Biblio

Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should classify the known classes correctly while sensitive to the unknown class. In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification. We propose two transformations for the function call graph (FCG) based malware representations to facilitate the pretext task. Also, we present a statistical thresholding approach to find the optimal threshold for the unknown class. Moreover, the experiment results indicate that our proposed pre-training process can improve different performances of different downstream loss functions for the OSR problem.

With the rapid growth of the number of global network entities and interconnections, the security risks of network relationships are constantly accumulating. As the basis of network interconnection and communication, Internet routing is facing severe challenges such as insufficient online monitoring capability of large-scale routing events and lack of effective and credible verification mechanism. Major global routing security events emerge one after another, causing extensive and far-reaching impacts. To solve these problems, China Telecom studied the BGP (border gateway protocol) SDN (software defined network) controller technology to monitor the interconnection routing, constructed the global routing information database trust source integrating multi-dimensional information and developed the function of the protocol level based real-time monitoring system of Internet routing security events. Through these means, it realizes the second-level online monitoring capability of large-scale IP network Internet service routing events, forms the minute-level route leakage interception and route hijacking blocking solutions, and achieves intelligent protection capability of Internet routing security.

In the field of airport passenger security, a new type of security inspection equipment called intelligent passenger security equipment is applied widely, which can significantly improve the efficiency of airport security screening and passenger satisfaction. This paper establishes a security check channel model based on intelligent passenger security check equipment, and studies the factors affecting the efficiency of airport security screening, such as the number of baggage unloading points, baggage loading points, secondary inspection points, etc. A simulation model of security check channel is established based on data from existing intelligent passenger security check equipment and data collected from Beijing Daxing Airport. Equipment utilization and queue length data is obtained by running the simulation model. According to the data, the bottleneck is that the manual inspection process takes too long, and the utilization rate of the baggage unloading point is too low. For the bottleneck link, an optimization scheme is proposed. With more manual check points and secondary inspection points and less baggage unloading points, the efficiency of airport security screening significantly increases by running simulation model. Based on the optimized model, the effect of baggage unloading point and baggage loading point on efficiency is further studied. The optimal parameter configuration scheme under the expected efficiency is obtained. This research can assist engineers to find appropriate equipment configuration quickly and instruct the airport to optimize the arrangement of security staff, which can effectively improve the efficiency of airport security screening and reduce the operating costs of airport.

The rapid improvement of computer and network technology not only promotes the improvement of productivity and facilitates people's life, but also brings new threats to production and life. Cyberspace security has attracted more and more attention. Different from traditional cyberspace security, APT attacks on key networks or infrastructure, with the main goal of stealing intellectual property, confidential information or sabotage, seriously threatening the interests and security of governments, enterprises and scientific research institutions. Timely detection and blocking is particularly important. The purpose of this paper is to study the security of software supply chain in power industry based on BAS technology. The experimental data shows that Type 1 projects account for the least amount and Type 2 projects account for the highest proportion. Type 1 projects have high unit price contracts and high profits, but the number is small and the time for signing orders is long.

Traffic in a backbone network has high forwarding rate requirements, and as the network gets larger, traffic increases and forwarding rates decrease. In a Software Defined Network (SDN), the controller can manage a global view of the network and control the forwarding of network traffic. A deterministic network has different forwarding requirements for the traffic of different priority levels. Static traffic load balancing is not flexible enough to meet the needs of users and may lead to the overloading of individual links and even network collapse. In this paper, we propose a new backbone network load balancing architecture - EDQN (Edge Deep Q-learning Network), which implements queue-based gate-shaping algorithms at the edge devices and load balancing of traffic on the backbone links. With the advantages of SDN, the link utilization of the backbone network can be improved, the delay in traffic transmission can be reduced and the throughput of traffic during transmission can be increased.

The 6G wireless communication networks are being studied to build a powerful networking system with global coverage, enhanced spectral/energy/cost efficiency, better intelligent level and security. This paper presents a four-in-one networking paradigm named 3CL-Net that would broaden and strengthen the capabilities of current networking by introducing ubiquitous computing, caching, and intelligence over the communication connection to build 6G-required capabilities. To evaluate the practicability of 3CL-Net, this paper designs a platform based on the 3CL-Net architecture. The platform adopts leader-followers structure that could support all functions of 3CL-Net, but separate missions of 3CL-Net into two parts. Moreover, this paper has implemented part of functions as a prototype, on which some experiments are carried out. The results demonstrate that 3CL-Net is potential to be a practical and effective network paradigm to meet future requirements, meanwhile, 3CL-Net could motivate designs of related platforms as well.

Internet architecture has transformed into a more complex form than it was about a decade back. Today the internet comprises multimedia information where services and web applications have started to shift their focus on content. In our perspective of communication systems, content-centric networking (CCN) proposes a new methodology. The use of cache memory at the network level is an important feature of this new architecture. This cache is intended to store transit details for a set period, and it is hoped that this capability will aid in network quality, especially in a rapidly increasing video streaming situation. Information-centric networking (ICN) is the one architecture that is seen as a possible alternative for shifting the Internet from a host-centric to a content-centric point-of-view. It focuses on data rather than content. CCN is more reliable when it comes to data delivery as it does not need to depend on location for data. CCN architecture is scalable, secure and provides mobility support. In this paper, we implement a ccnchat, a chat testing application, which is created with the help of libraries provided by Palo Alto Research Center (PARC) on local area network (LAN) between two users and demonstrate the working of this local chat application over CCN network that works alongside existing IP infrastructure.

Industrial Control Systems (ICS) are not secure by design–with recent developments requiring them to connect to the Internet, they tend to be highly vulnerable. Additionally, attacks on critical infrastructures such as power grids and nuclear plants can cause significant damage and loss of lives. Since such attacks tend to generate anomalies in the systems, an efficient way of attack detection is to monitor the systems and identify anomalies in real-time. An automated anomaly detection tool is introduced in this paper. Additionally, the functioning of the systems is viewed as Finite State Automata. Specific sensor measurements are used to determine permissible transitions, and statistical measures such as the Interquartile Range are used to determine acceptable boundaries for the remaining sensor measurements provided by the system. Deviations from the boundaries or permissible transitions are considered as anomalies. An additional feature is the provision of a finite state automata diagram that provides the operational constraints of a system, given a set of regulated input. This tool showed a high anomaly detection rate when tested with three types of ICS. The concepts are also benchmarked against a state-of-the-art anomaly detection algorithm called Isolation Forest, and the results are provided.

With the rapid development of artificial intelligence technology, deepfake technology based on deep learning is receiving more and more attention from society or the industry. While enriching people's cultural and entertainment life, in-depth fakes technology has also caused many social problems, especially potential risks to managing network credible identities. With the continuous advancement of deep fakes technology, the security threats and trust crisis caused by it will become more serious. It is urgent to take adequate measures to curb the abuse risk of deep fakes. The article first introduces the principles and characteristics of deep fakes technology and then deeply analyzes its severe challenges to network trusted identity management. Finally, it researches the supervision and technical level and puts forward targeted preventive countermeasures.

Benchmark datasets are heavily depended upon by the research community to validate theoretical findings and track progression in the state-of-the-art. NIDS dataset creation presents numerous challenges on account of the volume, heterogeneity, and complexity of network traffic, making the process labor intensive, and thus, prone to error. This paper provides a critical review of CIC-IDS-2017 and CIC-CSE-IDS-2018, datasets which have seen extensive usage in the NIDS literature, and are currently considered primary benchmarking datasets for NIDS. We report a large number of previously undocumented errors throughout the dataset creation lifecycle, including in attack orchestration, feature generation, documentation, and labeling. The errors destabilize the results and challenge the findings of numerous publications that have relied on it as a benchmark. We demonstrate the implications of these errors through several experiments. We provide comprehensive documentation to summarize the discovery of these issues, as well as a fully-recreated dataset, with labeling logic that has been reverse-engineered, corrected, and made publicly available for the first time. We demonstrate the implications of dataset errors through a series of experiments. The findings serve to remind the research community of common pitfalls with dataset creation processes, and of the need to be vigilant when adopting new datasets. Lastly, we strongly recommend the release of labeling logic for any dataset released, to ensure full transparency.

Rational and smart decision making by means of strategic interaction and mathematical modelling is the key aspect of Game theory. Security games based on game theory are used extensively in cyberspace for various levels of security. The contemporary security issues can be modelled and analyzed using game theory as a robust mathematical framework. The attackers, defenders and the adversarial as well as defensive interactions can be captured using game theory. The security games equilibrium evaluation can help understand the attackers' strategies and potential threats at a deeper level for efficient defense. Wireless sensor network (WSN) designs are greatly benefitted by game theory. A deep learning adversarial network algorithm is used in combination with game theory enabling energy efficiency, optimal data delivery and security in a WSN. The trade-off between energy resource utilization and security is balanced using this technique.

Deep learning has been widely applied for jobs involving face inpainting, however, there are usually some problems, such as incoherent inpainting edges, lack of diversity of generated images and other problems. In order to get more feature information and improve the inpainting effect, we therefore propose a Generative Adversarial Network of Skip Connection (SCGAN), which connects the encoder layers and the decoder layers by skip connection in the generator. The coherence and consistency of the image inpainting edges are improved, and the finer features of the image inpainting are refined, simultaneously using the discriminator's local and global double discriminators model. We also employ WGAN-GP loss to enhance model stability during training, prevent model collapse, and increase the variety of inpainting face images. Finally, experiments on the CelebA dataset and the LFW dataset are performed, and the model's performance is assessed using the PSNR and SSIM indices. Our model's face image inpainting is more realistic and coherent than that of other models, and the model training is more reliable.

Unmanned Aerial Vehicle (UAV)-based streaming media transmission may become unstable when the bit rate generated by the source load exceeds the channel capacity owing to the UAV location and speed change. The change of the location can affect the network connection, leading to reduced transmission rate; the change of the flying speed can increase the video payload due to more I-frames. To improve the transmission reliability, in this paper we design a Client-Server-Ground&User (C-S-G&U) framework, and propose an algorithm of splitting-merging stream (SMS) for multi-link concurrent transmission. We also establish multiple transport links and configure the routing rules for the cross-layer design. The multi-link transmission can achieve higher throughput and significantly smaller end-to-end delay than a single-link especially in a heavy load situation. The audio and video data are packaged into the payload by the Real-time Transport Protocol (RTP) before being transmitted over the User Datagram Protocol (UDP). The forward error correction (FEC) algorithm is implemented to promote the reliability of the UDP transmission, and an encryption algorithm to enhance security. In addition, we propose a Quality of Service (QoS) strategy so that the server and the user can control the UAV to adapt its transmission mode dynamically, according to the load, delay, and packet loss. Our design has been implemented on an engineering platform, whose efficacy has been verified through comprehensive experiments.

One of the most challenging issues facing Internet of Medical Things (IoMT) cyber defense is the complexity of their ecosystem coupled with the development of cyber-attacks. Medical equipments lack built-in security and are increasingly becoming connected. Moving beyond traditional security solutions becomes a necessity to protect patients and organizations. In order to effectively deal with the security risks of networked medical devices in such a complex and heterogeneous system, we need to measure security risks and prioritize mitigation actions. In this context, we propose a Fuzzy AHP-based method to assess security attributes of connected medical devices and compare different device models against a selected profile with regards to the user requirements. The proposal aims to empower user security awareness to make well-educated decisions.

Emotion Detection through Facial feature recognition is an active domain of research in the field of human-computer interaction (HCI). Humans are able to share multiple emotions and feelings through their facial gestures and body language. In this project, in order to detect the live emotions from the human facial gesture, we will be using an algorithm that allows the computer to automatically detect the facial recognition of human emotions with the help of Convolution Neural Network (CNN) and OpenCV. Ultimately, Emotion Detection is an integration of obtained information from multiple patterns. If computers will be able to understand more of human emotions, then it will mutually reduce the gap between humans and computers. In this research paper, we will demonstrate an effective way to detect emotions like neutral, happy, sad, surprise, angry, fear, and disgust from the frontal facial expression of the human in front of the live webcam.

Mortality from lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. Many existing detection methods on lung nodules can achieve high sensitivity but meanwhile introduce an excessive number of false-positive proposals, which is clinically unpractical. In this paper, we propose the multi-head detection and spatial attention network, shortly MHSnet, to address this crucial false-positive issue. Specifically, we first introduce multi-head detectors and skip connections to capture multi-scale features so as to customize for the variety of nodules in sizes, shapes, and types. Then, inspired by how experienced clinicians screen CT images, we implemented a spatial attention module to enable the network to focus on different regions, which can successfully distinguish nodules from noisy tissues. Finally, we designed a lightweight but effective false-positive reduction module to cut down the number of false-positive proposals, without any constraints on the front network. Compared with the state-of-the-art models, our extensive experimental results show the superiority of this MHSnet not only in the average FROC but also in the false discovery rate (2.64% improvement for the average FROC, 6.39% decrease for the false discovery rate). The false-positive reduction module takes a further step to decrease the false discovery rate by 14.29%, indicating its very promising utility of reducing distracted proposals for the downstream tasks relied on detection results.

Data Protection and Consenting Communication Mechanisms (DPCCMs) enable users to express their privacy decisions and manage their online consent. Thus, they can become a crucial means of protecting individuals' online privacy and agency, thereby replacing the current problematic practices such as “consent dialogues”. Based on an in-depth analysis of different DPCCMs, we propose an interdisciplinary set of factors that can be used for a comparison of such mechanisms. Moreover, we use the results from a qualitative expert study to identify some of the main multidisciplinary challenges that DPCCMs should address to become widely adopted data privacy mechanisms. We leverage both the factors and the challenges to compare two current open specifications, i.e. the Advanced Data Protection Control (ADPC) and the Global Privacy Control (GPC), and discuss future work.

Artificial intelligence (AI) has been successfully employed in industries for decades, beginning with the invention of expert systems in the 1960s and continuing through the present ubiquity of deep learning. Data-driven AI solutions have grown increasingly common as a means of supporting ever-more complicated industrial processes owing to the accessibility of affordable computer and storage infrastructure. Despite recent optimism, implementing AI to smart industrial applications still offers major difficulties. The present paper gives an executive summary of AI methodologies with an emphasis on deep learning before detailing unresolved issues in AI safety, data privacy, and data quality — all of which are necessary for completely automated commercial AI systems.

Internet of Things (IoT) is widely present nowadays, from businesses to connected houses, and more. IoT is considered a part of the Internet of the future and will comprise billions of intelligent communication. These devices transmit data from sensors to entities like servers to perform suitable responses. The problem of securing these data from cyberattacks increases due to the sensitive information it contains. In addition, studies have shown that most of the time data transiting in IoT devices does not apply encrypted communication. Thus, anyone has the ability to listen to or modify the information. Encrypting communications seems mandatory to secure networks and data transiting from sensors to servers. In this paper, we propose an approach to secure the transmission and the storage of data in IoT using Elliptic Curve Cryptography (ECC). The proposed method offers a high level of security at a reasonable computational cost. Indeed, we present an adequate architecture that ensures the use of a state-of-the-art cryptography algorithm to encrypt sensitive data in IoT.

Cloud computing is a fast growing field that provides the user with resources like software, infrastructure and virtual hardware processing power. The steady rise of cloud computing in recent times allowed large companies and even individual users to move towards working with cloud storage systems. However, the risks of leakage of uploaded data in the cloud storage and the questions about the privacy of such systems are becoming a huge problem. Security incidents occur frequently everywhere around the world. Sometimes, data leak may occur at the server side by hackers for their own profit. Data being shared must be encrypted before outsourcing it to the cloud storage. Existing encryption/decryption systems utilize large computational power and have troubles managing the files. This paper introduces a file system that is a more efficient, virtual, with encryption/decryption scheme using parallel encryption. To make encryption and decryption of files easier, Parallel encryption is used in place of serial encryption which is integrated with Identity-Based Encryption in the file system. The proposed file system aims to secure files, reduce the chances of file stored in cloud storage getting leaked thus providing better security. The proposed file system, OutFS+, is more robust and secure than its predecessor, OutFS. Cloud outsourcing takes place faster and the files can be downloaded to the OutFS+ instance on the other side. Moreover, OutFS+ is secure since it is a virtual layer on the operating system and can be unmounted whenever the user wants to.

The vast proliferation of the connected devices makes the operation of the traditional networks so complex and drops the network performance, particularly, failure cases. In fact, a novel solution is proposed to enable the management of the network resources and services named software defined network (SDN). SDN splits the data plane and the control plane by centralizing all the control plane on one common platform. Further, SDN makes the control plane programmable by offering high flexibility for the network management and monitoring mostly in failure cases. However, the main challenge in SDN is security that is presented as the first barrier for its development. Security in SDN is presented at various levels and forms, particularly, the communication between the data plane and control plane that presents a weak point in SDN framework. In this article, we suggest a new security framework focused on the combination between the trust and awareness concepts (TAS-SDN) for a dynamic southbound communication SDN. Further, TAS-SDN uses trust levels to establish a secure communication between the control plane and data plane. As a result, we discuss the implementation and the performance of TAS-SDN which presents a promote security solution in terms of time execution, complexity and scalability for SDN.

With the continues growth of our technology, majority in our sectors are becoming smart and one of its great applications is in agriculture, which we call it as smart farming. The application of sensors, IoT, artificial intelligence, networking in the agricultural setting with the main purpose of increasing crop production and security level. With this advancement in farming, this provides a lot of privileges like remote monitoring, optimization of produce and too many to mention. In light of the thorough systematic analysis performed in this study, it was discovered that Edge-of-things is a potential computing scheme that could boost an artificial intelligence for intelligent remote farm security and intrusion detection pre-alarm system over other computing schemes. Again, the purpose of this study is not to replace existing cloud computing, but rather to highlight the potential of the Edge. The Edge architecture improves end-user experience by improving the time-related response of the system. response time of the system. One of the strengths of this system is to provide time-critical response service to make a decision with almost no delay, making it ideal for a farm security setting. Moreover, this study discussed the comparative analysis of Cloud, Fog and Edge in relation to farm security, the demand for a farm security system and the tools needed to materialize an Edge computing in a farm environment.

When users request location services, they are easy to expose their privacy information, and the scheme of using a third-party server for location privacy protection has high requirements for the credibility of the server. To solve these problems, a localized differential privacy protection scheme in mobile environment is proposed, which uses Markov chain model to generate probability transition matrix, and adds Laplace noise to construct a location confusion function that meets differential privacy, Conduct location confusion on the client, construct and upload anonymous areas. Through the analysis of simulation experiments, the scheme can solve the problem of untrusted third-party server, and has high efficiency while ensuring the high availability of the generated anonymous area.

In recent years, differential privacy has gradually become a standard definition in the field of data privacy protection. Differential privacy does not need to make assumptions about the prior knowledge of privacy adversaries, so it has a more stringent effect than existing privacy protection models and definitions. This good feature has been used by researchers to solve the in-depth learning problem restricted by the problem of privacy and security, making an important breakthrough, and promoting its further large-scale application. Combining differential privacy with BEGAN, we propose the DP-BEGAN framework. The differential privacy is realized by adding carefully designed noise to the gradient of Gan model training, so as to ensure that Gan can generate unlimited synthetic data that conforms to the statistical characteristics of source data and does not disclose privacy. At the same time, it is compared with the existing methods on public datasets. The results show that under a certain privacy budget, this method can generate higher quality privacy protection data more efficiently, which can be used in a variety of data analysis tasks. The privacy loss is independent of the amount of synthetic data, so it can be applied to large datasets.

Social media has beneficial and detrimental impacts on social life. The vast distribution of false information on social media has become a worldwide threat. As a result, the Fake News Detection System in Social Networks has risen in popularity and is now considered an emerging research area. A centralized training technique makes it difficult to build a generalized model by adapting numerous data sources. In this study, we develop a decentralized Deep Learning model using Federated Learning (FL) for fake news detection. We utilize an ISOT fake news dataset gathered from "Reuters.com" (N = 44,898) to train the deep learning model. The performance of decentralized and centralized models is then assessed using accuracy, precision, recall, and F1-score measures. In addition, performance was measured by varying the number of FL clients. We identify the high accuracy of our proposed decentralized FL technique (accuracy, 99.6%) utilizing fewer communication rounds than in previous studies, even without employing pre-trained word embedding. The highest effects are obtained when we compare our model to three earlier research. Instead of a centralized method for false news detection, the FL technique may be used more efficiently. The use of Blockchain-like technologies can improve the integrity and validity of news sources.