Automated vulnerability detection has become a research hot spot because it is beneficial for improving software quality and security. The code metric (CM) one class of important representations in source code. implicit relationships among different attributes have not been sufficiently considered traditional based on CMs. In this paper, view the local perception capability convolutional neural...

The need to deduce interesting and valuable information from large, complex, information-rich data sets is common to many research fields. Rule discovery or rule mining uses a set of IF-THEN rules to classify a class or category in a comprehensible way. Besides the classical approaches, many rule mining approaches use biologicallyinspired algorithms such as evolutionary algorithms and swarm int...

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the cu...

A connection-chain refers to a mechanism in which someone recursively logs into a host, then from there logs into another host, and so on. Connection-chains represent an important vector in many security attacks, so it is essential to be able to detect them. In this paper, we propose a host-based algorithm to detect them. We adopt a black-box approach by passively monitoring inbound and outboun...

With the rapid growth of open-source software, code cloning has become increasingly prevalent. If there are security vulnerabilities in a cloned segment, those may spread related software to potentially lead incidents. The existing methods vulnerable detection performed on condition that source is converted into an intermediate representation. However, these do not fully consider rich semantic ...

Spectral variability remains a challenging problem for target detection and classification in hyperspectral imagery (HSI). In this paper, we have applied the nonlinear support vector data description (SVDD) to perform full-pixel target detection. Using a pure target signature, we have developed a novel pattern recognition (PR) algorithm to train an SVDD to characterize the target class. We have...

Wireless Sensor Networks (WSNs) have become a key technology for the IoT and despite obvious benefits, challenges still exist regarding security. As more devices are connected to the internet, new cyber attacks are emerging which join well-known attacks posing significant threats to the confidentiality, integrity and availability of data in WSNs. In this work, we investigated two computational ...

Electricity theft is one of the challenging problems in smart grids. The power utilities around globe face huge economic loss due to ET. traditional electricity detection (ETD) models confront several challenges, such as highly imbalance distribution consumption data, curse dimensionality and inevitable effects non-malicious factors. To cope with aforementioned concerns, this paper presents a n...

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor becoming faulty having adverse effects the network if they not diagnosed inform fault status to other nodes. Fault diagnosis is difficult when behave some times provide good data at times. The intermittent disturbances may be random or kind of spikes either regular irregul...