Cities are becoming increasingly smart as the Internet of Things (IoT) proliferates. With IoT devices interconnected, smart cities can offer novel and ubiquitous services as well as automate many of our daily lives (e.g., smart health, smart home). The abundance in the number of IoT devices leads to divergent types of security threats as well. One of such important attacks is the Distributed Denial of Service attack(DDoS). DDoS attacks have become increasingly common in the internet of things because of the rapid growth of insecure devices. These attacks slow down legitimate network requests. Although DDoS attacks were first reported in 1996, the sophistication of these attacks has increased significantly. In mid-August 2020, a 2 Terabytes per second(TBps) attack targeting critical infrastructure, such as finance, was reported. In the next two years, it is predicted that this number will double to 15 million attacks. Blockchain technology, whose development dates back to the advent of the internet, has become one of the most important advancements to come along since that time. Several applications can use this technology to secure exchanges. Using blockchain to mitigate DDoS attacks is discussed in this survey paper in diverse domains to date. Its purpose is to expose the strengths, weaknesses, and limitations of the different approaches to DDoS mitigation. As a research and development platform for DDoS mitigation, this paper will act as a central hub for a more comprehensive understanding of these approaches.

A distributed denial-of-service (DDoS) is a malicious attempt by attackers to disrupt the normal traffic of a targeted server, service or network. This is done by overwhelming the target and its surrounding infrastructure with a flood of Internet traffic. The multiple compromised computer systems (bots or zombies) then act as sources of attack traffic. Exploited machines can include computers and other network resources such as IoT devices. The attack results in either degraded network performance or a total service outage of critical infrastructure. This can lead to heavy financial losses and reputational damage. These attacks maximise effectiveness by controlling the affected systems remotely and establishing a network of bots called bot networks. It is very difficult to separate the attack traffic from normal traffic. Early detection is essential for successful mitigation of the attack, which gives rise to a very important role in cybersecurity to detect the attacks and mitigate the effects. This can be done by deploying machine learning or deep learning models to monitor the traffic data. We propose using various machine learning and deep learning algorithms to analyse the traffic patterns and separate malicious traffic from normal traffic. Two suitable datasets have been identified (DDoS attack SDN dataset and CICDDoS2019 dataset). All essential preprocessing is performed on both datasets. Feature selection is also performed before detection techniques are applied. 8 different Neural Networks/ Ensemble/ Machine Learning models are chosen and the datasets are analysed. The best model is chosen based on the performance metrics (DEEP NEURAL NETWORK MODEL). An alternative is also suggested (Next best - Hypermodel). Optimisation by Hyperparameter tuning further enhances the accuracy. Based on the nature of the attack and the intended target, suitable mitigation procedures can then be deployed.

Target attack identification and detection has always been a concern of network security in the current environment. However, the economic losses caused by DDoS attacks are also enormous. In recent years, DDoS attack detection has made great progress mainly in the user application layer of the network layer. In this paper, a review and discussion are carried out according to the different detection methods and platforms. This paper mainly includes three parts, which respectively review statistics-based machine learning detection, target attack detection on SDN platform and attack detection on cloud service platform. Finally, the research suggestions for DDoS attack detection are given.

This paper mainly explores the detection and defense of DDoS attacks in the SDN architecture of the 5G environment, and proposes a DDoS attack detection method based on the deep learning two-level model CNN-LSTM in the SDN network. Not only can it greatly improve the accuracy of attack detection, but it can also reduce the time for classifying and detecting network traffic, so that the transmission of DDoS attack traffic can be blocked in time to ensure the availability of network services.

Intrusion detection systems (IDS) are most efficient way of defending against network-based attacks aimed at system devices, especially wireless devices. These systems are used in almost all large-scale IT infrastructures components, and they effected with different types of network attacks such as DDoS attack. Distributed Denial of-Services (DDoS) attacks the protocols and systems that are intended to provide services (to the public) are inherently vulnerable to attacks like DDoS, which were launched against a number of important Internet sites where security precautions were in place.

DDoS attacks, one of the oldest forms of cyberthreats, continue to be a favorite tool of mass interruption, presenting cybersecurity hazards to practically every type of company, large and small. As a matter of fact, according to IDC, DDoS attacks are predicted to expand at an 18 percent compound annual growth rate (CAGR) through 2023, indicating that it is past time to enhance investment in strong mitigation systems. And while some firms may assume they are limited targets for a DDoS assault, the amount of structured internet access to power corporation services and apps exposes everyone to downtime and poor performance if the infrastructure is not protected against such attacks. We propose using correlations between missing packets to increase detection accuracy. Furthermore, to ensure that these correlations are calculated correctly.

This paper studies Distributed Denial of Service (DDoS) attack detection by adopting the Deep Neural Network (DNN) model in Software Defined Networking (SDN). We first deploy the flow collector module to collect the flow table entries. Considering the detection efficiency of the DNN model, we also design some features manually in addition to the features automatically obtained by the flow table. Then we use the preprocessed data to train the DNN model and make a prediction. The overall detection framework is deployed in the SDN controller. The experiment results illustrate DNN model has higher accuracy in identifying attack traffic than machine learning algorithms, which lays a foundation for the defense against DDoS attack.

The issues of development and legal regulation of cybersecurity in Ukraine are considered. The expediency of further improvement of the regulatory framework, its implementation and development of cybersecurity systems is substantiated. Further development of the theoretical base of cyber defense using spline functions is proposed. The characteristics of network traffic are considered from the point of view of detecting DDoS cyber attacks (SYN-Flood, ICMP-Flood, UDP-Flood) and predicting DDoS cyber-attacks using spline functions. The spline extrapolation method makes it possible to predict DDoS cyber attacks with great accuracy.

Machine learning-based DDoS attack detection methods are mostly implemented at the packet level with expensive computational time costs, and the space cost of those sketch-based detection methods is uncertain. This paper proposes a two-stage DDoS attack detection algorithm combining time series-based multi-dimensional sketch and machine learning technologies. Besides packet numbers, total lengths, and protocols, we construct the time series-based multi-dimensional sketch with limited space cost by storing elephant flow information with the Boyer-Moore voting algorithm and hash index. For the first stage of detection, we adopt CNN to generate sketch-level DDoS attack detection results from the time series-based multi-dimensional sketch. For the sketch with potential DDoS attacks, we use RNN with flow information extracted from the sketch to implement flow-level DDoS attack detection in the second stage. Experimental results show that not only is the detection accuracy of our proposed method much close to that of packet-level DDoS attack detection methods based on machine learning, but also the computational time cost of our method is much smaller with regard to the number of machine learning operations.

A direct access (DAX) file system maximizes the benefit of persistent memory(PM)’s low latency through removing the page cache layer from the file system access paths. However, this paper reveals that data block allocation of the DAX file systems in common is significantly slower than that of conventional file systems because the DAX file systems require the zero-out operation for the newly allocated blocks to prevent the leakage of old data previously stored in the allocated data blocks. The retarded block allocation significantly affects the file write performance. In addition to this revelation, this paper proposes an off-critical-path data block sanitization scheme tailored for DAX file systems. The proposed scheme detaches the zero-out operation from the latency-critical I/O path and performs that of released data blocks in the background. The proposed scheme’s design principle is universally applicable to most DAX file systems. For evaluation, we implemented our approach in Ext4-DAX and XFS-DAX. Our evaluation showed that the proposed scheme reduces the append write latency by 36.8%, and improved the performance of FileBench’s fileserver workload by 30.4%, YCSB’s workload A on RocksDB by 3.3%, and the Redis-benchmark by 7.4% on average, respectively.

Healthcare has become one of the most important aspects of people’s lives, resulting in a surge in medical big data. Healthcare providers are increasingly using Internet of Things (IoT)-based wearable technologies to speed up diagnosis and treatment. In recent years, Through the Internet, billions of sensors, gadgets, and vehicles have been connected. One such example is for the treatment and care of patients, technology—remote patient monitoring—is already commonplace. However, these technologies also offer serious privacy and data security problems. Data transactions are transferred and logged. These medical data security and privacy issues might ensue from a pause in therapy, putting the patient’s life in jeopardy. We planned a framework to manage and analyse healthcare large data in a safe manner based on blockchain. Our model’s enhanced privacy and security characteristics are based on data sanitization and restoration techniques. The framework shown here make data and transactions more secure.

Real-time data transmissions from a vehicle enhance road safety and traffic efficiency by aggregating data in a central server for data analytics. When drivers share their instantaneous vehicular information for a service provider to perform a legitimate task, a curious service provider may also infer private information it has not been authorized for. In this paper, we propose a privacy preservation framework based on the Hilbert Schmidt Independence Criterion (HSIC) to sanitize driving data to protect the vehicle’s trajectory from adversarial inference while ensuring the data is still useful for driver behavior detection. We develop a deep learning model to learn the HSIC sanitizer and demonstrate through two datasets that our approach achieves better utility-privacy trade-offs when compared to three other benchmarks.

With Covid19 being endemic, it is very essential to continue proper physical hygiene protocols even today to avoid escalation. To ensure hygiene inside educational institutions, many governing bodies-imposed protocols to insist students wear hand gloves and facemasks. Such an implementation, however, has increased surgical waste in and around educational institutions, and also there is a rise in allergies due to the constant use of hand gloves by the students. Hence, a prototype of a hand sanitization-based attendance monitoring system has been proposed in the current research paper. This proposed sanitizer with attendance through remote monitoring (SWARM) uses Raspberry Pi devices to capture the image of a student’s identity card holding the registration number and through a bar code analysis module of computer vision, the ID number is extracted. This ID number is compared with a master attendance file to mark the students’ presence and then the updated file is shared with the concerned teacher via email. Such a setup is installed in the laboratory premise, thereby reducing the unnecessary use and disposal of surgical waste within the educational premise.

Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. There are several known defensive mechanisms for handling offline attacks, however defensive measures for online learning, where data points arrive sequentially, have not garnered similar interest. In this work, we propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup. Our proposed method utilizes an influence function which is a classic technique in robust statistics. Further, we supplement it with the existing data sanitization methods for filtering out some of the poisoned data points. We study the effectiveness of our defense mechanism on multiple datasets and across multiple attack strategies against an online learner.

Technology plays a vital role in our lives to meet basic hygiene necessities. Currently, the whole world is facing an epidemic situation and the practice of using sanitizers is common nowadays. Sanitizers are used by people to sanitize their hands and bodies. It is also used for sanitizing objects that come into contact with the machine. While sanitizing a small area, people manage to sanitize via pumps, but it becomes difficult to sanitize the same area every day. One of the most severe sanitation concerns is a simple, economic and efficient method to adequately clean the indoor and outdoor environments. In particular, effective sanitization is required for people working in a clinical environment. Recently, some commonly used sanitizer techniques include electric sanitizer spray guns, electric sanitizer disinfectants, etc. However, these sanitizers are not automated, which means a person is required to roam personally with the device to every place to spray the disinfectant or sanitize an area. Therefore, a novel, cost-effective automatic sanitizing machine (ASM) named ASMBoT is designed that can dispense the sanitizer effectively by solving the aforementioned problems.

In recent years, new types of cyber attacks called targeted attacks have been observed. It targets specific organizations or individuals, while usual large-scale attacks do not focus on specific targets. Organizations have published many Word or PDF files on their websites. These files may provide the starting point for targeted attacks if they include hidden data unintentionally generated in the authoring process. Adhatarao and Lauradoux analyzed hidden data found in the PDF files published by security agencies in many countries and showed that many PDF files potentially leak information like author names, details on the information system and computer architecture. In this study, we analyze hidden data of PDF files published on the website of police agencies in Japan and compare the results with Adhatarao and Lauradoux's. We gathered 110989 PDF files. 56% of gathered PDF files contain personal names, organization names, usernames, or numbers that seem to be IDs within the organizations. 96% of PDF files contain software names.

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings - and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS. In this paper, we conduct the first black-box study measuring the extent of ReDoS vulnerabilities in live web services. We apply the Consistent Sanitization Assumption: that client-side sanitization logic, including regexes, is consistent with the sanitization logic on the server-side. We identify a service's regex-based input sanitization in its HTML forms or its API, find vulnerable regexes among these regexes, craft ReDoS probes, and pinpoint vulnerabilities. We analyzed the HTML forms of 1,000 services and the APIs of 475 services. Of these, 355 services publish regexes; 17 services publish unsafe regexes; and 6 services are vulnerable to ReDoS through their APIs (6 domains; 15 subdomains). Both Microsoft and Amazon Web Services patched their web services as a result of our disclosure. Since these vulnerabilities were from API specifications, not HTML forms, we proposed a ReDoS defense for a popular API validation library, and our patch has been merged. To summarize: in client-visible sanitization logic, some web services advertise Re-DoS vulnerabilities in plain sight. Our results motivate short-term patches and long-term fundamental solutions. “Make measurable what cannot be measured.” -Galileo Galilei

Machine learning (ML) models are increasingly being used in the development of Malware Detection Systems. Existing research in this area primarily focuses on developing new architectures and feature representation techniques to improve the accuracy of the model. However, recent studies have shown that existing state-of-the art techniques are vulnerable to adversarial machine learning (AML) attacks. Among those, data poisoning attacks have been identified as a top concern for ML practitioners. A recent study on clean-label poisoning attacks in which an adversary intentionally crafts training samples in order for the model to learn a backdoor watermark was shown to degrade the performance of state-of-the-art classifiers. Defenses against such poisoning attacks have been largely under-explored. We investigate a recently proposed clean-label poisoning attack and leverage an ensemble-based Nested Training technique to remove most of the poisoned samples from a poisoned training dataset. Our technique leverages the relatively large sensitivity of poisoned samples to feature noise that disproportionately affects the accuracy of a backdoored model. In particular, we show that for two state-of-the art architectures trained on the EMBER dataset affected by the clean-label attack, the Nested Training approach improves the accuracy of backdoor malware samples from 3.42% to 93.2%. We also show that samples produced by the clean-label attack often successfully evade malware classification even when the classifier is not poisoned during training. However, even in such scenarios, our Nested Training technique can mitigate the effect of such clean-label-based evasion attacks by recovering the model's accuracy of malware detection from 3.57% to 93.2%.

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.

The requirements of much larger file sizes, different storage formats, and immersive viewing conditions pose significant challenges to the goals of compressing VR content. At the same time, the great potential of deep learning to advance progress on the video compression problem has driven a significant research effort. Because of the high bandwidth requirements of VR, there has also been significant interest in the use of space-variant, foveated compression protocols. We have integrated these techniques to create an end-to-end deep learning video compression framework. A feature of our new compression model is that it dispenses with the need for expensive search-based motion prediction computations by using displaced frame differences. We also implement foveation in our learning based approach, by introducing a Foveation Generator Unit (FGU) that generates foveation masks which direct the allocation of bits, significantly increasing compression efficiency while making it possible to retain an impression of little to no additional visual loss given an appropriate viewing geometry. Our experiment results reveal that our new compression model, which we call the Foveated MOtionless VIdeo Codec (Foveated MOVI-Codec), is able to efficiently compress videos without computing motion, while outperforming foveated version of both H.264 and H.265 on the widely used UVG dataset and on the HEVC Standard Class B Test Sequences.

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 60% compared to HEVC.

With the rapid development of multimedia and short video, there is a growing concern for video copyright protection. Some work has been proposed to add some copyright or fingerprint information to the video to trace the source of the video when it is stolen and protect video copyright. This paper proposes a video watermarking method based on a deep neural network and curriculum learning for watermarking of sliced videos. The first frame of the segmented video is perturbed by an encoder network, which is invisible and can be distinguished by the decoder network. Our model is trained and tested on an online educational video dataset consisting of 2000 different video clips. Experimental results show that our method can successfully discriminate most watermarked and non-watermarked videos with low visual disturbance, which can be achieved even under a relatively high video compression rate(H.264 video compress with CRF 32).

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards.

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored “big” surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding.

With the rapid development of artificial intelligence, video target tracking is widely used in the fields of intelligent video surveillance, intelligent transportation, intelligent human-computer interaction and intelligent medical diagnosis. Deep learning has achieved remarkable results in the field of computer vision. The development of deep learning not only breaks through many problems that are difficult to be solved by traditional algorithms, improves the computer's cognitive level of images and videos, but also promotes the progress of related technologies in the field of computer vision. This paper combines the deep learning algorithm and target tracking algorithm to carry out relevant experiments on basketball motion detection video, hoping that the experimental results can be helpful to basketball motion detection video target tracking.