Sentiment analysis is the study, to classify the text based on customer reviews which can provide valuable information to improve business. Previously the analysis was carried out based on the information provided by the customers using natural language processing and machine learning. In this paper, sentiment analysis on IMDB movie reviews dataset is implemented using Machine Learning (ML) and Deep Learning (DL) approaches to measure the accuracy of the model. ML algorithms are the traditional algorithms that work in a single layer while deep learning algorithms work on multilayers and gives better output. This paper helps the researchers to identify the best algorithm for sentiment analysis. The comparison of the machine learning and deep learning approaches shows that DL algorithms provide accurate and efficient results.

Probabilistic Packet Marking algorithm suggests a methodology to identify all the participated routers of the attack path by probabilistically marking the packets. In this approach, these marked packets contain partial information regarding the routers of the attack path. At receiver, to get the complete information of every router, it requires more number of marked packets and hence more combinations and more false positives. To overcome this drawback we have presented a novel idea in finding the exact IP address of the routers in the attack path by applying Chinese Remainder Theorem. The result of our implementation reveals that our idea requires less number of marked packets and takes no time in constructing the attack path. The same idea is true even in the case of multiple attackers

Today the frequency of technological transformations is very high. In order to cope up with these, there is a demand for fast processing and secured algorithms should be proposed for data exchange. In this paper, Advanced Encryption Standard (AES) is modified using DNA cryptography for fast processing and dynamic S-boxes are introduced to develop an attack resistant algorithm. This is strengthened by combining symmetric and asymmetric algorithms. Diffie-Hellman key exchange is used for AES key generation and also for secret number generation used for creation of dynamic S-boxes. The proposed algorithm is fast in computation and can resist cryptographic attacks like linear and differential cryptanalysis attacks.

Denial of service (DOS) attack is one of the most common attacks on the internet. The most difficult part of this attack is to find the source of the denial of service (DOS) attack. Savage et al. proposed PPM algorithm to traceback the route to the attacker. We found two disadvantages of the Savage traceback technique. The first disadvantage is probability of finding of far away routers is very less which results in losing some of the routers identity. This affects the attack graph construction. The second disadvantage is, because of re-marking of the edges the constructed graph contain new edges which do not exist in attack graph. In this paper, we propose a modified probabilistic packet marking (MPPM) IP traceback methodology and we found that the results are quite interesting when compared with the approach proposed by Savage.