data generated from IDS is increasing exponentially results in the emergence of substantial security risks, it highlighted the imperative to strengthen network security. The performance of traditional machine learning methods depends on the dataset and the data balance distribution in it. while most of IDS datasets suffer from unbalancing, this limits the performance of the machine learning method used in the system and results in missed detections and false alarms in the conventional IDSs. To address this issue, this paper presents a new model-based Generative Adversarial Network (GAN) called TDCGAN to enhance the detection rate of less of minor class in the dataset while maintaining efficiency. The proposed model consists of one generator and three discriminators with an election layer at the end of architecture. The UGR’16 data set is used for evaluation purposes. In order to demonstrate the efficacy of our proposed model, various machine learning algorithms have been utilized for comparison. The experimental findings have determined that TDCGAN presents an efficient resolution for addressing imbalanced intrusion detection and surpasses the performance of other oversampling machine learning methods.

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VANET Intrusion Investigation Based Forensics Technology: A New Framework

Cited by 4 publications

References 31 publications

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System

A New Data Balancing Approach based Generative Adversarial Network for Network Intrusion Detection System

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