Biblio

Malware attacks in the cyber world continue to increase despite the efforts of Malware analysts to combat this problem. Recently, Malware samples have been presented as binary sequences and assembly codes. However, most researchers focus only on the raw Malware sequence in their proposed solutions, ignoring that the assembly codes may contain important details that enable rapid Malware detection. In this work, we leveraged the capabilities of deep autoencoders to investigate the presence of feature disparities in the assembly and raw binary Malware samples. First, we treated the task as outliers to investigate whether the autoencoder would identify and justify features as samples from the same family. Second, we added noise to all samples and used Deep Autoencoder to reconstruct the original samples by denoising. Experiments with the Microsoft Malware dataset showed that the byte samples' features differed from the assembly code samples.

This paper aims to contribute towards creating ambient abnormal behavior detection for smart security system from real-time human pose estimation using fuzzy-based systems. Human poses from keypoint detected by pose estimation model are transformed to as angle positions of the axis between human bodies joints comparing to reference point in the axis x to deal with problem of the position change occurred when an individual move in the image. Also, the article attempts to resolve the problem of the ambiguity interpreting the poses with triangular fuzzy logic-based system that determines the detected individual behavior and compares to the poses previously learnt, trained, and recorded by the system. The experiment reveals that the accuracy of the system ranges between 90.75% (maximum) and 84% (minimum). This means that if the accuracy of the system at 85%. The system can be applied to guide future research for designing automatic visual human behavior detection systems.

Data secure deletion operation in storage media is an important function of data security management. The internal physical properties of SSDs are different from hard disks, and data secure deletion of disks can not apply to SSDs directly. Copyback operation is used to improve the data migration performance of SSDs but is rarely used due to error accumulation issue. We propose a data securely deletion algorithm based on copyback operation, which improves the efficiency of data secure deletion without affecting the reliability of data. First, this paper proves that the data secure delete operation takes a long time on the channel bus, increasing the I/O overhead, and reducing the performance of the SSDs. Secondly, this paper designs an efficient data deletion algorithm, which can process read requests quickly. The experimental results show that the proposed algorithm can reduce the response time of read requests by 21% and the response time of delete requests by 18.7% over the existing algorithm.

As a mature and open mobile operating system, Android runs on many IoT devices, which has led to Android-based IoT devices have become a hotbed of malware. Existing static detection methods for malware using artificial intelligence algorithms focus only on the java code layer when extracting API features, however there is a lot of malicious behavior involving native layer code. Thus, to make up for the neglect of the native code layer, we propose a heterogeneous information network-based Android malware detection method with cross-layer features. We first translate the semantic information of apps and API calls into the form of meta-paths, and construct the adjacency of apps based on API calls, then combine information from different meta-paths using multi-core learning. We implemented our method on the dataset from VirusShare and AndroZoo, and the experimental results show that the accuracy of our method is 93.4%, which is at least 2% higher than other related methods using heterogeneous information networks for malware detection.

A weather radar is expected to provide information about weather conditions in real time and valid. To obtain these results, weather radar takes a lot of data samples, so a large amount of data is obtained. Therefore, the weather radar equipment must provide bandwidth for a large capacity for transmission and storage media. To reduce the burden of data volume by performing compression techniques at the time of data acquisition. Compressive Sampling (CS) is a new data acquisition method that allows the sampling and compression processes to be carried out simultaneously to speed up computing time, reduce bandwidth when passed on transmission media, and save storage media. There are three stages in the CS method, namely: sparsity transformation using the Discrete Cosine Transform (DCT) algorithm, sampling using a measurement matrix, and reconstruction using the Orthogonal Matching Pursuit (OMP) algorithm. The sparsity transformation aims to convert the representation of the radar signal into a sparse form. Sampling is used to extract important information from the radar signal, and reconstruction is used to get the radar signal back. The data used in this study is the real data of the IDRA beat signal. Based on the CS simulation that has been done, the best PSNR and RMSE values are obtained when using a CR value of two times, while the shortest computation time is obtained when using a CR value of 32 times. CS simulation in a sector via DCT using the CR value two times produces a PSNR value of 20.838 dB and an RMSE value of 0.091. CS simulation in a sector via DCT using the CR value 32 times requires a computation time of 10.574 seconds.

This paper offers a comparative vector assessment of DDoS and disinformation attacks. The assessed dimensions are as follows: (1) the threat agent, (2) attack vector, (3) target, (4) impact, and (5) defense. The results revealed that disinformation attacks, anchoring on astroturfs, resemble DDoS’s zombie computers in their method of amplification. Although DDoS affects several layers of the OSI model, disinformation attacks exclusively affect the application layer. Furthermore, even though their payloads and objectives are different, their vector paths and network designs are very similar. This paper, as its conclusion, strongly recommends the classification of disinformation as an actual cybersecurity threat to eliminate the inconsistencies in policies in social networking platforms. The intended target audiences of this paper are IT and cybersecurity experts, computer and information scientists, policymakers, legal and judicial scholars, and other professionals seeking references on this matter.

As autonomous service robots will become increasingly ubiquitous in our daily lives, human-robot conflicts will become more likely when humans and robots share the same spaces and resources. This thesis investigates the conflict resolution of robots and humans in everyday conflicts in the domestic and public context. Hereby, the acceptability, trustworthiness, and effectiveness of verbal and non-verbal strategies for the robot to solve the conflict in its favor are evaluated. Based on the assumption of the Media Equation and CASA paradigm that people interact with computers as social actors, robot conflict resolution strategies from social psychology and human-machine interaction were derived. The effectiveness, acceptability, and trustworthiness of those strategies were evaluated in online, virtual reality, and laboratory experiments. Future work includes determining the psychological processes of human-robot conflict resolution in further experimental studies.

The main objective of this research is to increase security awareness against phishing attacks in the education sector by teaching users about phishing URLs. The educational media was made based on references from several previous studies that were used as basic references. Development of antiphishing game framework educational media using the extended DPE framework. Participants in this study were vocational and college students in the technology field. The respondents included vocational and college students, each with as many as 30 respondents. To assess the level of awareness and understanding of phishing, especially phishing URLs, participants will be given a pre-test before playing the game, and after completing the game, the application will be given a posttest. A paired t-test was used to answer the research hypothesis. The results of data analysis show differences in the results of increasing identification of URL phishing by respondents before and after using educational media of the anti-phishing game framework in increasing security awareness against URL phishing attacks. More serious game development can be carried out in the future to increase user awareness, particularly in phishing or other security issues, and can be implemented for general users who do not have a background in technology.

In recent times, the occurrence of malware attacks are increasing at an unprecedented rate. Particularly, the image-based malware attacks are spreading worldwide and many people get harmful malware-based images through the technique called steganography. In the existing system, only open malware and files from the internet can be identified. However, the image-based malware cannot be identified and detected. As a result, so many phishers make use of this technique and exploit the target. Social media platforms would be totally harmful to the users. To avoid these difficulties, Machine learning can be implemented to find the steganographic malware images (contents). The proposed methodology performs an automatic detection of malware and steganographic content by using Machine Learning. Steganography is used to hide messages from apparently innocuous media (e.g., images), and steganalysis is the approach used for detecting this malware. This research work proposes a machine learning (ML) approach to perform steganalysis. In the existing system, only open malware and files from the internet are identified but in the recent times many people get harmful malware-based images through the technique called steganography. Social media platforms would be totally harmful to the users. To avoid these difficulties, the proposed Machine learning has been developed to appropriately detect the steganographic malware images (contents). Father, the steganalysis method using machine learning has been developed for performing logistic classification. By using this, the users can avoid sharing the malware images in social media platforms like WhatsApp, Facebook without downloading it. It can be also used in all the photo-sharing sites such as google photos.

In the present innovation, for the trading of information, the internet is the most well-known and significant medium. With the progression of the web and data innovation, computerized media has become perhaps the most famous and notable data transfer tools. This advanced information incorporates text, pictures, sound, video etc moved over the public organization. The majority of these advanced media appear as pictures and are a significant part in different applications, for example, chat, talk, news, website, web-based business, email, and digital books. The content is still facing various challenges in which including the issues of protection of copyright, modification, authentication. Cryptography, steganography, embedding techniques is widely used to secure the digital data. In this present the hybrid model of LSB steganography and Advanced Encryption Standard (AES) cryptography techniques to enhanced the security of the digital image and text that is undeniably challenging to break by the unapproved person. The security level of the secret information is estimated in the term of MSE and PSNR for better hiding required the low MSE and high PSNR values.

A critical property of distributed data processing systems is the high level of reliability of such systems. A practical solution to this problem is to place copies of archives of magnetic media in the nodes of the system. These archives are used to restore data destroyed during the processing of requests to this data. The paper shows the impact of the use of archives on the reliability indicators of distributed systems.

In recent years, the detection of illegal and harmful messages which plays an significant role in Internet service is highly valued by the government and society. Although artificial intelligence technology is increasingly applied to actual operating systems, it is still a big challenge to be applied to systems that require high real-time performance. This paper provides a real-time detection system solution based on artificial intelligence technology. We first introduce the background of real-time detection of illegal and harmful messages. Second, we propose a complete set of intelligent detection system schemes for real-time detection, and conduct technical exploration and innovation in the media classification process including detection model optimization, traffic monitoring and automatic configuration algorithm. Finally, we carry out corresponding performance verification.

Steganography is one of the techniques for secure transformation of data which aims at hiding information inside other media in such a way that no one will notice. The cover media that can accommodate secret information include text, audio, image, and video. Images are the most popular covering media in steganography, due to the fact that, they are heavily used in daily applications and have high redundancy in representation. In this paper, we propose an adaptive steganography algorithm for hiding information in RGB images. To minimize visual perceptible distortion, the proposed algorithm uses edge pixels for embedding data. It detects the edge pixels in the image using the Sobel filter. Then, the message is embedded into the LSBs of the blue channel of the edge pixels. To resist statistical attacks, the distribution of the blue channel of the edge pixels is used when embedding data in the cover image. The experimental results showed that the algorithm offers high capacity for hiding data in cover images; it does not distort the quality of the stego image; it is robust enough against statistical attacks; and its execution time is short enough for online data transfer. Also, the results showed that the proposed algorithm outperforms similar approaches in all evaluation metrics.

Disinformation, fake news, and unverified rumors spread quickly in online social networks (OSNs) and manipulate people's opinions and decisions about life events. The solid mathematical solutions of the strategic decisions in OSNs have been provided under game theory models, including multiple roles and features. This work proposes a game-theoretic opinion framework to model subjective opinions and behavioral strategies of attackers, users, and a defender. The attackers use information deception models to disseminate disinformation. We investigate how different game-theoretic opinion models of updating people's subject opinions can influence a way for people to handle disinformation. We compare the opinion dynamics of the five different opinion models (i.e., uncertainty, homophily, assertion, herding, and encounter-based) where an opinion is formulated based on Subjective Logic that offers the capability to deal with uncertain opinions. Via our extensive experiments, we observe that the uncertainty-based opinion model shows the best performance in combating disinformation among all in that uncertainty-based decisions can significantly help users believe true information more than disinformation.

The growing use of digital media has been accompanied by an increase of the risks associated with the use of information systems, notably cybersecurity risks. In turn, the increasing use of information systems has an impact on users' media and information literacy. This research aims to address the relationship between media and information literacy, and the adoption of risky cybersecurity behaviours. This approach will be carried out through the definition of a conceptual framework supported by a literature review, and a quantitative research of the relationships mentioned earlier considering a sample composed by students of a Higher Education Institution.

The 2020 Corona pandemic has shown that on-line real-time multimedia communication is of vital importance when regular face-to-face meetings are not possible. One popular choice for conducting these meetings is the open standard WebRTC which is implemented in every major web browser. Even though this technology has found widespread use, there are still open issues with how different congestion control (CC) algorithms of Media- and DataChannels interact. In 2018 we have shown that the issue of self-inflicted queuing delay can be mitigated by introducing a CC coupling mechanism called FSE-NG. Originally, this solution was only capable of linking DataChannel flows controlled by TCP-style CCs and MediaChannels controlled by NADA CC. Standardization has progressed and along with NADA, IETF has also standardized the RTP CC SCReAM. This work extends the FSE-NG algorithm to also incorporate flows controlled by the latter algorithm. By means of simulation, we show that our approach is capable of drastically reducing end-to-end delays while also increasing RTP throughput and thus enabling WebRTC communication in scenarios where it has not been applicable before.

The COVID-19 pandemic introduced novel incentives for adversaries to exploit the state of turmoil. As we have witnessed with the increase in for instance phishing attacks and domain name registrations piggybacking the COVID-19 brand name. In this paper, we perform an analysis at Internet-scale of COVID-19 domain name registrations during the early stages of the virus' spread, and investigate the rationales behind them. We leverage the DomainTools COVID-19 Threat List and additional measurements to analyze over 150,000 domains registered between January 1st 2020 and May 1st 2020. We identify two key rationales for covid-related domain registrations. Online marketing, by either redirecting traffic or hosting a commercial service on the domain, and domain parking, by registering domains containing popular COVID-19 keywords, presumably anticipating a profit when reselling the domain later on. We also highlight three public policy take-aways that can counteract this domain registration behavior.

USB flash drives are used widely to store or transfer data among the employees in the company. There was greater concern about leaks of information especially company crown jewel or intellectual property data inside the USB flash drives because of theft, loss, negligence or fraud. This study is a real case in XYZ company which aims to find remaining the company’s crown jewel or intellectual property data inside the USB flash drives that belong to the employees. The research result showed that sensitive information (such as user credentials, product recipes and customer credit card data) could be recovered from the employees’ USB flash drives. It could obtain a high-risk impact on the company as reputational damage and sabotage product from the competitor. This result will help many companies to increase security awareness in protecting their crown jewel by having proper access control and to enrich knowledge regarding digital forensic for investigation in the company or enterprise.

Digital forensics is concerned with identifying, reporting and responding to security breaches. It is about how to acquire, analyze and report digital evidence and using the technical skills, discovering the traces of Cyber Crime. The field of digital forensics is in high demand due to the constant threats of data breaches and information hacks. Digital Forensics is utilized in the identification and elimination of crimes in any controversy where evidence is preserved in online space. This is the use of specialized techniques for retrieval, authentication and electronic data analysis. Computer forensics deals with the identification, preservation, analysis, documentation and presentation of digital evidence. The paper has analyzed the present-day trends that includes IoT forensics, cloud forensics, network forensics and social media forensics. Recent researches have shown a wide range of threats and cyber-attacks, which requires forensic investigators and forensics scientists to simplify the digital world. Hence, all our research gives a clear view of digital forensics which could be of a great help in forensic investigation. In this research paper we have discussed about the need and way to preserve the digital evidence, so that it is not compromised at any point in time and an unalter evidence can be presented before the court of law.

In the computer era, various digital devices are used along with networking technology for data communication in secured manner. But sometimes these systems are misused by the attackers. Information security with the high efficiency devices, tools are utilized for protecting the communication media and valuable data. In case of any unwanted incidents and security breaches, digital forensics methods and measures are well utilized for detecting the type of attacks, sources of attacks, their purposes. By utilizing information related to security measures, digital forensics evidences with suitable methodologies, digital forensics investigators detect the cyber-crimes. It is also necessary to prove the cyber-crimes before the law enforcement department. During this process investigators type to collect different types of information from the digital devices concerned to the cyber-attack. One of the major tasks of the digital investigator is collecting and managing the seizure records from the crime-scene. The present paper discusses the seizure record framework for digital forensics investigations.

On the basis of analyzing the development and application of virtual reality (VR) technology at home and abroad, and combining with the specific situation of the exhibition hall, this paper establishes an immersive scene roaming system of the exhibition hall. The system is completed by virtual scene modeling technology and virtual roaming interactive technology. The former uses modeling software to establish the basic model in the virtual scene, while the latter uses VR software to enable users to control their own roles to run smoothly in the roaming scene. In interactive roaming, this paper optimizes the A* pathfinding algorithm, uses binary heap to process data, and on this basis, further optimizes the pathfinding algorithm, so that when the pathfinding target is an obstacle, the pathfinder can reach the nearest place to the obstacle. Texture mapping technology, LOD technology and other related technologies are adopted in the modeling, thus finally realizing the immersive scene roaming system of the exhibition hall.

Cloud computing is one of the greatest and authoritative paradigms in computing as it provides access and use of various third-party services at a lower cost. However, there exist various security challenges facing cloud computing especially in the aspect of data privacy and this is more critical when dealing with sensitive personal or organization's data. Cloud service providers encrypt data during transfer from the local hard drive to the cloud server and at the server-side, the only problem is that the encryption key is stored by the service provider meaning they can decrypt your data. This paper discusses how cloud security can be enhanced by using client-side data encryption (pre-internet encryption), this will allow the clients to encrypt data before uploading to the cloud and store the key themselves. This means that data will be rendered to the cloud in an unreadable and secure format that cannot be accessed by unauthorized persons.

In the field of image steganography, edge detection based implantation methods play vital rules in providing stronger security of hided data. In this arena, researcher applies a suitable edge detection method to detect edge pixels in an image. Those detected pixels then conceive secret message bits. A very recent trend is to employ multiple edge detection methods to increase edge pixels in an image and thus to enhance the embedding capacity. The uses of multiple edge detectors additionally boost up the data security. Like as the demand for embedding capacity, many applications need to have the modified image, i.e., stego image, with good quality. Indeed, when the message payload is low, it will not be a better idea to finds more local pixels for embedding that small payload. Rather, the image quality will look better, visually and statistically, if we could choose a part but sufficient pixels to implant bits. In this article, we propose an algorithm that uses multiple edge detection algorithms to find edge pixels separately and then selects pixels which are common to all edges. This way, the proposed method decreases the number of embeddable pixels and thus, increases the image quality. The experimental results provide promising output.

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on Deep Neural Networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real.

Advances in machine learning, deep learning, and Artificial Intelligence(AI) allows people to exchange other people's faces and voices in videos to make it look like what they did or say whatever you want to say. These videos and photos are called “deepfake” and are getting more complicated every day and this has lawmakers worried. This technology uses machine learning technology to provide computers with real data about images, so that we can make forgeries. The creators of Deepfake use artificial intelligence and machine learning algorithms to mimic the work and characteristics of real humans. It differs from counterfeit traditional media because it is difficult to identify. As In the 2020 elections loomed, AI-generated deepfakes were hit the news cycle. DeepFakes threatens facial recognition and online content. This deception can be dangerous, because if used incorrectly, this technique can be abused. Fake video, voice, and audio clips can do enormous damage. This paper examines the algorithms used to generate deepfakes as well as the methods proposed to detect them. We go through the threats, research patterns, and future directions for deepfake technologies in detail. This research provides a detailed description of deep imitation technology and encourages the creation of new and more powerful methods to deal with increasingly severe deep imitation by studying the history of deep imitation.