In terms of the Internet and communication, security is the fundamental challenging aspect. There are numerous ways to harm the security of internet users; the most common is phishing, which is a type of attack that aims to steal or misuse a user’s personal information, including account information, identity, passwords, and credit card details. Phishers gather information about the users through mimicking original websites that are indistinguishable to the eye. Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft. Therefore, there is a strong need to develop a system that efficiently detects phishing websites. Three distinct deep learning-based techniques are proposed in this paper to identify phishing websites, including long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and lastly an LSTM–CNN-based approach. Experimental findings demonstrate the accuracy of the suggested techniques, i.e., 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The proposed phishing detection method demonstrated by the CNN-based system is superior.

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. The phishing attacker spreads messages containing false URL links through emails, social media platforms, or messages, targeting people to steal sensitive data like credentials. Attackers generate phishing URLs that resemble those of legitimate websites to gain these confidential data. Hence, there is a need to prevent the siphoning of data through the duplication of trustworthy websites and raise public awareness of such practices. For this purpose, many machine learning and deep learning models have been employed to detect and prevent phishing attacks, but due to the ever-evolving nature of these attacks, many systems fail to provide accurate results. In this study, we propose a deep learning-based system using a 1D convolutional neural network to detect phishing URLs. The experimental work was performed using datasets from Phish-Tank, UNB, and Alexa, which successfully generated 200 thousand phishing URLs and 200 thousand legitimate URLs. The experimental results show that the proposed system achieved 99.7% accuracy, which was better than the traditional models proposed for URL-based phishing detection.

The rapid evaluation of smart cities has revolutionized the research and development field to a very extensive level which presents challenges in handling massive amounts of data. However, the integration of IoT into various aspects of life has introduced various challenges related to the security and privacy of IoT systems. IoT sensors capture large volumes of sensitive customer data, which can potentially make them a target and pose serious threats, including financial loss and identity theft. Strong intrusion detection systems are essential for protecting networked, data-driven ecosystems from potential cyber threats. In this paper, we propose a novel deep learning-based approach that focuses on emerging zero-touch networks that autonomously manage network resources to ensure network security, the proposed approach identifies various network intrusions such as DDoS, Botnet, Brute force, and Infiltration. Our proposed approach presents a major improvement in IoT security. We have used the CICIDS-2018 benchmark dataset and propose a deep learning-based network intrusion detection System for Zero Touch Networks (DL-NIDS-ZTN). The proposed study utilizes convolutional neural networks that correctly identify benign and malicious traffic and achieve 99.80% accuracy with the CICIDS-2018 dataset. By implementing the DL-NIDS-ZTN methodology, we aim to strengthen the security framework of smart cities and ensure the secure and seamless integration of IoT.

Phishing sites are increasingly causing harm to consumers, commercial enterprises, and the online infrastructure. Online safety is dependent on how well these evil sites can be detected in time and correctly. A number of solutions that exist are based on lexical, token features, or structural hints. Although useful to a certain degree, these methods tend to lose more contextual meaning in URLs. This paper presents SemanticPhishNet, a hybrid detection system that uses semantic knowledge to detect phishing attacks by utilizing semantic knowledge via a transformer-based system to process HTML documents and URL information to produce accurate and efficient phishing detections. The architecture uses MiniLM (identical type as distillbert) to obtain contextual embeddings of cleaned HTML and augmented text of URLs and a simple dense classifier to perform effective binary classification. A stratified three-way split of data was used to evaluate the model with real-world obfuscation patterns like replacement of “http” by “hxxp”. The experimental findings show that SemanticPhishNet has high performance in various measures, outperforming other state-of-the-art models in accuracy, recall and generalization ability. We conduct experiments on cross-validation and external validation with independent data. The framework exhibits good performance (96–97% cross-validation accuracy) and external evaluation demonstrates realistic generalization (67% accuracy), albeit revealing the difficulties of domain shift in phishing. The proposed model performs better than many of the existing models in the real world. The confusion matrices and ROC analysis indicate that the phishing and benign classes are consistently separated in both the validation and test sets. The findings show that the suggested model is efficient, stable, and scalable to the present-day phishing detection. The paper stresses the importance of appropriate evaluation techniques, such as leakage-aware splits and cross-dataset evaluation.

Detecting dangerous objects, such as firearms or knives, is crucial for public safety or accurate situational assessment in crime scenes in law enforcement applications. Drones as first responders have been actively utilized for this purpose, showing significant benefits in law enforcement with fast and early detection of such objects. However, automated detection is still challenging, particularly with low-quality drone cameras that operate in low illumination conditions. We evaluate the performance of four popular AI deep learning models to automate the detection of dangerous objects recorded from low-quality drone cameras. The results show that the YOLOv5s model achieves the best detection performance, yielding mAP50 results of 0.964 for color and 0.949 for infrared videos, which are excellent performances considering the low-quality and low-resolution dataset. The trained network model is further implemented as an online web application where law enforcement officers can upload videos taken from drones or CCTV.