This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresses the source of the above issues through detecting an intrusion in cloud computing before it further disrupts normal network operations, because the complexity of malicious attack techniques have evolved from traditional malicious attack technologies (direct malicious attack), which include different malicious attack classes, such as DoS, Probe, R2L, and U2R malicious attacks, especially the zero-day attack in online mode. The proposed online intrusion detection cloud system (OIDCS) adopts the principles of the new spiking neural network architecture called NeuCube algorithm. It is proposed that this system is the first filtering system approach that utilizes the NeuCube algorithm. The OIDCS inherits the hybrid (supervised/unsupervised) learning feature of the NeuCube algorithm and uses this algorithm in an online system with lifelong learning to classify input while learning the system. The system is accurate, especially when working with a zero-day attack, reaching approximately 97% accuracy based on the to-be-remembered (TBR) encoding algorithm.

Steganography is the art of hiding a secret message in some kind of media. The main goal is not to hide only the secret message but also the existence of communication and secure data transferring. There are a lot of methods that were utilized for building the steganography; such as LSB (Least Significant Bits), Discrete Cosine Transform (DCT), Discrete Fourier Transform, Spread-Spectrum Encoding, and Perceptual Masking, but all of them are challenged by steganalysis. This paper proposes a new technique for Gray Scale Image Steganography that uses the idea of image segmentation and LSB to deal with such problem. The proposed method deals with different types of images by converting them to a virtual gray scale 24 bitmaps, finds out the possible segments inside image and then computes the possible areas for each segment with boundaries. Any intruder trying to find the transformed image will not be able to understand it without the correct knowledge about the transformation process. The knowledge is represented by the key of image segmentation, key of data distribution inside segment (area selection), key of mapping within each area segment, key agreement of cryptography method, key of secret message length and key of message extension. Our method is distinguishing oneself by one master key to generate the area selection key, pixels selection keys and cryptography key. Thus, the existence of secret message is hard to be detected by the steganalysis. Experiment results show that the proposed technique satisfied the main requirements of steganography; visual appearance, modification rate, capacity, undetectability, and robustness against extraction (security). Also it achieved the maximum capacity of cover image with a modification rate equals 0.04 and visual quality for stego-image comparable to cover image.

Steganography is a technique for hiding data in media in a way that makes its existence hard to detect. Text files are a preferable format for use in steganography due to the small storage size of such files. This paper presents an Arabic text steganographic algorithm based on Unicode. The algorithm imposes a minimal change on connected letters without any change in size and shape. The experiment resulted in a high capacity rate ratio of about 180 bit/KB and low modification rate less than 90 letters/KB.

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This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresses the source of the above issues through detecting an intrusion in cloud computing before it further disrupts normal network operations, because the complexity of malicious attack techniques have evolved from traditional malicious attack technologies (direct malicious attack), which include different malicious attack classes, such as DoS, Probe, R2L, and U2R malicious attacks, especially the zero-day attack in online mode. The proposed online intrusion detection cloud system (OIDCS) adopts the principles of the new spiking neural network architecture called NeuCube algorithm. It is proposed that this system is the first filtering system approach that utilizes the NeuCube algorithm. The OIDCS inherits the hybrid (supervised/unsupervised) learning feature of the NeuCube algorithm and uses this algorithm in an online system with lifelong learning to classify input while learning the system. The system is accurate, especially when working with a zero-day attack, reaching approximately 97% accuracy based on the to-be-remembered (TBR) encoding algorithm.