naser mohammadzadeh

The smart home is an important Internet of Things applications. Due to the smartphones development, expansion of their network, and growing the data transfer rate, security in personal life has become a dramatic challenge. Therefore, it is essential to secure such a system to create a sense of relaxation in the lives of users and homeowners to deal with possible occurrences. The integration of technologies for the automation of home affairs with the Internet of things means that all physical objects can be accessed on cyberspace; therefore, the concerns raised by users about the lack of privacy and security are serious arguments that science and technology should answer. Therefore, addressing security issues is a crucial necessity for the development of the smart homes. Although authentication protocols have been proposed based on smart cards for multi-server architectures, their schemes cannot protect the system against stolen smart cards and dictionary attacks in the login phase and do not satisfy perfect forward secrecy. To overcome these limitations, this paper proposes an anonymous, secure protocol in connected smart home environments, using solely lightweight operations. The proposed protocol in this paper provides efficient authentication, key agreement, and enables the anonymity of devices and unlinkability. It is demonstrated that the computation complexity of the protocol is low as compared to the existing schemes, while security has been significantly improved. This protocol ensures that even if the stakeholder’s device or the IoT device is attacked, they are robust against them.

Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing unnecessary features is a solution to this problem. Using machine learning methods is one of the best ways to design an intrusion detection system. Focusing on this issue, in this paper, we propose a hybrid intrusion detection system using the decision tree and support vector machine (SVM) approaches. In our method, the feature selection is initially done by the C5.0 decision tree pruning, and then the features with the least predictor importance value are removed. After removing each feature, the least square support vector machine (LS-SVM) is applied. The set of features having the highest surface area under the Receiver Operating Characteristic (ROC) curve for LS-SVM are considered as final features. The experimental results on two KDD Cup 99 and UNSW-NB15 data sets show that the proposed approach improves true positive and false positive criteria and accuracy compared to the best prior work.

The Internet of Things (IoT), is a new concept that its emergence has caused ubiquity of sensors in the human life. All data are collected, processed, and transmitted by these sensors. As the number of sensors increases,   the first challenge in establishing a secure connection is authentication between sensors. Anonymity, lightweight, and trust between entities are other main issues that should be considered. However, this challenge also requires some features so that the authentication is done properly. Anonymity, light weight and trust between entities are among the issues that need to be considered. In this study, we have evaluated the authentication protocols concerning the Internet of Things and analyzed the security vulnerabilities and limitations found in them. A new authentication protocol is also proposed using the hash function and logical operators, so that the sensors can use them as computationally limited entities. This protocol is performed in two phases and supports two types of intra-cluster and inter-cluster communication. The analysis of proposed protocol shows that security requirements have been met and the protocol is resistant against various attacks. In the end, confidentiality and authentication of the protocol are proved applying AVISPA tool and the veracity of the protocol using the BAN logic. Focusing on this issue, in this paper, we have evaluated the authentication protocols in the Internet of Things and analyzed their limitations and security vulnerabilities. Moreover, a new authentication protocol is presented which the anonymity is its main target. The hash function and logical operators are used not only to make the protocol lightweight but also to provide some computational resources for sensors. In compiling this protocol, we tried to take into account three main approaches to covering the true identifier, generating the session key, and the update process after the authentication process. As with most authentication protocols, this protocol is composed of two phases of registration and authentication that initially register entities in a trusted entity to be evaluated and authenticated at a later stage by the same entity. It is assumed that in the proposed protocol we have two types of entities; a weak entity and a strong entity. The poor availability of SNs has low computing power and strong entities of CH and HIoTS that can withstand high computational overhead and carry out heavy processing.
We also consider strong entities in the proposed protocol as reliable entities since the main focus of this research is the relationship between SNs. On the other hand, given the authenticity of the sensors and the transfer of the key between them through these trusted entities, the authenticity of the sensors is confirmed, and the relationship between them is also reliable. This protocol supports two types of intra-cluster and inter-cluster communication. The analysis of the proposed protocol shows that security requirements such as untraceability, scalability, availability, etc. have been met and it is resistant against the various attacks like replay attack, eavesdropping attack.

Climatological parameters are among the most important ecological capability evaluation criterias, on other hand, climate changes which has intensified in the past decades and has the consequences such as; increases in climatic threshold phenomena like occurrence of horrific floodings, destructive hurricanes, abnormal and sudden colds and heats, untimely rainfalls and heavy snows, widespread droughts and etc., has shown the necessity of studding the impacts of climate change on various parts of economy and social more than before. But due to low spatial and temporal resolution of global climate models, regional experts must increase the resolution of this model's data using downscaling methods. This study has evaluated the power and precision of LARS-WG model in climatic data generating and future climate forecasting for Guilan province of Iran. Accordingly the daily data collected from synoptic stations in Guilan, with a minimum of 15 years of daily data, between 1995 to 2009 has been used. The parameters used included; rainfall, minimum temperature, maximum temperature and solar radiation. The results show that, the highest calculated value of Mean Absolute Error (MAE) for rainfall modeled data was 14.48 in Astara station, and the highest bias calculated value for rainfall parameter was -4.35 and in Astara station too. The model precision for modeling the minimum and maximum temperature was desired for modeling this parameters, and the highest MAE and bias values for minimum temperature were 0.17 and 0.065 in sequence and both in Anzali station. Also, the highest MAE and bias calculated values of maximum temperature were 0.26 and 0.23 in sequence and both in Rasht station. And in LARS-WG precision in modeling the solar radiation parameter, the Rasht station had the highest calculated MAE and bias values, with the amount of 0.31 and 0.08 in sequence. Results analysis show that the LARS-WG model, has the proper power and precision for climate modeling and data generating in Guilan province of Iran.