A study of caesar cipher and transposition cipher in jawi messages

Cryptography known as art and science which is used to hide the messages that contain a few policy terminologies. These terminologies in cryptography are plaintext/ messages, ciphertext, encryption, decryption and key. Encryption is a proses to transform the plaintext together with key into ciphertext. Decryption is the reverse process of encryption. Caesar cipher and transposition cipher are two historical ciphers in cryptography. Caesar cipher is a monoalphabetic cipher. It is a substitution cipher which replace each letter in plaintext with another letter to form the ciphertext. Transposition cipher uses a technique which rearrangement letters in plaintext with a keyword and produce the ciphertext. Caesar cipher and Tansposition cipher both are commonly used to encrypt the English letters. The output of encrypted of English letters are known as ciphertext. The attacker can easily cryptanalysed the Caesar cipher by observing the frequency distribution English letters and ciphertext. For Transposition cipher, the cipher can be cracked by knowing the keyword. To date, there is no any research encrypt Jawi letters using Caesar cipher and Transposition cipher. Hence, in this paper encryption and decryption by using Caesar cipher and Transposition cipher in Jawi messages are proposed. Next, the security level of Caesar cipher and Transposition cipher in Jawi messages are compared. The result has shown that both ciphers are still not secure to protect the confidentiality of the Jawi messages

A stylometry approach for blind linguistic steganalysis model against translation-based steganography

Steganography is the art of hiding information in ways that prevent the detection of a secret message. In Translation-based Steganography (TBS), the secret messages are encoded in the “noise” made via translation of natural language text programmed. The adversarial technique to extract the secret message is called steganalysis, which can be categorized into two types; targeted vs. blind. While targeted steganalysis is designed to attack a specific embedding algorithm, blind steganalysis use features extracted or selection from the medium to detect any anomalies that indicate a possibility that a secret data has been embedded within the medium. However, accuracy of blind steganalysis algorithms highly depend on the features selected from the input data especially when attacking embedding techniques in TBS. This thesis explore the potential of using stylometry or linguistic style to improve the representation of characteristics among the word distribution in distinguishing the stego text from the cover text for TBS. This is because all translated in TBS text have an intrinsic structural styles that can be used to improve the performance of a blind steganalysis model. The proposed stylometry-based blind steganalysis model consists of two stages, which are stylometric feature selection and classification. The proposed stylometric features selected from a set of cover text are categorized into two group features; lexical and syntactic features before implemented into the model Support Vector Machine (SVM) as the classifier. The performance of the stylometry-based blind steganalysis model is then evaluated based on all false rate, missing rate and accuracy rate and compared against three other standard classifiers in steganalysis; Naive Bayes (NB), k-Nearest Neighbor (k-NN), and Decision Tree (J48). The results showed that the stylometric features are impactful to a blind steganalysis model by giving higher detection performance. Meanwhile, SVM is the best classifier for stego text detection with significantly low processing time performanc