Adaptive Deep Rider LSTM-enabled Objective Functions for RPL routing in IoT applications

This paper presents a proposed Objective Function (OF) design using various routing metrics for improving the performance of IoT applications. The most important idea of the proposed design is the selection of the routing metrics with respect to the application requirements. The various metrics, such as Energy, Distance, Delay, Link quality, Trust (EDDLT) are used for improving the objective function design of the RPL in various IoT applications. Here, the Adaptive Deep rider LSTM is newly employed for the energy prediction where the Adaptive Deep Rider LSTM is devised by the combination of the adaptive theory with the Rider Adam Algorithm (RAA), and the Deep-Long Short Memory (Deep-LSTM). However, the evaluation of the proposed method is carried out energy dissipation, throughput, and delay by achieving a minimum energy dissipation of 0.549, maximum throughput of 1, and a minimum delay of 0.191, respectively.

Provably Secure Authentication Approach for Data Security in Cloud Using Hashing, Encryption, and Chebyshev-Based Authentication

Secure and efficient authentication mechanism becomes a major concern in cloud computing due to the data sharing among cloud server and user through internet. This paper proposed an efficient Hashing, Encryption and Chebyshev HEC-based authentication in order to provide security among data communication. With the formal and the informal security analysis, it has been demonstrated that the proposed HEC-based authentication approach provides data security more efficiently in cloud. The proposed approach amplifies the security issues and ensures the privacy and data security to the cloud user. Moreover, the proposed HEC-based authentication approach makes the system more robust and secured and has been verified with multiple scenarios. However, the proposed authentication approach requires less computational time and memory than the existing authentication techniques. The performance revealed by the proposed HEC-based authentication approach is measured in terms of computation time and memory as 26ms, and 1878bytes for 100Kb data size, respectively.

A Quantitative Method for Measuring Health of Authoritative Name Servers

The Domain Name System - DNS is regarded as one of the critical infrastructure component of the global Internet because a large-scale DNS outage would effectively take a typical user offline. Therefore, the Internet community should ensure that critical components of the DNS ecosystem - that is, root name servers, top-level domain registrars and registries, authoritative name servers, and recursive resolvers - function smoothly. To this end, the community should monitor them periodically and provide public alerts about abnormal behavior. The authors propose a novel quantitative approach for evaluating the health of authoritative name servers – a critical, core, and a large component of the DNS ecosystem. The performance is typically measured in terms of response time, reliability, and throughput for most of the Internet components. This research work proposes a novel list of parameters specifically for determining the health of authoritative name servers: DNS attack permeability, latency comparison, and DNSSEC validation.

A Secure Three Factor Based Authentication Scheme for Telecare Medicine Information System with Privacy Preservation

Telecare Medicine Information System (TMIS) is now attracting field for remote healthcare, diagnosis and emergency health services etc. The major objective of this type of system is to provide medical facilities to patients who are critically ill and unable to attend hospitals or put in isolation for observations. A major challenge of such systems is to securely transmit patients' health related information to the medical server through an insecure channel. This collected sensitive data is further used by medical practitioners for diagnosis and treatment purposes. Therefore, security and privacy are essential for healthcare data. In this paper, a robust authentication protocol based on Chebyshev Chaotic map has been proposed for adequate security while transmitting data. The privacy preservation is maintained by a rule set which mainly controls the views. A detailed security analysis was performed for the proposed scheme.

Predicting Security Vulnerable Developers Based On Their Techno-Behavioral Characteristics

Assigning developers for highly secured software projects requires identifying developers’ tendency to contribute towards vulnerable software codes called developer-centric security vulnerability to mitigate issues on human resource management, financial and project timelines. There are problems in assessing the previous codebases in evaluating the developer-centric security vulnerability level of each developer. Thus, this paper suggests a method to evaluate this through the techno-behavioral features of their previous projects. Consequently, we present results of an exploratory study of the developer-centric security vulnerability level prediction using a dataset of 1827 developers by logically selecting 13 techno-behavioral features. Our results depict that there is a correlation between techno-behavioral features and developer-centric security vulnerability with 89.46% accuracy. This model enables to predict developer-centric security vulnerability level of any developer if the required techno-behavioral features are available avoiding the analysis of his/her previous codebases.

Anomaly Detection using system logs

Anomaly detection is a very important step in building a secure and trustworthy system. Manually it is daunting to analyze and detect failures and anomalies. In this paper, we proposed an approach that leverages the pattern matching capabilities of Convolution Neural Network (CNN) for anomaly detection in system logs. Features from log files are extracted using a windowing technique. Based on this feature, a one-dimensional image (1×n dimension) is generated where the pixel values of an image correlate with the features of the logs. On these images, the 1D Convolution operation is applied followed by max pooling. Followed by Convolution layers, a multi-layer feed-forward neural network is used as a classifier that learns to classify the logs as normal or abnormal from the representation created by the convolution layers. The model learns the variation in log pattern for normal and abnormal behavior. The proposed approach achieved improved accuracy compared to existing approaches for anomaly detection in Hadoop Distributed File System (HDFS) logs.

Proxy-3S

Virtualization plays a key role in the area of Mobile Cloud Computing (MCC). In MCC, the protection of distributed VMs and mobile users’ sensitive data, in terms of security and privacy, is highly required. This paper presents a novel cloud proxy known as Three Policies Secure Cloud Proxy (Proxy-3S) that combines three security policies: VM users’ access control, VMs’ secure allocation and VMs’ secure communication. The proposed approach aims to keep the distributed VMs safe in different servers on the cloud. It enhances the access authorization to permit intensive distributed application tasks on the cloud or mobile devices while processing and communicating private information between VMs. Furthermore, an algorithm that enables secure communication among distributed VMs and protection of sensitive data in VMs on the cloud is proposed. Several experiments were conducted using a real-world healthcare distributed application. The experiments achieved promising results for high-level data protection and good efficiency rating compared to existing works.

NETWORK ANOMALIES DETECTION APPROACH BASED ON WEIGHTED VOTING

To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.

Lightweight VLSI Architectures for Image Encryption Applications

Lightweight cryptography offers significant security service in constrained environments such as wireless sensor networks and Internet of Things. The focus of this article is to construct lightweight SPN block cipher architectures with substitution box based on finite fields. The paper also details the FPGA implementation of the lightweight symmetric block cipher algorithm of SPN type with combinational S-box. Restructuring of traditional look-up-table Substitution Box (S-Box) sub-structure with a combinational logic S-box is attempted. Elementary architectures namely the basic round architecture and reduced datawidth architecture incorporating look-up-table and combinational S-Box substructure are compared in terms of area and throughput. Proposed restructure mechanism occupies less FPGA resources with no comprise in the latency and also demonstrates performance efficiency and low power consumption in Xilinx FPGAs. Robustness of the proposed method against various statistical attacks has been analyzed through comparison with other existing encryption mechanisms.

Watermarking Images via Counting-Based Secret Sharing for Lightweight Semi-Complete Authentication

Watermarking is the process of embedding specific data to prove ownership copyright authentication. It is needed whenever media-files are used without proper permission is granted. The current watermarking challenge comes from the ownership proof especially as slight tampering occurs on the multimedia-file which injure the watermarking causing difficulty in its copyright proof. This paper proposes utilizing counting-based secret sharing strategy to allow validation of ownership RGB watermarking even if some of the image-file is interfered. We validate image watermarking partially as lightweight semi-complete verification, which is not possible in the normal random-stream watermarking schemes. Although the work is still in its early stage, it is promising research showing real attractive contribution opportunities. The work is tested and compared to other strategies in aspects of robustness, capacity, and security, providing attractive remarks worth noting. The study evaluation results showed interesting contribution believed to be pioneering.