Biblio

Sentiment Analysis (SA) is an approach for detecting subjective information such as thoughts, outlooks, reactions, and emotional state. The majority of previous SA work treats it as a text-classification problem that requires labelled input to train the model. However, obtaining a tagged dataset is difficult. We will have to do it by hand the majority of the time. Another concern is that the absence of sufficient cross-domain portability creates challenging situation to reuse same-labelled data across applications. As a result, we will have to manually classify data for each domain. This research work applies sentiment analysis to evaluate the entire vaccine twitter dataset. The work involves the lexicon analysis using NLP libraries like neattext, textblob and multi class classification using BERT. This word evaluates and compares the results of the machine learning algorithms.

Malicious cybersecurity activities have become increasingly worrisome for individuals and companies alike. While machine learning methods like Graph Neural Networks (GNNs) have proven successful on the malware detection task, their output is often difficult to understand. Explainable malware detection methods are needed to automatically identify malicious programs and present results to malware analysts in a way that is human interpretable. In this survey, we outline a number of GNN explainability methods and compare their performance on a real-world malware detection dataset. Specifically, we formulated the detection problem as a graph classification problem on the malware Control Flow Graphs (CFGs). We find that gradient-based methods outperform perturbation-based methods in terms of computational expense and performance on explainer-specific metrics (e.g., Fidelity and Sparsity). Our results provide insights into designing new GNN-based models for cyber malware detection and attribution.

The application of mobile intelligent terminal in the environment is very complex, and its own computing capacity is also very limited, so it is vulnerable to malicious attacks. The security classification of mobile intelligent terminals can effectively ensure the security of their use. Therefore, a security classification method for mobile intelligent terminals based on multi-source data fusion is proposed. The Boolean value is used to count the multi-source data of the mobile intelligent terminal, and the word frequency method is used to calculate the weight of the multi-source data of the mobile intelligent terminal. The D-S evidence theory is used to complete the multi-source data fusion of the mobile intelligent terminal and implement the multi-source data fusion processing of the mobile intelligent terminal. On this basis, the security level permission value of mobile intelligent terminal is calculated to achieve the security level division of mobile intelligent terminal based on multi-source data fusion. The experimental results show that the accuracy of mobile intelligent terminal security classification is higher than 96% and the classification time is less than 3.8 ms after the application of the proposed method. Therefore, the security level of mobile intelligent terminals after the application of this method is high, and the security performance of mobile intelligent terminals is strong, which can effectively improve the accuracy of security classification and shorten the time of security classification.

In order to meet the needs of intellectual property protection and controlled sharing of scientific research sensitive data, a mechanism is proposed for security protection throughout “transfer, store and use” process of sensitive data which based on blockchain. This blockchain bottom layer security is reinforced. First, the encryption algorithm used is replaced by the national secret algorithm and the smart contract is encapsulated as API at the gateway level. Signature validation is performed when the API is used to prevent illegal access. Then the whole process of data up-chain, storage and down-chain is encrypted, and a mechanism of data structure query and data query condition construction based on blockchain smart is provided to ensure that the data is “usable and invisible”. Finally, data access control is ensured through role-based and hierarchical protection, and the blockchain base developed has good extensibility, which can meet the requirement of sensitive data security protection in scientific research filed and has broad application prospects.

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.

Physical layer (PHY) security in decode-and-forward (DF) relay systems is discussed. Based on the types of wiretap links, the secrecy performance of three typical secure DF relay models is analyzed. Different from conventional works in this field, rigorous derivations of the secrecy channel capacity are provided from an information-theoretic perspective. Meanwhile, closed-form expressions are derived to characterize the secrecy outage probability (SOP). For the sake of unveiling more system insights, asymptotic analyses are performed on the SOP for a sufficiently large signal-to-noise ratio (SNR). The analytical results are validated by computer simulations and are in excellent agreement.

All-or-nothing transforms (AONT) were proposed by Rivest as a message preprocessing technique for encrypting data to protect against brute-force attacks, and have many applications in cryptography and information security. Later the unconditionally secure AONT and their combinatorial characterization were introduced by Stinson. Informally, a combinatorial AONT is an array with the unbiased requirements and its security properties in general depend on the prior probability distribution on the inputs s-tuples. Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has perfect security provided that all the inputs s-tuples are equiprobable, and has weak security provided that all the inputs s-tuples are with non-zero probability. This paper aims to explore on the gap between perfect security and weak security for combinatorial (t, s, v)-AONTs. Concretely, we consider the typical scenario that all the s inputs take values independently (but not necessarily identically) and quantify the amount of information H(\textbackslashmathcalX\textbackslashmid \textbackslashmathcalY) about any t inputs \textbackslashmathcalX that is not revealed by any s−t outputs \textbackslashmathcalY. In particular, we establish the general lower and upper bounds on H(\textbackslashmathcalX\textbackslashmid \textbackslashmathcalY) for combinatorial AONTs using information-theoretic techniques, and also show that the derived bounds can be attained in certain cases.

Tactical Data Link (TDL) is one of the important elements in Network Centric Warfare (NCW). TDL provides the means for rapid exchange of tactical information between air, ground, sea units and command centers. In military operations, TDL has high demands for resilience, responsiveness, reliability, availability and security. MANET has characteristics that are suitable for the combat environment, namely the ability to self-form and self-healing so that this network may be applied to the TDL system. To produce high performance in MANET adapted for TDL system, an efficient MAC Protocol method is needed. This paper provides a survey of several MAC Protocol methods on a tactical MANET. In this paper also suggests some improvements to the MANET MAC protocol to improve TDL system performance.

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.

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.

Memory corruption attacks have existed for multiple decades, and have become a major threat to computer systems. At the same time, a number of defense techniques have been proposed by research community. With the wide adoption of CPU-based memory safety solutions, sophisticated attackers tend to tamper with system memory via direct memory access (DMA) attackers, which leverage DMA-enabled I/O peripherals to fully compromise system memory. The Input-Output Memory Management Units (IOMMUs) based solutions are widely believed to mitigate DMA attacks. However, recent works point out that attackers can bypass IOMMU-based protections by manipulating the DMA interfaces, which are particularly vulnerable to race conditions and other unsafe interactions.State-of-the-art hardware-supported memory protections rely on metadata to perform security checks on memory access. Consequently, the additional memory request for metadata results in significant performance degradation, which limited their feasibility in real world deployments. For quantitative analysis, we separate the total metadata access latency into DRAM latency, on-chip latency, and cache latency, and observe that the actual DRAM access is less than half of the total latency. To minimize metadata access latency, we propose EMC, a low-overhead heap memory safety solution that implements a tripwire based mechanism on the memory controller. In addition, by using memory controller as a natural gateway of various memory access data paths, EMC could provide comprehensive memory safety enforcement to all memory data paths from/to system physical memory. Our evaluation shows an 0.54% performance overhead on average for SPEC 2017 workloads.

Recently, hardware Trojan has become a serious security concern in the integrated circuit (IC) industry. Due to the globalization of semiconductor design and fabrication processes, ICs are highly vulnerable to hardware Trojan insertion by malicious third-party vendors. Therefore, the development of effective hardware Trojan detection techniques is necessary. Testability measures have been proven to be efficient features for Trojan nets classification. However, most of the existing machine-learning-based techniques use supervised learning methods, which involve time-consuming training processes, need to deal with the class imbalance problem, and are not pragmatic in real-world situations. Furthermore, no works have explored the use of anomaly detection for hardware Trojan detection tasks. This paper proposes a semi-supervised hardware Trojan detection method at the gate level using anomaly detection. We ameliorate the existing computation of the Sandia Controllability/Observability Analysis Program (SCOAP) values by considering all types of D flip-flops and adopt semi-supervised anomaly detection techniques to detect Trojan nets. Finally, a novel topology-based location analysis is utilized to improve the detection performance. Testing on 17 Trust-Hub Trojan benchmarks, the proposed method achieves an overall 99.47% true positive rate (TPR), 99.99% true negative rate (TNR), and 99.99% accuracy.

One way to detect and assess software vulnerabilities is by extracting security-related information from commit messages. Automating the detection and assessment of vulnerabilities upon security commit messages is still challenging due to the lack of structured and clear messages. We created a convention, called SECOM, for security commit messages that structure and include bits of security-related information that are essential for detecting and assessing vulnerabilities for both humans and tools. The full convention and details are available here: https://tqrg.github.io/secom/.

Network attacks are always a nightmare for the network administrators as it eats away a huge wavelength and disturbs the normal working of many critical services in the network. Network behavior based profiling and detection is considered to be an accepted method; but the modeling data and method is always a big concern. The network event-based profiling is getting acceptance as they are sequential in nature and the sequence depicts the behavior of the system. This sequential network events can be analyzed using different techniques to create a profile for anomaly detection. In this paper we examine the possibility of two techniques for sequential event analysis using Modified GSP and IPAM algorithm. We evaluate the performance of these algorithms on the CSE-CIC-IDS 2018 data set to benchmark the performance. This experiment is different from other anomaly-based detection which evaluates the features of the dataset to detect the abnormalities. The performance of the algorithms on the dataset is then confirmed by the pattern evolving from the analysis and the indications it provides for early detection of network attacks.

Impactful data breaches that exposed the online accounts and financial information of billions of individuals have increased recently because of the digitization of numerous industries. As a result, the need for comprehensive cybersecurity measures has risen, particularly with regard to the safekeeping of user passwords. Strong password storage security ensures that even if an attacker has access to compromised data, they are unable to utilize the passwords in attack vectors like credential-stuffing assaults. Additionally, it will reduce the risk of threats like fraudulent account charges or account takeovers for users. This study compares the performance of several hashing algorithms, including Bcrypt, SHA-256 and MD5 and how bcrypt algorithm outperforms the other algorithms. Reversal of each of the results will be attempted using Rainbow Tables for better understanding of hash reversals and the comparisons are tabulated. The paper provides a detail implementation of bcrypt algorithm and sheds light on the methodology of BCRYPT hashing algorithm results in robust password security. While SHA-256 hashing algorithms are, easily susceptible to simple attacks such as brute force as it a fast algorithm and making bcrypt more favorable.

The student's fault is not the only cause of dropping out of school. Often, cases of dropping out of school are only associated with too general problems. However, sensitive issues that can be detrimental to certain parties in this regard, such as the institution's reputation, are usually not made public. To overcome this, an in-depth analysis of these cases is needed for proper handling. Many risks are associated with creating a single repository for this sensitive information. Therefore, some encryption is required to ensure data is not leaked. However, encryption at rest and in transit is insufficient as data leakage is a considerable risk during processing. In addition, there is also a risk of abuse of authority by insiders so that no single entity is allowed to have access to all data. Homomorphic encryption presents a viable solution to this challenge. Data may be aggregated under the security provided by Homomorphic Encryption. This method makes the data available for computation without being decrypted first and without paying the risk of having a single repository.

Homomorphic encryption is suitable for a machine learning in the cloud such as a privacy-preserving machine learning. However, ordinary homomorphic public key encryption has a problem that public key holders can generate ciphertexts and anyone can execute homomorphic operations. In this paper, we will propose a solution based on the Keyed Homomorphic-Public Key Encryption proposed by Emura et al.

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.

Imbalanced class distribution can cause information loss and missed/false alarms for deep learning and machine-learning algorithms. The detection performance of traditional intrusion detection systems tend to degenerate due to skewed class distribution caused by the uneven allocation of observations in different kinds of attacks. To combat class imbalance and improve network intrusion detection performance, we adopt the conditional generative adversarial network (CTGAN) that enables the generation of samples of specific classes of interest. CTGAN builds on the generative adversarial networks (GAN) architecture to model tabular data and generate high quality synthetic data by conditionally sampling rows from the generated model. Oversampling using CTGAN adds instances to the minority class such that both data in the majority and the minority class are of equal distribution. The generated security alerts are used for training classifiers that realize critical alert detection. The proposed scheme is evaluated on a real-world dataset collected from security operation center of a large enterprise. The experiment results show that detection accuracy can be substantially improved when CTGAN is adopted to produce a balanced security-alert dataset. We believe the proposed CTGAN-based approach can cast new light on building effective systems for critical alert detection with reduced missed/false alarms.

In real-time video streaming, data packets are transported over the network from a transmitter to a receiver. The quality of the received video fluctuates as the network conditions change, and it can degrade substantially when there is considerable packet loss. Forward error correction (FEC) techniques can be used to recover lost packets by incorporating redundant data. Conventional FEC schemes do not work well when scalable video coding (SVC) is adopted. In this paper, we propose a novel FEC scheme that overcomes the drawbacks of these schemes by considering the reference picture structure of SVC and weighting the reference pictures more when FEC redundancy is applied. The experimental results show that the proposed FEC scheme outperforms conventional FEC schemes.

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

Cloud computing is preferred because of its numerous improvements, such as data security, low maintenance cost, unlimited storage capacity and consistent backups. However, legitimate users take advantage of cloud storage services for storing a considerable amount of sensitive data. After storing data on the cloud, data users pass on control over data to cloud administrators. Although for assuring data security, sensitive information needs to be encrypted before deploying it on the cloud server. In traditional searchable encryption, encrypted data can be searched using keywords on a cloud server without knowing data details, and users can retrieve certain specific files of interest after authentication. However, the results are only related to the exact matching keyword searches. This drawback affects system usability and efficiency, due to which existing encryption methods are unsuitable in cloud computing. To avoid the above problems, this study includes as follows: Firstly, we analyze all fuzzy keyword search techniques that are wildcard based, gram based and trie-traverse. Secondly, we briefly describe certificateless cryptography and suggest a certificateless searchable encryption scheme. Finally, this study gives easy access to developing a fuzzy keyword searchable system for a new researcher to combine the above two points. It provides easy access and efficient search results.

Secrete message protection has become a focal point of the network security domain due to the problems of violating the network use policies and unauthorized access of the public network. These problems have led to data protection techniques such as cryptography, and steganography. Cryptography consists of encrypting secrete message to a ciphertext format and steganography consists of concealing the secrete message in codes that make up a digital file, such as an image, audio, and video. Steganography, which is different from cryptography, ensures hiding a secret message for secure transmission over the public network. This paper presents a steganographic approach using digital images for data hiding that aims to providing higher performance by combining fuzzy logic type I to pre-process the cover image and difference expansion techniques. The previous methods have used the original cover image to embed the secrete message. This paper provides a new method that first identifies the edges of a cover image and then proceeds with a difference expansion to embed the secrete message. The experimental results of this work identified an improvement of 10% of the existing method based on increased payload capacity and the visibility of the stego image.

Child facial expression recognition is a relatively less investigated area within affective computing. Children’s facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children’s facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.