Security Considerations on Three-Factor Anonymous Authentication Scheme for WSNs

Wireless sensor networks (WSNs) are used to monitor various environmental conditions including movement, pollution level, temperature, humidity, and etc. Secure authentication is very important for the success of WSNs. Li et al. proposed a three-factor anonymous authentication scheme in WSNs over Internet of things (IoT). They argued that their authentication scheme achieves more security and functional features, which are required for WSNs over IoT. Especially, they insisted that their user authentication scheme provides security against sensor node impersonation attack, and resists session-specific temporary information attack and various other attacks. However, this paper shows some security weaknesses in Li et al.’s scheme, especially focused on sensor node masquerading attack, known session-specific temporary information attack and deficiency of perfect forward secrecy. Especially, security considerations are very important to the modern IoT based applications. Thereby, the result of this paper could be very helpful for the IoT security researches.

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security tasks.Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks.This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.We examined the performance metrics of different machine learning classification categories such asK-Nearest Neighbour(KNN),Logistic Regression(LR),Support Vector Machine(SVM),Gboost,Decision Tree(DT),Na飗e Bayes,Long Short Term Memory(LSTM),and Multi-Layer Perceptron(MLP)on aWSN-dataset in different sizes.The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets,and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics.The performance metrics used in these validations were accuracy,F1-score,False Positive Ratio(FPR),False Negative Ratio(FNR),and the training execution time.Moreover,the test results showed the Gboost algorithm got 99.6%,98.8%,0.4%0.13%in accuracy,F1-score,FPR,and FNR,respectively.At training execution time,it obtained 1.41 s for the average of all training time execution datasets.In addition,this paper demonstrated that for the numeric statistical data type,the best results are in the size of the dataset ranging from3000 to 6000 records and the percentage between categories is not less than 50%for each category with the other categories.Furthermore,this paper investigated the effect of Gboost on the WSN lifetime,which resulted in a 32%reduction compared to other Gboost-free scenarios.

A Key Management Scheme Using(p,q)-Lucas Polynomials in Wireless Sensor Network

Wireless Sensor Network(WSN)has witnessed an unpredictable growth for the last few decades.It has many applications in various critical sectors such as real-time monitoring of nuclear power plant,disaster management,environment,military area etc.However,due to the distributed and remote deployment of sensor nodes in such networks,they are highly vulnerable to different security threats.The sensor network always needs a proficient key management scheme to secure data because of resourceconstrained nodes.Existing polynomial based key management schemes are simple,but the computational complexity is a big issue.Lucas polynomials,Fibonacci polynomials,Chebychev polynomials are used in Engineering,Physics,Combinatory and Numerical analysis etc.In this paper,we propose a key management scheme using(p,q)-Lucas polynomial to improve the security of WSN.In(p,q)-Lucas polynomial,p represents a random base number while q represents a substitute value of x in the polynomial.The value of p is unique,and q is different according to communication between nodes.Analysis of the proposed method on several parameters such as computational overhead,efficiency and storage cost have been performed and compared with existing related schemes.The analysis demonstrates that the proposed(p,q)-Lucas polynomial based key management scheme outperforms over other polynomials in terms of the number of keys used and efficiency.

ECBK: Enhanced Cluster Based Key Management Scheme for Achieving Quality of Service

Wireless sensor networks consist of many small nodes with distributing devices to monitor conditions at different locations. Usually wireless sensor nodes are sprinkled in a sensor field grouping limited areas. This paper highlights the Enhanced Cluster Based Key management (ECBK) protocol to achieve secure data delivery based on clustering mechanism. This protocol gives more importance to Cluster Coordinator node, which is used to coordinate the members and provide protective communication among the sensor nodes to enhance reliability. In Enhanced Cluster Based Key management two types of nodes are deployed. The high power nodes form clusters with surrounding nodes to enable the routing process without interference. This paper introduces ECBK protocol that balances the load among the clusters, achieves high throughput, end to end delay will be reduced, routing overhead also reduced and also it prolongs the network lifetime. Simulation results show that the presence of high transmission nodes reduces the delay, load balance, routing overhead, and enhances the throughput increased by 45% compared to other similar methods.