# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
## 2 Related Work and Background  
### Related Work  
In unsupervised detection methods, several statistical based metrics are firstly proposed by analyzing the difference of rating patterns among user profiles [9]. Attack profiles with high filler sizes can be successfully detected by this method. By calculating the principal components in the rating database, the PCA-based detection method [10] has effective detection performance. However, this detection method assumes that attack size is known. In practice, the prior knowledge such as attack size is not easy to get. Based on the theory of Beta distribution, the Beta-Protection detection method is proposed [11]. This method can identify certain types of attack profiles. However, this method has poor detection performance when facing recommendation attack with large-scale attack sizes. The clustering-based detection methods [12; 13; 14; 15; 16] assume that the test sets contain both genuine and attack profiles at the same time. Then, the test sets are clustered to different clusters for the detection. However, in practical applications the test sets may only contain genuine profiles or attack profiles. In these cases, the assumption of these methods can no longer be satisfied.  
In semi-supervised detection methods, a detection method based on Bayesian classifier is firstly proposed [17]. After enhancement training with unlabeled user profiles, this method has high recall but low precision when detecting the attack. After that, the ensemble learning method is used to improve the detection performance [18]. However, these existing methods require the features of user profiles which are extracted manually.  
In supervised detection methods, three famous machine learning algorithms are firstly trained to construct classifiers for detecting the attack [19]. Although, these methods have high recall, they suffer from low precision. Based on a variant of AdaBoost algorithm, a detection method RadaBoost is proposed. RadaBoost is used to improve the performance for detecting the attack with imbalanced samples [20]. The SVM-based detection method is proposed in paper [21]. In this work, the genuine and attack profiles are first balanced before being used for training. The precision is improved by analyzing the target item analysis. One drawback of this method is that it is a challenging task to determine the target item since the rating database usually contains a large number of items. For improving the detection performance of single classifier, the trust features are extracted and used to train an SVM-based classifier [22]. However, this work also faces the challenge of determining target items. Recently, the methods based on the technologies of deep learning are proposed to detect the attack [23; 24; 25; 26; 27]. Deep neural networks, which contain single convolutional layer, multiple convolutional layers, or hybrid layers, are designed to establish detection methods, respectively. These deep learning-based detection methods do not require the hand-designed features. Instead, they learn the features of recommendation attack, automatically. Benefit from the strong learning ability of deep learning, most of these methods usually have excellent detection performance. However, the key hyperparameters of the existing deep learning-based detection methods are optimized by manual analysis which relies too much on domain experts and their experience.