International Journal of Computer and Applications

Distributed denial-of-service (DDoS) attacks became one of the main Internet security problems over the last decade, threatening public web servers in particular. Although the DDoS mechanism is widely understood, its detection is a very hard task because of the similarities between normal traffic and useless packets, sent by compromised hosts to their victims. This work presents a lightweight method for DDoS attack detection based on traffic flow features, in which the extraction of such information is made with a very low overhead compared to traditional approaches. This is possible due to the use of the NOX platform which provides a programmatic interface to facilitate the handling of switch information. Other major contributions include the high rate of detection and very low rate of false alarms obtained by flow analysis using Self Organizing Maps.

Lightweight DDoS flooding attack detection using NOX/OpenFlow

Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model

The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

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Applications of artificial intelligence in transport: an overview

Machine learning technology in biodiesel research: A review

The remote sensing (RS) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial intelligence (AI), provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system. Without being explicitly programmed, machine learning models learn from training data, i.e., past experience. Machine learning, when applied to remotely sensed data, has the potential to evolve a real-time farm-specific management system to reinforce farmers' ability to make appropriate decisions. Recently, the use of machine learning techniques combined with RS data has reshaped precision agriculture in many ways, such as crop identification, yield prediction and crop water stress assessment, with better accuracy than conventional RS methods. As agriculture accounts for approximately 70% of the worldwide water withdrawals, it must be used in the most efficient way to obtain maximum yields and food production. The use of water management and irrigation based on plant water stress have been demonstrated to not only save water but also increase yield. To date, RS and ML-based results have encouraged farmers and decision-makers to adopt this technology to meet global food demands. This phenomenon has led to the much-needed interest of researchers in using ML to improve agriculture outcomes. However, the use of ML for the potential evaluation of water stress continues to be unexplored and the existing methods can still be greatly improved. This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.

Remote sensing and machine learning for crop water stress determination in various crops: a critical review

A blockchain is a distributed ledger of records called as blocks. These blocks are linked using cryptographic hash. Each block contains a hash of the previous block, a timestamp, and transaction data. Consensus layer is the main layer in Blockchain Architecture, in which consensus protocol is configured to decide how new block is added in blockchain. Consensus algorithm solves the problem of trust in blockchain. Consensus algorithms can be classified into two classes. The first class is voting-based consensus, which requires nodes in the blockchain network to broadcast their results of mining a new block or transaction, before appending the block to blockchain. The second class is proof-based consensus, which requires the nodes joining the blockchain network to solve and mathematical puzzle to show that they are more eligible than the others to do the appending or mining work. Performance of blockchain can be increased with the use of suitable consensus algorithm. However, theory and data support for the selecting suitable consensus in private blockchain is very limited. This paper contributes theory and data used for selecting suitable consensus algorithm and would help researchers for further exploring of consensus in private blockchain environment.

Survey on Private Blockchain Consensus Algorithms

In today’s world, almost everybody is affluent with computers and network based technology is growing by leaps and bounds. So, network security has become very important, rather an inevitable part of computer system. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied to intrusion detection systems which have an important role in detecting Intrusions. This paper reviews different machine approaches for Intrusion detection system. This paper also presents the system design of an Intrusion detection system to reduce false alarm rate and improve accuracy to detect intrusion.

/pdf/survey-on-intrusion-detection-system-using-machine-learning-sfpbrvm0dz.pdf

Survey on Intrusion Detection System using Machine Learning Techniques

With the rapid expansion of computer usage and computer network the security of the computer system has became very important. Every day new kind of attacks are being faced by industries. Many methods have been proposed for the development of intrusion detection system using artificial intelligence technique. In this paper we will have a look at an algorithm based on neural networks that are suitable for Intrusion Detection Systems (IDS) [1] [2]. The name of this algorithm is “Self Organizing Maps” (SOM). Neural networks method is a promising technique which has been used in many classification problems. The neural network component will implement the neural approach, which is based on the assumption that each user is unique and leaves a unique footprint on a computer system when using it. If a user's footprint does not match his/her reference footprint based on normal system activities, the system administrator or security officer can be alerted to a possible security breach. At the end of the paper we will figure out the advantages and disadvantages of Self Organizing Maps and explain how it is useful for building an Intrusion Detection System.

Intrusion Detection System using Self Organizing Maps

In past decade, the problem of traffic has become severe due to industrialization especially in big cities. Hence, the urban population has to invest much valuable time during traveling.

V. K. Pachghare

Papers

Remote sensing and machine learning for crop water stress determination in various crops: a critical review

Survey on Private Blockchain Consensus Algorithms

Survey on Intrusion Detection System using Machine Learning Techniques

Intrusion Detection System using Self Organizing Maps

Road traffic prediction and congestion control using Artificial Neural Networks