Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks today.In MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation.Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET.However,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of misclassification.Therefore,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models.Here,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of classifier.Then,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier.Moreover,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations.During evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

A complete genome assembly of Glaciecola mesophila sp. nov. sequenced by using BIGIS-4 sequencer system

Using a pyrosequencing-based custom-made sequencer BIGIS-4, we sequenced a Gram-negative bacterium Glaciecola mesophila sp. nov. (Gmn) isolated from marine invertebrate specimens. We generated 152043 sequencing reads with a mean high-quality length of 406 bp, and assembled them using the BIGIS-4 post-processing module. No systematic low-quality data was detected beyond expected homopolymer-derived errors. The assembled Gmn genome is 5144318 bp in length and harbors 4303 annotated genes. A large number of metabolic genes correspond to various nutrients from surface marine invertebrates. Its abundant cold-tolerant and cellular signaling and related genes reveal a fundamental adaptation to low-temperature marine environment.