查看更多>>摘要：In recent years, IoT adoption has been higher, and this causes lots of security concerns. One of the fundamental security concerns in IoT adoption is the question, "Are you who you say you are?" Thus, authentication forms the gateway for a secure communication system with IoT. So far, the human voice is one of the most natural, non-intrusive, and convenient behavioural biometric factors compared to other biometric authentication methods. Despite the non-intrusive characteristics of voice as a biometric authentication factor when accessing IoT technologies, there is a concern of a general societal trust and distrust with IoT technology and the risk of theft of users' data and imitation. This study derived a realistic trust evaluation model that incorporates privacy, reliability, security, usability, safety, and availability factors into a trust vector for a flexible measurement of trust in the user accessing IoT technologies.
查看更多>>摘要：Peer-to-peer (P2P) botnet is one of the greatest threats to digital data. It has become a common tool for performing a lot of malicious activities such as DDoS attacks, phishing attacks, spreading spam, identity theft, ransomware, extortion attack, and many other fraudulent activities. P2P botnets are very resilient and stealthy and keep mutating to evade security mechanisms. Therefore, it has become necessary to identify and detect botnet flow from the normal flow. This paper uses supervised machine learning algorithms to detect P2P botnet flow. This paper also uses an ensemble learning technique to combine the performances of various supervised machine learning models to make predictions. To validate the results, four performance metrics have been used. These are accuracy, precision, recall, and Fl -score. Experimental results show that the proposed approach delivers 99.99% accuracy, 99.81% precision, 99.11% recall, and 99.32% Fl score, which outperform the previous botnet detection approaches.
查看更多>>摘要：With the development of smart cities, video surveillance networks have become an important infrastructure for urban governance. However, by replacing or tampering with surveillance cameras, an important front-end device, attackers are able to access the internal network. In order to identify illegal or suspicious camera identities in advance, a camera identity identification method that incorporates multidimensional identification features is proposed. By extracting the static information of cameras and dynamic traffic information, a camera identity system that incorporates explicit, implicit, and dynamic identifiers is constructed. The experimental results show that the explicit identifiers have the highest contribution, but they are easy to forge; the dynamic identifiers rank second, but the traffic preprocessing is complex; the static identifiers rank last but are indispensable. Experiments on 40 cameras verified the effectiveness and feasibility of the proposed identifier system for camera identification, and the accuracy of identification reached 92.5%.
查看更多>>摘要：The potential of machine learning mechanisms played a key role in improving the intrusion detection task. However, other factors such as quality of data, overfitting, imbalanced problems, etc. may greatly affect the performance of an intelligent intrusion detection system (IDS). To tackle these issues, this paper proposes a novel machine learning-based IDS called i-2NIDS. The novelty of this approach lies in the application of the nested cross-validation method, which necessitates using two loops: the outer loop is for hyper-parameter selection that costs least error during the run of a small amount of training set and the inner loop for the error estimation in the test set. The experiments showed significant improvements within NSL-KDD dataset with a test accuracy rate of 99.97%, 99.79%, 99.72%, 99.96%, and 99.98% in detecting normal activities, DDoS/DoS, Probing, R2L and U2R attacks, respectively. The obtained results approve the efficiency and superiority of the approach over other recent existing experiments.
查看更多>>摘要：Currently, analysts in a variety of countries have developed various protocols for WSN clustering. Among them, the significant one is LEACH (low-energy adaptive cluster hierarchical) that accomplishes the objective of energy balancing by occasionally varying the CHs in the region. Nevertheless, since it implements a random number method, the appropriateness of the CH is full of suspicions. As a result, this work intends to discover the optimal cluster head selection (CHS) model for maximizing energy aware and secured routing in WSN. Here, optimal CH is chosen based upon constraints such as "trust evaluation (direct and indirect trust), distance, security (risk level evaluation), distance, energy and delay". In addition, the routing model considers the path quality determination of cluster (reliability). For choosing the best CH in WSN, slime wrap food update with cat and mouse optimization (SWFU-CMO) is deployed. Finally, the simulated outcomes verify the efficacy of presented approach related to residual energy, throughput, delay, etc.
查看更多>>摘要：Value of information privacy has changed over time. Hence a weak personal data protection legal system will increase the threats and damages to individuals, especially in case of sensitive data like health information. Considering increasing amount of incidents, there is not any report or study showing how far Health companies protect both personal information of Malaysian citizens. The objective of this study was to assess the level of compliance with Malaysian Personal Data Protection Act 2010 by hospitals, clinics, and pharmacies. The authors used qualitative method using document analysis. The authors evaluated privacy policies of samples in line with requirements of the Act, especially Notice and Choice Principle and rights of individuals. Findings of the study showed serious non-compliance. Some companies are completely unaware of the Act. Considering sensitivity of health information and its value, the authors suggested amending alternatives to be applied for these privacy statements. The authors suggested specific inspections and issuance of guidelines and orders by data protection commissioner.
查看更多>>摘要：Android is the most popular mobile OS; it has the highest market share worldwide on mobile devices. Due to its popularity and large availability among smartphone users from all around the world, it becomes the first target for cyber criminals who take advantage of its open-source nature to distribute malware through applications in order to steal sensitive data. To cope with this serious problem, many researchers have proposed different methods to detect malicious applications. Machine learning techniques are widely being used for malware detection. In this paper, the authors proposed a new method of feature selection based on the dragonfly algorithm, named BDA-FS, to improve the performance of Android malware detection. Different feature subsets selected by the application of this proposed method in combination with machine learning were used to build the classification model. Experimental results show that incorporating dragonfly algorithm into Android malware detection performed better classification accuracy with few features compared to machine learning without feature selection.
查看更多>>摘要：Forensic skills analysts play an imperative support to practice streaming data generated from the IoT networks. However, these sources pose size limitations that create traffic and increase big data assessment. The obtainable solutions have utilized cybercrime detection techniques based on regular pattern deviation. Here, a generalized model is devised considering the MapReduce as a backbone for detecting the cybercrime. The objective of this model is to present an automatic model, which using the misbehavior in IoT device can be manifested, and as a result the attacks exploiting the susceptibility can be exposed by newly devised automatic model. The simulation of IoT is done such that energy constraints are considered as basic part. The routing is done with fractional gravitational search algorithm to transmit the information amongst the nodes. Apart from this, the MapReduce is adapted for cybercrime detection and is done at base station (BS) considering deep neuro fuzzy network (DNFN) for identifying the malwares.
查看更多>>摘要：In view of the rich information and strong autocorrelation of remote sensing images, a super-resolution reconstruction algorithm based on symmetric local fusion blocks is proposed using a convolutional neural network based on local fusion blocks, which improves the effect of high-frequency information reconstruction. By setting local fusion in the residual block, the problem of insufficient high-frequency feature extraction is alleviated, and the reconstruction accuracy of remote sensing images of deep networks is improved. To improve the utilization of global features and reduce the computational complexity of the network, a residual method is used to set the symmetric jump connection between the local fusion blocks to form the symmetry between them. Experimental results show that the reconstruction results of 2-, 3-, and 4-fold sampling factors on the UC Merced and nwpu-resisc45 remote sensing datasets are better than those of comparison algorithms in image clarity and edge sharpness, and the reconstruction results are better in objective evaluation and subjective vision.
查看更多>>摘要：Industrial internet security incidents occur frequently, and it is very important to accurately and effectively detect industrial internet attacks. In this paper, a novel CNN-LSTM fusion model-based method is proposed to detect malicious behavior under industrial internet security. Firstly, the data distribution is analyzed with the help of kernel density estimation, and the Pearson correlation coefficient is used to select the strong correlation feature as the model input. The one-dimensional convolutional neural network and the long short-term memory network respectively extract the spatial sequence features of the data and then use the softmax function to complete the classification task. In order to verify the effectiveness of the model, it is evaluated on the NSL-KDD dataset and the GAS dataset, and experiments show that the model has a significant performance improvement over a single model. In the detection of industrial network traffic data, the accuracy rate of 97.09% and the recall rate of 90.84% are achieved.
NETL
NSTL
Igi Global