A Vulnerability Detection System Based on Fusion of Assembly Code and Source CodeRead the full article
Security and Communication Networks provides a prestigious forum for the R&D community in academia and industry working at the interdisciplinary nexus of next generation communications technologies for security implementations in all network layers.
Chief Editor, Dr Di Pietro, is a full professor of cybersecurity at the HBKU College of Science and Engineering, Qatar. His research interests include distributed systems security, cloud security, and wireless security.
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Permission Sensitivity-Based Malicious Application Detection for Android
Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.
Cryptographic Secrecy Analysis of Adaptive Steganographic Syndrome-Trellis Codes
Compared with traditional steganography, adaptive steganography based on STC (Syndrome-Trellis Codes) has extremely high antidetection ability and has been a mainstream and hot research direction in the field of information hiding over the past decades. However, it is noted, in specific scenarios, that a small number of methods can extract data from STC-based adaptive steganography, indicating security risks related to such algorithms. In this manuscript, the cryptographic secrecy of this kind of steganography is analyzed, on condition of two common attacks: stego-only attack and known-cover attack, respectively, from three perspectives: steganographic key equivocation, message equivocation, and unicity distance of the steganographic key. Focusing on the special layout characteristics of the parity-check matrix of STC, under the two attack conditions, the theoretical boundaries of the steganographic key equivocation function, the message equivocation function, and the unicity distance of the steganographic key are separately obtained, showing the impact of the three elements: the submatrix size, the randomness of the data, and the cover object on the cryptographic secrecy of the STC-based adaptive steganography, resulting in a theoretical reference to accurately judge the cryptographic secrecy of such steganography and design more secure steganography methods.
RT-SAD: Real-Time Sketch-Based Adaptive DDoS Detection for ISP Network
With the great changes in network scale and network topology, the difficulty of DDoS attack detection increases significantly. Most of the methods proposed in the past rarely considered the real-time, adaptive ability, and other practical issues in the real-world network attack detection environment. In this paper, we proposed a real-time adaptive DDoS attack detection method RT-SAD, based on the response to the external network when attacked. We designed a feature extraction method based on sketch and an adaptive updating algorithm, which makes the method suitable for the high-speed network environment. Experiment results show that our method can detect DDoS attacks using sampled Netflowunder high-speed network environment, with good real-time performance, low resource consumption, and high detection accuracy.
Research on Classification Method of Network Resources Based on Modified SVM Algorithm
According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.
Offline Pashto Characters Dataset for OCR Systems
In computer vision and artificial intelligence, text recognition and analysis based on images play a key role in the text retrieving process. Enabling a machine learning technique to recognize handwritten characters of a specific language requires a standard dataset. Acceptable handwritten character datasets are available in many languages including English, Arabic, and many more. However, the lack of datasets for handwritten Pashto characters hinders the application of a suitable machine learning algorithm for recognizing useful insights. In order to address this issue, this study presents the first handwritten Pashto characters image dataset (HPCID) for the scientific research work. This dataset consists of fourteen thousand, seven hundred, and eighty-four samples—336 samples for each of the 44 characters in the Pashto character dataset. Such samples of handwritten characters are collected on an A4-sized paper from different students of Pashto Department in University of Peshawar, Khyber Pakhtunkhwa, Pakistan. On total, 336 students and faculty members contributed in developing the proposed database accumulation phase. This dataset contains multisize, multifont, and multistyle characters and of varying structures.
Differentially Private Attributed Network Releasing Based on Early Fusion
Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.