Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1403.7726

Relevant Feature Selection Model Using Data Mining for Intrusion Detection System

Ayman I. Madbouly·Amr M. Gody·Tamer M. Barakat
View original source ↗Read source document ↗Export metadata ↓

Abstract

Network intrusions have become a significant threat in recent years as a result of the increased demand of computer networks for critical systems. Intrusion detection system (IDS) has been widely deployed as a defense measure for computer networks. Features extracted from network traffic can be used as sign to detect anomalies. However with the huge amount of network traffic, collected data contains irrelevant and redundant features that affect the detection rate of the IDS, consumes high amount of system resources, and slowdown the training and testing process of the IDS. In this paper, a new feature selection model is proposed; this model can effectively select the most relevant features for intrusion detection. Our goal is to build a lightweight intrusion detection system by using a reduced features set. Deleting irrelevant and redundant features helps to build a faster training and testing process, to have less resource consumption as well as to maintain high detection rates. The effectiveness and the feasibility of our feature selection model were verified by several experiments on KDD intrusion detection dataset. The experimental results strongly showed that our model is not only able to yield high detection rates but also to speed up the detection process.

Explore related subjects

cs.CRcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ayman I. Madbouly, Amr M. Gody, Tamer M. Barakat. 2014-03-30. Relevant Feature Selection Model Using Data Mining for Intrusion Detection System. https://doi.org/10.14445/22315381%2Fijett-v9p296

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MIRANDA: short signatures from a leakage-free full-domain-hash scheme

We present $\mathsf{Miranda}$, the first family of full-domain-hash signatures based on matrix codes. This signature scheme fulfils the paradigm of Gentry, Peikert and Vaikuntanathan ($\mathsf{GPV}$), which gives strong security guarantees. Our trapdoor is very simple and generic: if we propose it with matrix codes, it can actually be instantiated in many other ways since it only involves a subcode of a decodable code (or lattice) in a unique decoding regime of parameters. Though $\mathsf{Miranda}$ signing algorithm relies on a decoding task where there is exactly one solution, there are many possible signatures given a message to sign and we ensure that signatures are not leaking information on their underlying trapdoor by means of a very simple procedure involving the drawing of a small number of uniform bits. In particular $\mathsf{Miranda}$ does not use a rejection sampling procedure which makes its implementation a very simple task contrary to other $\mathsf{GPV}$-like signatures schemes such as $\mathsf{Falcon}$ or even $\mathsf{Wave}$. We instantiate $\mathsf{Miranda}$ with the famous family of Gabidulin codes represented as spaces of matrices and we study thoroughly its security (in the EUF-CMA security model). For~$128$ bits of classical security, the signature sizes are as low as~$90$ bytes and the public key sizes are in the order of~$2.6$ megabytes.

cs.CR↗
arXiv2026

SteganoBackdoor: Evading Data-Poisoning Defenses via Steganographic Backdoors

Transformer-based models are highly susceptible to backdoor attacks via supervised fine-tuning (SFT). To red-team existing data-poisoning defenses, prior work has increasingly focused on stylized triggers, synthetic artifacts, and token-level perturbations designed to evade detection. However, this trend has shifted threat models away from naturally occurring semantic triggers and realistic low-budget poisoning settings. Addressing this gap, we introduce SteganoBackdoor, an optimization-based framework that transforms semantic-trigger seeds through autoregressive token replacement, sequentially minimizing embedding overlap with the inference-time trigger while preserving a strong per-sample training-time payload. The resulting SteganoPoisons maintain linguistic fluency and encode the payload across ordinary tokens, such that no individual token carries a concentrated signal and the full payload instead emerges from their exact combination and ordering. Across 18 encoder-based and decoder-only models spanning 120M to 14B parameters, SteganoBackdoor achieves high attack success under sub-percent poisoning budgets and exposes limitations in existing data-poisoning defenses.

cs.CR↗
arXiv2026

Foundations and Design Principles of Lightweight Cryptography for IoT Systems

The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation, there is a significant gap in addressing different security aspects, such as design principles, specific to the IoT environment. This study aims to bridge this gap. This research presents an examination with focusing on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a concise overview of lightweight ciphers with emphasizing on their security challenges which is an essential consideration for real-time and resource-constrained applications.

cs.CR↗