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Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting abnormal network behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, such as denial of service, spam, phishing, and other types of attacks, are reported daily, and the number of attacks is growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposes an anomaly intrusion detection system method based on a new RNA encoding method and the ResNet50 Model, where encoding is performed by splitting the training records into different groups. These groups are protocol, service, flag, and digit, and each group is represented by the number of RNA characters that can represent the group's values. The RNA encoding phase converts network traffic records into RNA sequences, allowing for a comprehensive representation of the dataset. The detection model, which uses the ResNet architecture, effectively addresses training challenges and achieves high detection rates for different attack types. The KDD-Cup99 dataset is used for both training and testing. The testing dataset includes new attacks that do not appear in the training dataset, which means that the system can detect new attacks in the future. The efficiency of the suggested anomaly intrusion detection system is determined by calculating the detection rate (DR), false alarm rate (FAR), and accuracy. The achieved DR, FAR, and accuracy are 96.24%, 6.133%, and 95.99%, respectively. The experimental results revealed that the RNA encoding method can improve intrusion detection. [ABSTRACT FROM AUTHOR]

Cryptography is a method used to mask text based on any encryption method, and the authorized user only can decrypt and read this message. An intruder tried to attack in many manners to access the communication channel, like impersonating, non-repudiation, denial of services, modification of data, threatening confidentiality and breaking availability of services. The high electronic communications between people need to ensure that transactions remain confidential. Cryptography methods give the best solution to this problem. This paper proposed a new cryptography method based on Arabic words; this method is done based on two steps. Where the first step is binary encoding generation used to encode messages (characters, numbers, and special characters) to binary numbers. Three encoding ideas are proposed for this reason. At the same time, the second step is used Arabic language words to represent the obtained binary sequences to achieve the final secret text. The obtained results exhibited that sending a text containing secret messages in a different language (Arabic) cannot be suspicious when read. [ABSTRACT FROM AUTHOR]

With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition. [ABSTRACT FROM AUTHOR]

Recent researches showed that DNA encoding and pattern matching can be used for the intrusion-detection system (IDS), with results of high rate of attack detection. The evaluation of these intrusion detection systems is based on datasets that are generated decades ago. However, numerous studies outlined that these datasets neither inclusively reflect the network traffic, nor the modern low footprint attacks, and do not cover the current network threat environment. In this paper, a new DNA encoding for misuse IDS based on UNSW-NB15 dataset is proposed. The proposed system is performed by building a DNA encoding for all values of 49 attributes. Then attack keys (based on attack signatures) are extracted and, finally, Raita algorithm is applied to classify records, either attacks or normal, based on the extracted keys. The results of the current experiment showed that the proposed system achieved good detection rates for all of attacks, which included the Analysis, Backdoor, DoS, Exploits, Fuzzers, Generic, Reconnaissance, Shellcode, and Worms, with values of 82.56%, 92.68%, 75.59%, 75.42%, 67%, 99.28%, 81.02%, 73.6%, 85%, and 90.91%, respectively. The values of false alarm rate and accuracy were equal to 24% and 89.05%, respectively. Also, the execution time for the proposed system was found to be short, where the values of the encoding time and matching time for one record were 0.45 and 0.002 second, respectively. [ABSTRACT FROM AUTHOR]