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Shill attacks are a serious threat to the stability of filtering and recommendation systems. These attacks involve the injection of fake profiles into the system, which can compromise the reliability of system output. Several shilling attack detection techniques have been proposed, but they often have limitations in terms of accuracy. This works presents a enhanced method for detecting attacks in collaborative recommender systems. The proposed method is based on a combination of statistical and machine learning techniques. The statistical techniques are used to identify anomalous user behavior, while the machine learning techniques are used to classify users as either malicious or benign. The main contribution of the proposed method is the use of a hybrid approach that combines the strengths of statistical and machine learning techniques. The statistical techniques are able to identify anomalous user behavior that is not easily detected by machine learning techniques. The machine learning techniques are able to classify users as either malicious or benign with a high degree of accuracy. The proposed method was evaluated on a real-world dataset. The results showed that the proposed method was able to detect attacks with a high degree of accuracy. The proposed method uses a combination of ensemble learning and feature selection to achieve better accuracy than previous methods. The results of the experiments show that the proposed method can achieve an accuracy of up to 99%.

The stability and reliability of filtration and recommender systems are crucial for continuous operation. The presence of fake profiles, known as “shilling attacks,” can undermine the reliability of these systems. Therefore, it is important to detect and classify these attacks. Numerous techniques for detecting shilling attacks have been proposed, including supervised, semi-supervised, unsupervised, Deep Learning, and hyper deep learning methods. These techniques utilize well-known shilling attack models to target collaborative recommender systems. While previous research has focused on evaluating shilling attack strategies from a global perspective, considering factors such as attack size and attacker’s knowledge, there is a lack of comparative studies on the various existing and commonly used attack detection methods. This paper aims to fill this gap by providing a comprehensive survey of shilling attack models, detection attributes, and detection algorithms. Furthermore, we explore the traits of injected profiles that are exploited by detection algorithms, which has not been thoroughly investigated in prior works. We also conduct experimental studies on popular attack detection methods. Our experimental results reveal that hybrid deep learning algorithms exhibit the highest performance in shilling detection, followed by supervised learning algorithms and semi-supervised learning algorithms. In contrast, the unsupervised technique performs poorly. The deep learning-based Shilling Attack Detection demonstrates accuracy and quality in accurately identifying a variety of mixed attacks. This study provides valuable insights into shilling attack models, detection attributes, and detection algorithms. Our findings highlight the superior performance of hybrid deep learning algorithms in shilling detection, as well as the limitations of unsupervised techniques. Deep learning-based Shilling Attack Detection showcases its effectiveness and accuracy in identifying various types of attacks.

In artificial neural networks, activation functions play a significant role in the learning process. Choosing the proper activation function is a major factor in achieving a successful learning performance. Many activation functions are sufficient universal approximators, but their performance is lacking. Thus, many efforts have been directed toward activation functions to improve the learning performance of artificial neural networks. However, the learning process involves many challenges, such as saturation, dying, and exploding/vanishing the gradient problems. The contribution of this work resides in several axes. First, we introduce two novel activation functions: absolute linear units and inverse polynomial linear units. Both activation functions are augmented by an adjustable parameter that controls the slope of the gradient. Second, we present a comprehensive study and a taxonomy of various types of activation functions. Third, we conduct a broad range of experiments on several deep neural architecture models with consideration of network type and depth. Fourth, we evaluate the proposed activation functions’ performance in image and text classification tasks. For this purpose, several public benchmark datasets are utilized to evaluate and compare the performance of the proposed functions with that of a group of common activation functions. Finally, we deeply analyze the impact of several common activation functions on deep network architectures. Results reveal that the proposed functions outperform most of the popular activation functions in several benchmarks. The statistical study of the overall experiments on both classification categories indicates that the proposed activation functions are robust and superior among all the competitive activation functions in terms of average accuracy.