An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Online review platforms are becoming increasingly popular,encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services.Using Sybil accounts,bot farms,and real account purchases,immoral actors demonize rivals and advertise their goods.Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years.The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones.This paper adopts a semi-supervised machine learning method to detect fake reviews on any website,among other things.Online reviews are classified using a semi-supervised approach(PU-learning)since there is a shortage of labeled data,and they are dynamic.Then,classification is performed using the machine learning techniques Support Vector Machine(SVM)and Nave Bayes.The performance of the suggested system has been compared with standard works,and experimental findings are assessed using several assessment metrics.

A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems

Recently,automotive intrusion detection systems(IDSs)have emerged as promising defense approaches to counter attacks on in-vehicle networks(IVNs).However,the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation.Despite the availability of several datasets for automotive IDSs,there has been a lack of comprehensive analysis focusing on assessing these datasets.This paper aims to address the need for dataset assessment in the context of automotive IDSs.It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs,to evaluate the quality of datasets.These metrics take into consideration various aspects such as dataset description,collection environment,and attack complexity.This paper evaluates eight commonly used datasets for automotive IDSs using the proposed metrics.The evaluation reveals biases in the datasets,particularly in terms of limited contexts and lack of diversity.Additionally,it highlights that the attacks in the datasets were mostly injected without considering normal behaviors,which poses challenges for training and evaluating machine learning-based IDSs.This paper emphasizes the importance of addressing the identified limitations in existing datasets to improve the performance and adaptability of automotive IDSs.The proposed metrics can serve as valuable guidelines for researchers and practitioners in selecting and constructing high-quality datasets for automotive security applications.Finally,this paper presents the requirements for high-quality datasets,including the need for representativeness,diversity,and balance.

Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.

A NEW ML DETECTION ALGORITHM FOR ORTHOGONAL MULTICODE SYSTEM IN NAKAGAMI FADING CHANNEL

Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent detection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoretical analysis and simulation results are presented. It is shown that the proposed ML algorithm is at least 0.7 dB better than the conventional Matched-Filter (MF) algorithm for uncoded systems, in both non-fading and fading channels. For the consideration of practical application, it is further simplified in complexity. Compared with the original ML algorithm, the simplified ML algorithm can provide significant reduction in complexity with small degradation in performance.

Comparison of Two Neural Networks in MC-CDMA Multiuser Detection

MC CDMA is a thriving topic in recent years. Multiuser interference is also very severe as in DS CDMA. ML method is the best multiuser detection, but it has a computational complexity exponentially increased with the number of users. Mean field annealing and chaotic neural network are two promising optimum techniques. This paper applies them into the ML detection, comparison of the two methods is made.

Novel K-best detection algorithms for MIMO system

Aiming at the optimum path excluding characteristics and the full constellation searching characteristics of the K-best detection algorithm, an improved-performance K-best detection algorithm and several reduced-complexity K-best detection algorithms are proposed. The improved-performance K-best detection algorithm deploys minimum mean square error （MMSE） filtering of a channel matrix before QR decomposition. This algorithm can decrease the probability of excluding the optimum path and achieve better performance. The reducedcomplexity K-best detection algorithms utilize a sphere decoding method to reduce searching constellation points. Simulation results show that the improved performance K-best detection algorithm obtains a 1 dB performance gain compared to the K- best detection algorithm based on sorted QR decomposition （SQRD）. Performance loss occurs when K = 4 in reduced complexity K-best detection algorithms. When K = 8, the reduced complexity K-best detection algorithms require less computational effort compared with traditional K-best detection algorithms and achieve the same performance.

Maximum Likelihood Detection for Detect-and-Forward Relay Channels

This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).

Fall detection system in enclosed environments based on single Gaussian model

In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.

AN EFFICIENT APPROXIMATE MAXIMUM LIKELIHOOD SIGNAL DETECTION FOR MIMO SYSTEMS

This paper proposes an efficient approximate Maximum Likelihood (ML) detection method for Multiple-Input Multiple-Output (MIMO) systems,which searches local area instead of exhaustive search and selects valid search points in each transmit antenna signal constellation instead of all hy-perplane. Both of the selection and search complexity can be reduced significantly. The method per-forms the tradeoff between computational complexity and system performance by adjusting the neighborhood size to select the valid search points. Simulation results show that the performance is comparable to that of the ML detection while the complexity is only as the small fraction of ML.

AN ENHANCED DETECTION ALGORITHM FOR V-BLAST SYSTEM

A decoding method complemented by Maximum Likelihood (ML) detection for V-BLAST (Verti- cal Bell Labs Layered Space-Time) system is presented. The ranked layers are divided into several groups. ML decoding is performed jointly for the layers within the same group while the Decision Feedback Equalization (DFE) is performed for groups. Based on the assumption of QPSK modulation and the quasi-static flat fading channel, simulations are made to testify the performance of the proposed algorithm. The results show that the algorithm outperforms the original V-BLAST detection dramatically in Symbol Error Probability (SEP) per- formance. Specifically, Signal-to-Noise Ratio (SNR) improvement of 3.4dB is obtained for SEP of 10?2 (4×4 case), with a reasonable complexity maintained.

ML辅助网络自动化系统的对抗样本攻击方法

针对网络自动化系统中用于异常检测的基于机器学习(Machine Learning,ML)的分类器,提出了一种黑盒对抗样本攻击方法来误导分类器输出错误的分类结果。首先,设计了一种对抗样本生成算法来人工合成替代分类器的训练数据集,算法不仅基于一组仅包含“正常”类型的合法遥测数据生成涵盖所有异常类型的合成数据,而且标记数据时还最小化对目标分类器的查询次数。然后,利用对抗样本去攻击了系统中基于ML的分类器,分析了对抗样本给分类器性能带来的影响,并将结果推广到不同结构的ML模型。最后,利用从真实的IP-over-EON多层网络测试平台采集的遥测数据进行了仿真实验,实验结果验证了该方法的有效性。

基于Duffing振子和ML的微弱信号幅值估计新方法

基于Du ffing振子和最大似然参量估计方法,提出一种微弱正弦信号幅值估计的新方法。介绍了新方法的原理和具体实现过程。将混有噪声的待测信号送入Du ffing系统,依据大周期工作状态下Du ffing系统具有优良的信噪比改善特性,采用最大似然法估计Du ffing系统的输出信号幅值,进一步由系统输入输出之间的关系确定输入的微弱正弦信号的幅值。通过仿真实验,对该方法和最大似然法直接用于微弱正弦信号幅值估计的结果进行了对比。实验结果表明:该方法明显提高了估计精度。

空间调制信号的改进M-ML检测算法

空间调制(SM)系统的最大似然(ML)最优检测算法的计算复杂度很高,具有较低计算复杂度的M-ML检测算法受到了人们的关注.M-ML算法按照接收天线序号由小到大的顺序进行检测,从误比特率性能角度考虑并不是最佳的.通过研究不同检测顺序对算法性能的影响,提出了两个改进的M-ML算法,仿真结果表明改进的M-ML算法在误比特率性能上优于M-ML算法.由于M-ML算法在不同的信噪比下每层保留固定的节点数M,尤其在高信噪比时会造成计算资源的浪费,因此提出一种动态M-ML算法,即通过门限值自适应选择每层保留的节点数.仿真结果表明动态M-ML算法降低了M-ML算法的计算复杂度,同时性能逼近M-ML算法.

ML-OSIC检测的快速递归算法

针对垂直分层空时(V-BLAST)结构,研究MIMO-OFDM系统的信号检测问题.根据矩阵伪逆的递推关系,提出了一种基于最大似然-排序串行干扰抵消(ML-OSIC)算法的简化处理方案,依次计算每一次迭代的迫零加权矩阵和加权向量.与传统的ML-OSIC算法相比,笔者给出的快速递归算法既可以基本保证检测算法的最优性,又能获得更快的处理速度和更低的计算复杂度.

基于数据包头序列的物联网恶意流量检测

现有的基于机器学习(ML)的恶意流量检测方法,通常以高维的流量特征作为输入,并采用复杂模型,在实践中产生高误报率且资源占用较高。更重要的是,加密协议的广泛使用,使得数据包有效载荷特征很难被访问。幸运的是,物联网(IoT)设备的网络行为通常是有规律和周期性的,该特征反映在通信数据包序列上,每个数据包一定程度上描述了一次网络事件。基于此,本文提出了基于数据包头序列的恶意流量检测方法。它将流量序列转换为网络事件序列,并计算一组特征(即序列性、频率性、周期性和爆发性)来描述网络行为。实验环境包含一组真实的物联网设备,并将提出的方法部署在树莓派模拟的网关上。实验结果表明,与最新的检测方法相比,本文提出的方法能够在复杂网络环境下保持高准确性和低误报率,并提升了处理速率。

E-Commerce Fraud Detection Based on Machine Learning Techniques:Systematic Literature Review

The e-commerce industry’s rapid growth,accelerated by the COVID-19 pandemic,has led to an alarming increase in digital fraud and associated losses.To establish a healthy e-commerce ecosystem,robust cyber security and anti-fraud measures are crucial.However,research on fraud detection systems has struggled to keep pace due to limited real-world datasets.Advances in artificial intelligence,Machine Learning(ML),and cloud computing have revitalized research and applications in this domain.While ML and data mining techniques are popular in fraud detection,specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth.Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context.To bridge this gap,our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis(PRISMA)methodology.We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape.Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs.Through our investigation,we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud.Our paper examines the research on these techniques as published in the past decade.Employing the PRISMA approach,we conducted a content analysis of 101 publications,identifying research gaps,recent techniques,and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.

GO-MC-CDMA上行链路ML多用户检测研究

正交分组多载波码分多址(GO-MC-CDMA)每个用户组是独立的MC-CDMA系统,合理选择组内载波数使得最大似然(ML)算法切实可行。分析了GO-MC-CDMA上行链路在考虑载波频偏(CFO)的情况下采用最大似然序列估计(MLSE)检测的系统性能;比较了不同用户数和不同数字调制方式对误比特率(BER)的影响。数值仿真结果表明:载波频偏在一定范围内时,采用ML多用户检测的GO-MC-CDMA系统具有较好的对抗载波频偏能力和优异的误比特率性能;用户数目和调制方式对误比特率影响较大。

空时频移键控(ST-FSK)的分离ML信号检测方法

该文提出一种分离最大似然(ML)信号检测方法,在信道状态信息已知的假设下,利用信号的正交性特点, 使多个接收矢量的ML联合检测问题分离为若干个矢量的单独ML检测问题。若采用合适的信道估值算法,在运算量上不仅大大低于非相干检测,还能获得性能的提高。仿真实验验证了算法的有效性。

基于PSO的ML-PDA算法及其并行实现

针对密集杂波条件下的目标检测与跟踪问题,开展极大似然-概率数据关联(maximum likelihoodprobabilistic data association,ML-PDA)算法优化与实时计算问题研究。在算法层面,通过在极大化对数似然比(log likelihood ratio,LLR)过程中引入粒子群优化(particle swarm optimization,PSO)方法,并进一步提出基于观测引导的PSO播撒粒子方式,提升算法的计算效率;在实现层面,提出基于图形处理器(graphic processing unit,GPU)的PSO实现策略。仿真实验结果说明了基于观测引导PSO算法搜索的有效性。在GPU平台上实现该算法获得显著的加速比,验证了所提出方法具有工程实时性。

基于ML改进技术的IDS的设计与实现

网络入侵检测系统(IDS)是放置在比较重要的网段内或主机上,不停地监视各种传输数据包以及系统审计日志,进行智能分析与判断目的性攻击的系统,是当前网络安全研究的热点问题之一。文中将机器学习(ML)技术加入IDS的检测之中,不仅可以建立已知攻击的特征轮廓,还能检测出其变体和未知攻击,是对入侵检测技术的一个扩展。同时以Sniffer捕获数据为基础数据包,设计并实现了一个基于改进支持向量机(SVM)核函数技术的IDS。通过实验数据对比,说明了该系统在日志分析以及网络嗅探方面的有效性,以及其在时间复杂度等方面的高效性。