Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the deficiency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.

Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.

Floods are considered one of Malaysia’s most detrimental natural hazards in terms of frequency, extent, duration, damage, and population affected. Consequently, flood risk reduction has become a popular research topic in recent years. Despite various efforts made, flood mitigation strategies within the Malaysian government sectors remain ineffective in alleviating the negative impacts of flooding. As a result, this paper aims to examine the current challenges faced by Malaysian disaster management agencies and their recommendations for reducing the disastrous effects of floods. To achieve this, a qualitative approach was employed through nine focus group discussions comprising a total of 48 participants. Inductive thematic analysis was used to categorize these challenges into several groups under different phases of the disaster management cycle. The obtained results revealed that most challenges faced by Malaysian government agencies were from the pre-disaster phase. Several recommendations were suggested to overcome these challenges and to obtain an effective disaster management capability. In addition, the findings also indicated that although Artificial Intelligence (AI) has great potential in flood risk reduction, AI integration into flood disaster management is still at an early stage in Malaysia. Outcomes from this study are expected to provide valuable insights to support decision-makers in handling flood events more effectively, ultimately saving lives and reducing the damaging impacts of floods in Malaysia.