物联网IoT入侵检测：二维降维与双层分类方法
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“IoT骨干网中基于异常的入侵检测的二维降维和两层分类模型.pdf”探讨了在物联网（IoT）环境中如何通过一种创新的双层维度降低和分类模型来有效地检测入侵和恶意活动。该模型针对用户到根（U2R）和远程到本地（R2L）攻击，旨在提升网络基础设施的韧性。
随着IoT设备和服务的广泛应用，网络安全问题变得越来越重要。传统的入侵检测方法可能无法有效地应对物联网环境中的复杂威胁。因此，该研究提出了一种新的模型，它结合了分量分析和线性鉴别分析（LDA）进行数据降维，以及纳伊夫贝叶斯（Naive Bayes）和确定性因子版本的K-最近邻（K-NN）算法进行两层分类，以识别潜在的入侵行为。
在高维数据集中，分量分析帮助提取关键特征，而LDA进一步减少了特征数量，降低了计算复杂度，同时保留了数据的关键信息。接下来，通过两个分类层次，第一层使用纳伊夫贝叶斯算法，这是一种基于概率的分类方法，能处理大量类别并假设特征之间相互独立。第二层引入K-NN，这是一种基于实例的学习，根据最接近的数据点（邻居）来做出决策，增强了模型的识别能力。
实验部分，该模型在NSL-KDD数据集上进行了验证，这是一个广泛使用的入侵检测系统数据集，包含了多种攻击类型。结果显示，提出的模型在检测U2R和R2L攻击方面表现出优于先前方法的性能。这表明，双层维度减少和分类策略可以有效提升IoT网络的安全防护能力。
这篇论文为IoT安全领域提供了一个实用且高效的解决方案，它强调了在复杂网络环境中，通过智能数据处理和机器学习技术来提高入侵检测效率的重要性。这一研究对于预防和抵御IoT网络中的恶意活动具有实际应用价值，也为未来的研究提供了新的思路和技术参考。
2168-6750 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TETC.2016.2633228, IEEE
Transactions on Emerging Topics in Computing
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3
Ambusaidi et al. in [33] proposed a mutual information based
IDS that selects optimal feature for classification based on
feature selection algorithm. Their approach was evaluated using
three benchmark data set (KDD Cup 99, NSL-KDD and Kyoto
2006+).
Intrusion detection systems have also been used for
managing security risks in industrial control systems [14]. For
example, Pan et al. [34] proposed a systematic and automated
approach to build a hybrid IDS that learns temporal state-based
specifications for electric power systems to accurately
differentiate between disturbances, normal control operations,
and cyber-attacks. Zhou et al. [35] presented an industrial
anomaly and multi model driven IDS based on Hidden Markov
Model to filter attacks from actual faults.
Security issues can be a barrier to widespread adoption of IoT
devices [36]. Whitmore et al., [37] showed that wide range of
techniques could mitigate cyber threat targeting IoT systems.
Ning et al. [38] proposed a hierarchical authentication
architecture to provide anonymous data transmission in IoT
networks. Cao et al. [39] highlighted the impact and
importance of ghost attacks on ZigBee based IoT devices. Chen
et al. [40] proposed an autonomic model-driven cyber security
management approach for IoT systems, which can be used to
estimate, detect, and respond to cyberattacks with little or no
human intervention. Teixeira et al. [41] proposed a scheme for
thwarting insiders attacks in IoT networks by crosschecking
data transformation of every IoT node.
III. PROPOSED TDTC MODEL
The proposed model comprises a dimension reduction module
and a classification module, to be discussed in sections III.A
and III.B, respectively.
Fig 2. In PCA, linear transformation is used to reduce high dimension dataset
to a low dimension dataset
A. Dimension Reduction Module
The dimension reduction module is deployed to address
limitations due to dimensionality that may lead to making
wrong decisions while increasing computational complexity of
the classifier. We deployed both Linear Discriminant Analysis
(LDA) (i.e. a supervised dimension reduction technique) and
Principal Component Analysis (i.e. an unsupervised dimension
reduction technique) in order to address the high dimensionality
issue. Principal Component Analysis (PCA) can be used to
perform feature selection and extraction [42]:
a) Feature selection: choose a subset of all features based
on their effectiveness in higher classification (i.e.
choosing more informative features)
b) Feature extraction: create a subset of new features by
combining existing features.
In TDTC, we used PCA as a feature extraction mechanism to
map the NSL-KDD dataset, which consists of 41 features to one
with a lower feature space by removing less significant features.
Feature extraction technique is commonly limited to linear
transforms: as shown in in Figure 2.
Let X be an N-dimensional random vector in the original
dataset, and the new feature space consists of lower M-
dimensions (M is the number of new dataset features that are
transformed) where ( ). For the transformation operation,
we will need to compute Eq. 1 to Eq.3:
Covariance matrix:
, (Eq.1)
Where m (mean vector) is:
(Eq.2)
Eigenvector-eigenvalue decomposition:
Where v=Eigenvector =Eigenvalue (Eq.3)
PCA will then sort the eigenvectors in descending order. In
other words, eigenvectors with lower eigenvalues have the least
information about the distribution of the data and these are the
eigenvectors we wish to drop. A common approach is to rank
the eigenvectors from the highest to the lowest eigenvalue and
choose the top eigenvectors based on eigenvalues. Similarly,
in TDTC, one may decide which eigenvalues are more useful;
thus, the ideal feature mapping matrix can be concluded and
used for linear transformation of training and test dataset.
At this layer of dimension reduction, Imbedded Error
Function (IEF) factor analysis measure [43] is used to select the
principal [44] as shown in Eq.4, where l, m denotes the number
of Principal Components (PCs). Both l and m are used to
represent the data and number of dimension, respectively. N
and denote the number of samples and Eigenvalues,
respectively.
(E q.4)
Cross Validation (CV) is used to evaluate optimum principals
with minimum errors as shown in Figure 3. Applying selection
criteria would reduce some features and help the next layer of
dimension reduction module to compute lower dimension
matrix and spreadable objects.
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