MANET Security - Many systems have recently begun to examine blockchain qualities in order to create cooperation enforcement methods. This paper provides a complete aod extensive evaluation of work on multi-hop MANETs with blockchain-based trust control between nodes. We contextualize tbe snag of security in MANETs resulting from the lack of trust between the participating nodes. We present tbe blockchain concepts aod discuss tbe limitation of tbe current blockchain in MANETs. We review the promising proposed ideas in the state-of-the-art based on research papers. FinaUy, we discuss aod summarize strategies and chaUenges for further research.

Microelectronics Security - In this paper, we present research on the analysis of the design space for cybersecurity visualizations in VizSec. At the beginning of this research, we analyzed 17 survey papers in the field of cybersecurity visualization. Based on the analysis of the focus areas in each of these survey papers, we identified five key components of visualization design, i.e. Input Data, Security Tasks, Visual Encoding, Interactivity, and Evaluation. To show how research papers align with these components, we analyzed 60 papers published at the IEEE Symposium on Visualization for Cyber Security (VizSec) between 2016 and 2021 in the context of the five identified components. As a result, each research paper was classified into several categories derived from the selected components of the visualization design. Our contributions are: (i) an analysis of the focus areas in survey papers on cybersecurity visualization and (ii) the classification of 60 research papers in the context of the selected components of the visualization design. Finally, we highlighted the main findings of the analysis and drew conclusions.

Microelectronics Security - A mail spoofing attack is a harmful activity that modifies the source of the mail and trick users into believing that the message originated from a trusted sender whereas the actual sender is the attacker. Based on the previous work, this paper analyzes the transmission process of an email. Our work identifies new attacks suitable for bypassing SPF, DMARC, and Mail User Agent’s protection mechanisms. We can forge much more realistic emails to penetrate the famous mail service provider like Tencent by conducting the attack. By completing a large-scale experiment on these well-known mail service providers, we find some of them are affected by the related vulnerabilities. Some of the bypass methods are different from previous work. Our work found that this potential security problem can only be effectively protected when all email service providers have a standard view of security and can configure appropriate security policies for each email delivery node. In addition, we also propose a mitigate method to defend against these attacks. We hope our work can draw the attention of email service providers and users and effectively reduce the potential risk of phishing email attacks on them.

Microelectronics Security - The boundaries between the real world and the virtual world are going to be blurred by Metaverse. It is transforming every aspect of humans to seamlessly transition from one virtual world to another. It is connecting the real world with the digital world by integrating emerging tech like 5G, 3d reconstruction, IoT, Artificial intelligence, digital twin, augmented reality (AR), and virtual reality (VR). Metaverse platforms inherit many security \& privacy issues from underlying technologies, and this might impede their wider adoption. Emerging tech is easy to target for cybercriminals as security posture is in its infancy. This work elaborates on current and potential security, and privacy risks in the metaverse and put forth proposals and recommendations to build a trusted ecosystem in a holistic manner.

Microelectronics Security - The need for safe large data storage services is at an all-time high and confidentiality is a fundamental need of any service. Consideration must also be given to service customer anonymity, one of the most important privacy considerations. As a result, the service should offer realistic and fine-grained [11] encrypted data sharing, which allows a data owner to share a cipher text of data with others under certain situations. In order to accomplish the aforesaid characteristics, our system offers a novel privacy- preserving cipher text multi-sharing technique. In this way, proxy re-encryption and anonymity are combined to allow many receivers to safely and conditionally receive a cipher text while maintaining the confidentiality of the underlying message and the identities of the senders and recipients. In this paper, a logical cloud security scheme is introduced called Modified Data Cipher Policies (MDCP), in which it is a new primitive also protects against known cipher text attacks, as demonstrated by the system.

Microelectronics Security - In recent years, information and communication systems have experienced serious security issues due to the rising popularity of image-sharing platforms and the ubiquity of numerous smart electronic devices. The increased volume of data generated by the medical and clinical communities necessitates the use of such advanced platforms for data exchange. As a result, the implementation of improved procedures and resources in terms of storage and security is essential. This research proposes a novel medical image encryption method based on chaos sequence and the modified Twofish algorithm. A quick and more efficient algorithm than current methods is built using chaos-based image encryption methods. The modified algorithm can be applied for hardware applications.

Microelectronics Security - By analyzing the current research status at home and abroad, researching and analyzing the system requirements, we develops and designs an environmental and security system based on NB-IoT and ZigBee protocols, so that the sensor data collected on the device side can realize realtime data monitoring and home environment safety alarm on the open-source control platform and user terminal. Finally, we test and demonstrate the system and summarize the results and future prospects.

Microelectronics Security - In practice, different styles of side channel attacks can utilize the leakages of a crypto device to recover the used secret key, which can pose a serious threat on the physical security of a crypto device. Among different styles of side channel attacks, template attack can be information theoretically the strongest attack style. However, numerical problems can seriously influence the key-recovery efficiency of template attack in practice, which can make template attack useless in practice. In light of this, the variance analysis based distinguisher is proposed for template attack. Compared with the classical template attack, variance analysis based template attack can reduce the computational complexity of template attack from O(d3) to O(d), where d denotes the number of interesting points. Besides, numerical problems do not exist anymore. Therefore, a large number of interesting points can be chosen to enlarge the leakage exploitation and accordingly optimize the key-recovery efficiency of template attack. The key-recovery efficiency of variance analysis based template attack is evaluated in both simulated and real scenarios, and the evaluation results show that compared with the classical template attack, variance analysis based template attack can maintain a high key-recovery efficiency while significantly decrease the number of traces that should be used in the profiling phase of template attack.

Microelectronics Security - In this paper, we propose a Chaotic Probability Constellation Shaping (CPCS) method in Free-Space Optical (FSO) communication to enhance security and improve the performance of the transmission data. Gather as many points as possible in the middle via chaotic controlling. The influence of turbulence on the signal transmission can be attenuated to the minimum. In the simulation, a ratio of 56Gb/s 16-QAM signal is transmitted 1-km space channel with an attenuation index of 10dB/km. The CPCS technique can improve almost 0.5 dB optical signal noise ratio (OSNR) performance @10-3 BER than that of the related original signal. Simulation results indicate that the proposed method not only enhances the security but also improves the BER performance.

Microelectronics Security - With the increasing improvement of network security technology, network security management is forming a closedloop process of transitioning from post-fire fighting to prechecking, real-time monitoring and protection, and postdisposal reinforcement. This paper introduces a new system based on network asset risk assessment and network asset security protection, which is capable of detecting unrepaired security vulnerabilities in network assets and monitoring users’ assets for compliance, and notifying them if there are problems, and also has SYSLOG asset upload technology for uploading asset changes.

Microelectronics Security - Web application security is the most important area when it comes to developing a web application. Many web applications having vulnerabilities due to poor implementation of security measures. These web applications will be deployed without fixing the vulnerabilities thus becomes vulnerable to many cyber-attacks. Simple attacks like brute-force and NoSQL injection could give unauthorized access to the user accounts. This leads to user privacy issues which could create huge loss to the organizations. These vulnerabilities can be fixed by implementing the necessary security measures while developing the web application. OWASP (Open Web Application Security Project) is a non-profit organization which gives the severity, impact and prevention methods about Top 10 vulnerabilities in web applications. This research deals with the implementation of bestsecurity practices for Node.js web applications in detail. This research paper proposes the security mechanisms for attacks related to front-end, middleware and backend web development using OWASP suggestions. The main focus of this research paper is on prevention of Denial-of-service attack, Brute force attack, NoSQL injection attack and Unrestricted file upload vulnerability.The proposed prevention methods are implemented in a web application to test the defensive mechanisms against the mentionedvulnerabilities.

Malware Classification - With the rapid development of technology and the increase in the use of Android software, the number of malware has also increased. This study presents a classification as malware/goodware with the features of 4465 Android applications. Cost is an important problem for the increasing number of applications and the analyzes to be made on each application. This study focused on this problem with the hybrid use of Gray Wolf Optimization Algorithm (GWO) and Deep Neural Networks (DNN). With the use of GWO, both feature selection and the features of the model to be created with DNN are determined. In this way, an approximate solution proposal is presented for the most suitable features and the most suitable model design. The model, which was created with the use of GWO-DNN hybrid in this study, offers an F1 score of 99.74%.

Malware Classification - The past decades witness the development of various Machine Learning (ML) models for malware classification. Semantic representation is a crucial basis for these classifiers. This paper aims to assess the effect of semantic representation methods on malware classifier performance. Two commonly-used semantic representation methods including N-gram and GloVe. We utilize diverse ML classifiers to conduct comparative experiments to analyze the capability of N-gram, GloVe and image-based methods for malware classification. We also analyze deeply the reason why the GloVe can produce negative effects on malware static analysis.

Malware Classification - Automated malware classification assigns unknown malware to known families. Most research in malware classification assumes that the defender has access to the malware for analysis. Unfortunately, malware can delete itself after execution. As a result, analysts are only left with digital residue, such as network logs or remnant artifacts of malware in memory or on the file system. In this paper, a novel malware classification method based on the Windows prefetch mechanism is presented and evaluated, enabling analysts to classify malware without a corresponding executable. The approach extracts features from Windows prefetch files, a file system artifact that contains historical process information such as loaded libraries and process dependencies. Results show that classification using these features with two different algorithms garnered F-Scores between 0.80 and 0.82, offering analysts a viable option for forensic analysis.

Malware Classification - Nowadays, increasing numbers of malicious programs are becoming a serious problem, which increases the need for automated detection and categorization of potential threats. These attacks often use undetected malware that is not recognized by the security vendor, making it difficult to protect the endpoints from viruses. Existing methods have been proposed to detect malware. However, as malware variations develop, they can lead to misdiagnosis and are difficult to diagnose accurately. To address this problem, in this work introduces a Recurrent Neural Network (RNN) to identify the malware or benign based on extract features using Information Gain Absolute Feature Selection (IGAFS) technique. First, Malware detection dataset is collected from kaggle repository. Then the proposed pre-process the dataset for removing null and noisy values to prepare the dataset. Next, the proposed Information Gain Absolute Feature Selection (IGAFS) technique is used to select most relevant features for malware from the pre-processed dataset. Selected features are trained into Recurrent Neural Network (RNN) method to classify as malware or not with better accuracy and false rate. The experimental result provides greater performance compared with previous methods.

Malware Classification - Mobile devices play a crucial role and have become an essential part of people's life particularly with online applications such as shopping, learning, mailing, etc. Android OS has continued to drive the market for other operating systems since 2012. Traditional Android malware detection methods, such as static, dynamic, hybrid analysis, or the Bayesian model, may show less accuracy to detect recent Android malware. We propose a deep learning method for Android malware detection using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). CNN provides efficient feature extraction from data and the use of additional LSTM layers improves prediction accuracy. According to the test results, CNN-LSTM can provide reliable malware prediction in Android applications. We train and test our approach using the CICMalDroid2020 dataset. The test results show that the CNN-LSTM classifier exceeds with an accuracy of 94%.

Malware Classification - Traditional methods of malware detection have difficulty in detecting massive malware variants. Malware detection based on malware visualization has been proved an effective method for identifying unknown malware variants. In order to improve the accuracy and reduce the detection time of above methods, a novel method for malware classification in a light-weight CNN architecture named MalshuffleNet is proposed. The model is customized based on ShuffleNet V2 by adjusting the numbers of the fully connected layer for adopting to malware classification. Empirical results on Malimg dataset indicate that our model achieves 99.03% in accuracy, and identify an unknown malware only taking 5.3 milliseconds on average.

Malware Classification - Malware attack is a severe problem that can cause a considerable loss. To prevent the malware attack, different malware detection and classification method have been implemented in recent years. This paper proposed a new method based on Markov image and transfer learning on machine learning. Also, an experience comparing the performance on malware detection and classification between the proposed and grayscale methods was done. The accuracy and loss of malware detection and classification by using the proposed method are 0.973 and 0.076, 0.987 and 0.062 respectively. The accuracy and loss of malware detection and classification using the grayscale method are 0.989 and 0.037, 0.973 and 0.202 respectively. Although the grayscale method has done better in malware detection, the proposed method's accuracy is over 0.97. Therefore, the result shows that the proposed method are suitable for malware detection and classification.

Malware Classification - Due to the constant updates of malware and its variants and the continuous development of malware obfuscation techniques. Malware intrusions targeting Windows hosts are also on the rise. Traditional static analysis methods such as signature matching mechanisms have been difficult to adapt to the detection of new malware. Therefore, a novel visual detection method of malware is proposed for first-time to convert the Windows API call sequence with sequential nature into feature images based on the Gramian Angular Field (GAF) idea, and train a neural network to identify malware. The experimental results demonstrate the effectiveness of our proposed method. For the binary classification of malware, the GAF visualization image of the API call sequence is compared with its original sequence. After GAF visualization, the classification accuracy of the classic machine learning model MLP is improved by 9.64%, and the classification accuracy of the deep learning model CNN is improved by 4.82%. Furthermore, our experiments show that the proposed method is also feasible and effective for the multi-class classification of malware.

Malware Classification - Rapid digitalisation spurred by the Covid-19 pandemic has resulted in more cyber crime. Malware-as-a-service is now a booming business for cyber criminals. With the surge in malware activities, it is vital for cyber defenders to understand more about the malware samples they have at hand as such information can greatly influence their next course of actions during a breach. Recently, researchers have shown how malware family classification can be done by first converting malware binaries into grayscale images and then passing them through neural networks for classification. However, most work focus on studying the impact of different neural network architectures on classification performance. In the last year, researchers have shown that augmenting supervised learning with self-supervised learning can improve performance. Even more recently, Data2Vec was proposed as a modality agnostic self-supervised framework to train neural networks. In this paper, we present BinImg2Vec, a framework of training malware binary image classifiers that incorporates both self-supervised learning and supervised learning to produce a model that consistently outperforms one trained only via supervised learning. We also show how our framework produces outputs that facilitate explanability.

Malware Classification - Methodologies used for the detection of malicious applications can be broadly classified into static and dynamic analysis based approaches. With traditional signature-based methods, new variants of malware families cannot be detected. A combination of deep learning techniques along with image-based features is used in this work to classify malware. The data set used here is the ‘Malimg’ dataset, which contains a pictorial representation of well-known malware families. This paper proposes a methodology for identifying malware images and classifying them into various families. The classification is based on image features. The features are extracted using the pre-trained model namely VGG16. The samples of malware are depicted as byteplot grayscale images. Features are extracted employing the convolutional layer of a VGG16 deep learning network, which uses ImageNet dataset for the pre-training step. The features are used to train different classifiers which employ SVM, XGBoost, DNN and Random Forest for the classification task into different malware families. Using 9339 samples from 25 different malware families, we performed experimental evaluations and demonstrate that our approach is effective in identifying malware families with high accuracy.

MANET Attack Detection - Mobile Adhoc Networks also known as MANETS or Wireless Adhoc Networks is a network that usually has a routable networking environment on top of a Link Layer ad hoc network. They consist of a set of mobile nodes connected wirelessly in a self-configured, self-healing network without having a fixed infrastructure. MANETS, have been predominantly utilized in military or emergency situations however, the prospects of Manets’ usage outside these realms is now being considered for possible public adoption in light of the recent global events such as the pandemic and new emerging infectious diseases. These particular events birthed new challenges, one of which was the considerable strain that was placed on mainstream ISP’s. Whilst there has been a significant amount of research conducted in the sphere Manet Security via various means such as: development of intrusion detection systems, attack classification and prediction systems, etcetera. There still exists prevailing concerns of MANET security and risks. Additionally, recently researched trends within the field has evidenced key disparities in terms of studies related to MANET Risk profiles. This paper seeks to provide an overview of existing studies with respect to MANETS as well as briefly introduces a new method of determining the initial Risk Profile of MANETS via the usage of probabilistic machine learning techniques. It explores new regions of probability-based approaches to further supplement the existing impact-based methodologies for assessing risk within Manets.

MANET Attack Detection - Mobile Ad-hoc network (MANET) has improved to be essential components of our daily lives. Due to its compatibility with multimedia data interchange in a mobile context, MANETs are employed in a variety of applications today, including those for crisis management and the battlefield, The popularity of infrastructure-less networks has grown along with the popularity of ad hoc networks in recent years as a result of the rise in wireless devices and technological developments MANETs have brought about a new type of technologies that allow them to operate without a fixed infrastructure. The dynamic nature of the MANET network makes it susceptible to numerous attacks. One of these is the wormhole, which spreads data from one site to another and can damage the network. If the source node chooses this fictitious route, the attacker has a backup plan to deliver or drop packets. In this paper, we proposed a technique by modifying the Ad-hoc On-demand Distance vector protocol (AODV) in the stage of RREQ and RREP with the sequence number transaction and the detection timer(DT). The proposed method when reached to 100 nodes, achieved the throughput of 95.5kbps, energy consumption of 55.9joule, end to end delay of 0.973sec and Packet Delivery Ratio (PDR) of 96.5%.

MANET Attack Detection - Nodes in a “distributed” Adhoc network do not share a single centralized infrastructure. Hosts and routers can be found on any mobile node. In addition, it sends packets to additional mobile nodes in the network that aren't directly connected to the main network. Network layer assaults such as black hole, wormhole, and denial-of-service (DoS) are all easily carried out on mobile Ad hoc networks (MANETs). Wrong-way attacks, which divert packets from one part of the network and route them through an alternate one, are extremely difficult to detect. Even though the wormhole attack has been countered, the current solutions still suffer from excessive delivery delays, packet delivery ratio issues, and energy consumption. In this paper, a cluster-based algorithm (CBA) detects hybrid wormhole assaults by computing based on sequence number, round-trip time (RTT), which is more optimistic than existing solutions for detecting both in-band and out-of-band connections are possible. RTT thresholds are predicted in this paper using CBA to distinguish between attack and non-attack routes. NS-2 network simulator is used to test the suggested technique. The proposed algorithm's performance was evaluated by looking at its throughput. Results demonstrate that CBA reduced 20% of total energy consumption compared to AODV, the traditional On-Demand Ad-hoc Distance Vector routing protocol.

MANET Attack Detection - One of the most essential self-configuring and independent wireless networks is the MANET. MANET employs a large number of intermediate nodes to exchange information without the need for any centralized infrastructure. However, some nodes act in a selfish manner, utilizing the network's resources solely for their own benefit and refusing to share with the surrounding nodes. Mobile ad hoc network security is a critical factor that is widely accepted. Selfish nodes are the primary problem of MANET. In a MANET, nodes that are only interested in themselves do not involve in the process of packet forwarding. A node can be identified as selfish or malicious due to some misbehavior reasons. Selfishness on the part of network nodes may be a factor in the low delivery ratio of packets and data loss. A high end-to-end delay is caused by node failure in a MANET network. To study the selfish node attack, a malicious selfish node is put into the network, and a trust-based algorithm for the selfish node attack is also suggested. In order to discover a solution to this issue, we have developed an algorithm called SNRM for the detection of selfish nodes. The routing protocol used in this paper for analysis is AODV. Using a simulation tool, PDR and end-to-end delay are evaluated and compared.