The connectivity of devices through the internet plays a remarkable role in our daily lives. Many network-based applications are utilized in different domains, e.g., health care, smart environments, and businesses. These applications offer a wide range of services and provide services to large groups. Therefore, the safety of network-based applications has always been an area of research interest for academia and industry alike. The evolution of deep learning has enabled us to explore new areas of research. Hackers make use of the vulnerabilities in networks and attempt to gain access to confidential systems and information. This information and access to systems can be very harmful and portray losses beyond comprehension. Therefore, detection of these network intrusions is of the utmost importance. Deep learning based techniques require minimal inputs while exploring every possible feature set in the network. Thus, in this paper, we present a one-dimensional convolutional neural network-based deep learning architecture for the detection of network intrusions. In this research, we detect four different types of network intrusions, i.e., DoS Hulk, DDoS, and DoS Goldeneye which belong to the active attack category, and PortScan, which falls in the passive attack category. For this purpose, we used the benchmark CICIDS2017 dataset for conducting the experiments and achieved an accuracy of 98.96% as demonstrated in the experimental results.

The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIA_v1 and CASIA_v2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIA_v1 and CASIA_v2 datasets. Since the CASIA_v2 dataset is more comprehensive compared to the CASIA_v1 dataset, we obtained 99.3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIA_v2 dataset. The results show the superiority of the proposed system.

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.

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