Congestion Prediction in Internet of Things Network using Temporal Convolutional Network A Centralized Approach

The unprecedented ballooning of network traffic flow, specifically, Internet of Things (IoT) network traffic, has big stressed of congestion on todays Internet. Non-recurring network traffic flow may be caused by temporary disruptions, such as packet drop, poor quality of services, delay, etc. Hence, the network traffic flow estimation is important in IoT networks to predict congestion. As the data in IoT networks is collected from a large number of diversified devices which have unlike format of data and also manifest complex correlations, so the generated data is heterogeneous and nonlinear in nature. Conventional machine learning approaches unable to deal with nonlinear datasets and suffer from misclassification of real network traffic due to overfitting. Therefore, it also becomes really hard for conventional machine learning tools like shallow neural networks to predict the congestion accurately. Accuracy of congestion prediction algorithms play an important role to control the congestion by regulating the send rate of the source. Various deeplearning methods (LSTM, CNN, GRU, etc.) are considered in designing network traffic flow predictors, which have shown promising results. In this work, we propose a novel congestion predictor for IoT, that uses Temporal Convolutional Network (TCN). Furthermore, we use Taguchi method to optimize the TCN model that reduces the number of runs of the experiments. We compare TCN with other four deep learning-based models concerning Mean Absolute Error (MAE) and Mean Relative Error (MRE). The experimental results show that TCN based deep learning framework achieves improved performance with 95.52% accuracy in predicting network congestion. Further, we design the Home IoT network testbed to capture the real network traffic flows as no standard dataset is available

A Novel Technique for Secure Information Transmission in Videos Using Salt Cryptography

This paper presents a new technique for transmitting secret information securely from one party to another by embedding this information into a video after encryption through salt cryptography. We have tried to utilize the advantages of salt cryptography which has been ignored by data hiding community. In this encryption method some random data is added to the secret keys and passwords. We will define this random data as a salt which is needed to access the encrypted data, along with the password. Alone these passwords have no use since they will be able to locate the hidden data only when mixed with proper salt. This salt is managed by a certified third party. Different salt is created for different pairs of communicating parties. The purpose of salt is to add arbitrary random data to the string being hashed, such that you increase the length of input to hash. We have also introduced the concept of Enterprise Dependent Value (EDD), which are the embedding values corresponding to the binary digits and are specific to the communicating enterprises. The effectiveness of the techniques has been shown through experimental results. The performance of the proposed technique has been compared with the other techniques of watermarking, steganography and encryption. Keywords: Cryptography, Decryption , Encryption , Salt, Steganography , Video watermarkin

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Spectrophotometric Determination of Cilostazol in Tablet Dosage Form

Purpose: To develop simple, rapid and selective spectrophotometric methods for the determination of cilostazol in tablet dosage form. Methods: Cilostazol was dissolved in 50 % methanol and its absorbance was scanned by ultraviolet (UV) spectrophotometry. Both linear regression equation and standard absorptivity were calculated and both methods were validated as per ICH guidelines. Cilostazol was determined in tablet dosage form using these validated methods. Results: The λmax of cilostazol was 258.2 nm in 50 % methanol. Beer-Lambert’s law was obeyed in the concentration range of 0 – 25 μg/ml and standard absorptivity was 420.2 dL.g-1.cm-1 . The numerical values for all the validation parameters were within acceptable limits. The results of cilostazol tablet determination by linear regression equation and standard absorptivity methods indicate purity of 100.0 - 102.4 and 98.7 - 101.1 % with standard deviations of 0.611 and 0.592, respectively. Comparing the methods at 99 % confidence limit, the F-test value was found to be 1.065. Conclusion: These validated methods may be useful for routine analysis of cilostazol as bulk drugs, in dosage forms as well as in dissolution studies in the pharmaceutical industry