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Wang, Xiangwen, Peng, Peng, Wang, Chun, Wang, Gang.  2018.  You Are Your Photographs: Detecting Multiple Identities of Vendors in the Darknet Marketplaces. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :431-442.

Darknet markets are online services behind Tor where cybercriminals trade illegal goods and stolen datasets. In recent years, security analysts and law enforcement start to investigate the darknet markets to study the cybercriminal networks and predict future incidents. However, vendors in these markets often create multiple accounts ($\backslash$em i.e., Sybils), making it challenging to infer the relationships between cybercriminals and identify coordinated crimes. In this paper, we present a novel approach to link the multiple accounts of the same darknet vendors through photo analytics. The core idea is that darknet vendors often have to take their own product photos to prove the possession of the illegal goods, which can reveal their distinct photography styles. To fingerprint vendors, we construct a series deep neural networks to model the photography styles. We apply transfer learning to the model training, which allows us to accurately fingerprint vendors with a limited number of photos. We evaluate the system using real-world datasets from 3 large darknet markets (7,641 vendors and 197,682 product photos). A ground-truth evaluation shows that the system achieves an accuracy of 97.5%, outperforming existing stylometry-based methods in both accuracy and coverage. In addition, our system identifies previously unknown Sybil accounts within the same markets (23) and across different markets (715 pairs). Further case studies reveal new insights into the coordinated Sybil activities such as price manipulation, buyer scam, and product stocking and reselling.

Benzer, R., Yildiz, M. C..  2018.  YOLO Approach in Digital Object Definition in Military Systems. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT). :35–37.

Today, as surveillance systems are widely used for indoor and outdoor monitoring applications, there is a growing interest in real-time generation detection and there are many different applications for real-time generation detection and analysis. Two-dimensional videos; It is used in multimedia content-based indexing, information acquisition, visual surveillance and distributed cross-camera surveillance systems, human tracking, traffic monitoring and similar applications. It is of great importance for the development of systems for national security by following a moving target within the scope of military applications. In this research, a more efficient solution is proposed in addition to the existing methods. Therefore, we present YOLO, a new approach to object detection for military applications.

Narwal, Bhawna, Ojha, Arushi, Goel, Nimisha, Dhawan, Sudipti.  2020.  A Yoking-Proof Based Remote Authentication Scheme for Cloud-Aided Wearable Devices (YPACW). 2020 IEEE International Conference for Innovation in Technology (INOCON). :1—5.

The developments made in IoT applications have made wearable devices a popular choice for collecting user data to monitor this information and provide intelligent service support. Since wearable devices are continuously collecting and transporting a user's sensitive data over the network, there exist increased security challenges. Moreover, wearable devices lack the computation capabilities in comparison to traditional short-range communication devices. In this paper, authors propounded a Yoking Proof based remote Authentication scheme for Cloud-aided Wearable devices (YPACW) which takes PUF and cryptographic functions and joins them to achieve mutual authentication between the wearable devices and smartphone via a cloud server, by performing the simultaneous verification of these devices, using the established yoking-proofs. Relative to Liu et al.'s scheme, YPACW provides better results with the reduction of communication and processing cost significantly.

Mehndiratta, Nishtha.  2020.  A Yoking-Proof and PUF-based Mutual Authentication Scheme for Cloud-aided Wearable Devices. 2020 IEEE International Conference for Innovation in Technology (INOCON). :1—4.

In today's world privacy is paramount in everyone's life. Alongside the growth of IoT (Internet of things), wearable devices are becoming widely popular for real-time user monitoring and wise service support. However, in contrast with the traditional short-range communications, these resource-scanty devices face various vulnerabilities and security threats during the course of interactions. Hence, designing a security solution for these devices while dealing with the limited communication and computation capabilities is a challenging task. In this work, PUF (Physical Unclonable Function) and lightweight cryptographic parameters are used together for performing two-way authentication between wearable devices and smartphone, while the simultaneous verification is performed by providing yoking-proofs to the Cloud Server. At the end, it is shown that the proposed scheme satisfies many security aspects and is flexible as well as lightweight.

Ye, Guixin, Tang, Zhanyong, Fang, Dingyi, Zhu, Zhanxing, Feng, Yansong, Xu, Pengfei, Chen, Xiaojiang, Wang, Zheng.  2018.  Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :332–348.
Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas.
Sani, Abubakar Sadiq, Yuan, Dong, Bao, Wei, Yeoh, Phee Lep, Dong, Zhao Yang, Vucetic, Branka, Bertino, Elisa.  2019.  Xyreum: A High-Performance and Scalable Blockchain for IIoT Security and Privacy. 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). :1920–1930.
As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.
Rodriguez, German, Torres, Jenny, Flores, Pamela, Benavides, Eduardo, Nuñez-Agurto, Daniel.  2019.  XSStudent: Proposal to Avoid Cross-Site Scripting (XSS) Attacks in Universities. 2019 3rd Cyber Security in Networking Conference (CSNet). :142–149.
QR codes are the means to offer more direct and instant access to information. However, QR codes have shown their deficiency, being a very powerful attack vector, for example, to execute phishing attacks. In this study, we have proposed a solution that allows controlling access to the information offered by QR codes. Through a scanner designed in APP Inventor which has been called XSStudent, a system has been built that analyzes the URLs obtained and compares them with a previously trained system. This study was executed by means of a controlled attack to the users of the university who through a flyer with a QR code and a fictional link accessed an infected page with JavaScript code that allowed a successful cross-site scripting attack. The results indicate that 100% of the users are vulnerable to this type of attacks, so also, with our proposal, an attack executed in the universities using the Beef software would be totally blocked.
Gupta, M. K., Govil, M. C., Singh, G., Sharma, P..  2015.  XSSDM: Towards detection and mitigation of cross-site scripting vulnerabilities in web applications. 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :2010–2015.

With the growth of the Internet, web applications are becoming very popular in the user communities. However, the presence of security vulnerabilities in the source code of these applications is raising cyber crime rate rapidly. It is required to detect and mitigate these vulnerabilities before their exploitation in the execution environment. Recently, Open Web Application Security Project (OWASP) and Common Vulnerabilities and Exposures (CWE) reported Cross-Site Scripting (XSS) as one of the most serious vulnerabilities in the web applications. Though many vulnerability detection approaches have been proposed in the past, existing detection approaches have the limitations in terms of false positive and false negative results. This paper proposes a context-sensitive approach based on static taint analysis and pattern matching techniques to detect and mitigate the XSS vulnerabilities in the source code of web applications. The proposed approach has been implemented in a prototype tool and evaluated on a public data set of 9408 samples. Experimental results show that proposed approach based tool outperforms over existing popular open source tools in the detection of XSS vulnerabilities.

Chaudhary, P., Gupta, B. B., Yamaguchi, S..  2016.  XSS detection with automatic view isolation on online social network. 2016 IEEE 5th Global Conference on Consumer Electronics. :1–5.

Online Social Networks (OSNs) are continuously suffering from the negative impact of Cross-Site Scripting (XSS) vulnerabilities. This paper describes a novel framework for mitigating XSS attack on OSN-based platforms. It is completely based on the request authentication and view isolation approach. It detects XSS attack through validating string value extracted from the vulnerable checkpoint present in the web page by implementing string examination algorithm with the help of XSS attack vector repository. Any similarity (i.e. string is not validated) indicates the presence of malicious code injected by the attacker and finally it removes the script code to mitigate XSS attack. To assess the defending ability of our designed model, we have tested it on OSN-based web application i.e. Humhub. The experimental results revealed that our model discovers the XSS attack vectors with low false negatives and false positive rate tolerable performance overhead.

Lei, L., Chen, M., He, C., Li, D..  2020.  XSS Detection Technology Based on LSTM-Attention. 2020 5th International Conference on Control, Robotics and Cybernetics (CRC). :175—180.
Cross-site scripting (XSS) is one of the main threats of Web applications, which has great harm. How to effectively detect and defend against XSS attacks has become more and more important. Due to the malicious obfuscation of attack codes and the gradual increase in number, the traditional XSS detection methods have some defects such as poor recognition of malicious attack codes, inadequate feature extraction and low efficiency. Therefore, we present a novel approach to detect XSS attacks based on the attention mechanism of Long Short-Term Memory (LSTM) recurrent neural network. First of all, the data need to be preprocessed, we used decoding technology to restore the XSS codes to the unencoded state for improving the readability of the code, then we used word2vec to extract XSS payload features and map them to feature vectors. And then, we improved the LSTM model by adding attention mechanism, the LSTM-Attention detection model was designed to train and test the data. We used the ability of LSTM model to extract context-related features for deep learning, the added attention mechanism made the model extract more effective features. Finally, we used the classifier to classify the abstract features. Experimental results show that the proposed XSS detection model based on LSTM-Attention achieves a precision rate of 99.3% and a recall rate of 98.2% in the actually collected dataset. Compared with traditional machine learning methods and other deep learning methods, this method can more effectively identify XSS attacks.
Habibi, G., Surantha, N..  2020.  XSS Attack Detection With Machine Learning and n-Gram Methods. 2020 International Conference on Information Management and Technology (ICIMTech). :516–520.

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.

Wang, An, Mohaisen, Aziz, Chen, Songqing.  2019.  XLF: A Cross-layer Framework to Secure the Internet of Things (IoT). 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). :1830–1839.
The burgeoning Internet of Things (IoT) has offered unprecedented opportunities for innovations and applications that are continuously changing our life. At the same time, the large amount of pervasive IoT applications have posed paramount threats to the user's security and privacy. While a lot of efforts have been dedicated to deal with such threats from the hardware, the software, and the applications, in this paper, we argue and envision that more effective and comprehensive protection for IoT systems can only be achieved via a cross-layer approach. As such, we present our initial design of XLF, a cross-layer framework towards this goal. XLF can secure the IoT systems not only from each individual layer of device, network, and service, but also through the information aggregation and correlation of different layers.
Guri, M., Zadov, B., Daidakulov, A., Elovici, Y..  2018.  xLED: Covert Data Exfiltration from Air-Gapped Networks via Switch and Router LEDs. 2018 16th Annual Conference on Privacy, Security and Trust (PST). :1–12.

An air-gapped network is a type of IT network that is separated from the Internet - physically - due to the sensitive information it stores. Even if such a network is compromised with a malware, the hermetic isolation from the Internet prevents an attacker from leaking out any data - thanks to the lack of connectivity. In this paper we show how attackers can covertly leak sensitive data from air-gapped networks via the row of status LEDs on networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device (‘side-channel'), malware controlling the status LEDs to carry any type of data (‘covert-channel') has never studied before. Sensitive data can be covertly encoded over the blinking of the LEDs and received by remote cameras and optical sensors. A malicious code is executed in a compromised LAN switch or router allowing the attacker direct, low-level control of the LEDs. We provide the technical background on the internal architecture of switches and routers at both the hardware and software level which enables these attacks. We present different modulation and encoding schemas, along with a transmission protocol. We implement prototypes of the malware and discuss its design and implementation. We tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and discuss detection and prevention countermeasures. Our experiments show that sensitive data can be covertly leaked via the status LEDs of switches and routers at bit rates of 1 bit/sec to more than 2000 bit/sec per LED.

Aljuhani, Ahamed, Alharbi, Talal, Liu, Hang.  2017.  XFirewall: A Dynamic and Additional Mitigation Against DDoS Storm. Proceedings of the International Conference on Compute and Data Analysis. :1–5.

The Distributed Denial of Service (DDoS) attack is a main concern in network security. Since the attackers have developed different techniques and methods, preventing DDoS attacks has become more difficult. Traditional firewall is ineffective in preventing DDoS attacks. In this paper, we propose a new type of firewall named XFirewall to defend against DDoS attacks. XFirewall is a temporary firewall and is created when an attack occurs. Also, XFirewall will be configured with dynamic rules based on real-time traffic analysis. We will discuss in detail the design and algorithm for generating an XFirewall.

Johnston, Reece, Kim, Sun-il, Coe, David, Etzkorn, Letha, Kulick, Jeffrey, Milenkovic, Aleksandar.  2016.  Xen Network Flow Analysis for Intrusion Detection. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :18:1–18:4.

Virtualization technology has become ubiquitous in the computing world. With it, a number of security concerns have been amplified as users run adjacently on a single host. In order to prevent attacks from both internal and external sources, the networking of such systems must be secured. Network intrusion detection systems (NIDSs) are an important tool for aiding this effort. These systems work by analyzing flow or packet information to determine malicious intent. However, it is difficult to implement a NIDS on a virtualized system due to their complexity. This is especially true for the Xen hypervisor: Xen has incredible heterogeneity when it comes to implementation, making a generic solution difficult. In this paper, we analyze the network data flow of a typical Xen implementation along with identifying features common to any implementation. We then explore the benefits of placing security checks along the data flow and promote a solution within the hypervisor itself.

Agarwal, N., Paul, K..  2016.  XEBRA: XEn Based Remote Attestation. 2016 IEEE Region 10 Conference (TENCON). :2383–2386.

Modern computing environments are increasingly getting distributed with one machine executing programs on the other remotely. Often, multiple machines work together to complete a task. Its important for collaborating machines to trust each other in order to perform properly. Such scenarios have brought up a key security issue of trustably and securely executing critical code on remote machines. We present a purely software based remote attestation technique XEBRA(XEn Based Remote Attestation) that guarantees the execution of correct code on a remote host, termed as remote attestation. XEBRA can be used to establish dynamic root of trust in a remote computing device using virtualization. We also show our approach to be feasible on embedded platforms by implementing it on an Intel Galileo board.

Kalbarczyk, Tomasz, Julien, Christine.  2016.  XD (Exchange-deliver): \#a Middleware for Developing Device-to-device Mobile Applications. Proceedings of the International Conference on Mobile Software Engineering and Systems. :271–274.

In this demonstration, we showcase the XD middleware, a framework for expressive multiplexing of application communication streams onto underlying device-to-device communication links. XD allows applications to remain agnostic about which low-level networking stack is actually delivering messages and instead focus on the application-level content and delivery parameters. The IoT space has been flooded with new communication technologies (e.g., BLE, ZigBee, 6LoWPAN) to add to those already available on modern mobile devices (e.g., BLE, WiFi-Direct), substantially increasing the barrier to entry for developing innovative IoT applications. XD presents application developers with a simple publish-subscribe API for sending and receiving data streams, unburdening them from the task of selecting and coordinating communication channels. Our demonstration shows two Android applications, Disseminate and Prophet, running using our XD middleware for communication. We implemented BLE, WiFi Direct with TCP, and WiFi Direct with UDP communication stacks underneath XD.

Lo, Wai Weng, Yang, Xu, Wang, Yapeng.  2019.  An Xception Convolutional Neural Network for Malware Classification with Transfer Learning. 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS). :1—5.

In this work, we applied a deep Convolutional Neural Network (CNN) with Xception model to perform malware image classification. The Xception model is a recently developed special CNN architecture that is more powerful with less over- fitting problems than the current popular CNN models such as VGG16. However only a few use cases of the Xception model can be found in literature, and it has never been used to solve the malware classification problem. The performance of our approach was compared with other methods including KNN, SVM, VGG16 etc. The experiments on two datasets (Malimg and Microsoft Malware Dataset) demonstrated that the Xception model can achieve the highest training accuracy than all other approaches including the champion approach, and highest validation accuracy than all other approaches including VGG16 model which are using image-based malware classification (except the champion solution as this information was not provided). Additionally, we proposed a novel ensemble model to combine the predictions from .bytes files and .asm files, showing that a lower logloss can be achieved. Although the champion on the Microsoft Malware Dataset achieved a bit lower logloss, our approach does not require any features engineering, making it more effective to adapt to any future evolution in malware, and very much less time consuming than the champion's solution.

Raza, Ali, Zaki, Yasir, Pötsch, Thomas, Chen, Jay, Subramanian, Lakshmi.  2017.  xCache: Rethinking Edge Caching for Developing Regions. Proceedings of the Ninth International Conference on Information and Communication Technologies and Development. :5:1–5:11.

End-users in emerging markets experience poor web performance due to a combination of three factors: high server response time, limited edge bandwidth and the complexity of web pages. The absence of cloud infrastructure in developing regions and the limited bandwidth experienced by edge nodes constrain the effectiveness of conventional caching solutions for these contexts. This paper describes the design, implementation and deployment of xCache, a cloud-managed Internet caching architecture that aims to proactively profile popular web pages and maintain the liveness of popular content at software defined edge caches to enhance the cache hit rate with minimal bandwidth overhead. xCache uses a Cloud Controller that continuously analyzes active cloud-managed web pages and derives an object-group representation of web pages based on the objects of a page. Using this object-group representation, xCache computes a bandwidth-aware utility measure to derive the most valuable configuration for each edge cache. Our preliminary real-world deployment across university campuses in three developing regions demonstrates its potential compared to conventional caching by improving cache hit rates by about 15%. Our evaluations of xCache have also shown that it can be applied in conjunction with other web optimizations solutions like Shandian, and can improve page load times by more than 50%.

Tao, J., Xiong, Y., Zhao, S., Xu, Y., Lin, J., Wu, R., Fan, C..  2020.  XAI-Driven Explainable Multi-view Game Cheating Detection. 2020 IEEE Conference on Games (CoG). :144–151.
Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.
Meskauskas, Z., Jasinevicius, R., Kazanavicius, E., Petrauskas, V..  2020.  XAI-Based Fuzzy SWOT Maps for Analysis of Complex Systems. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1–8.
The classical SWOT methodology and many of the tools based on it used so far are very static, used for one stable project and lacking dynamics [1]. This paper proposes the idea of combining several SWOT analyses enriched with computing with words (CWW) paradigm into a single network. In this network, individual analysis of the situation is treated as the node. The whole structure is based on fuzzy cognitive maps (FCM) that have forward and backward chaining, so it is called fuzzy SWOT maps. Fuzzy SWOT maps methodology newly introduces the dynamics that projects are interacting, what exists in a real dynamic environment. The whole fuzzy SWOT maps network structure has explainable artificial intelligence (XAI) traits because each node in this network is a "white box"-all the reasoning chain can be tracked and checked why a particular decision has been made, which increases explainability by being able to check the rules to determine why a particular decision was made or why and how one project affects another. To confirm the vitality of the approach, a case with three interacting projects has been analyzed with a developed prototypical software tool and results are delivered.
Kacimi, Zineb, Benhlima, Laila.  2017.  XACML Policies into mongoDB for Privacy Access Control. Proceedings of the Mediterranean Symposium on Smart City Application. :9:1–9:5.

Nowadays Big data is considered as one of the major technologies used to manage a huge number of data, but there is little consideration of privacy in big data platforms. Indeed, developers don't focus on implementing security best practices in their programs to protect personal and sensitive data, and organizations can face financial lost because of this noncompliance with applied regulations. In this paper, we propose a solution to insert privacy policies written in XACML (eXtensible Access Control Markup Language) in access control solution to NoSQL database, our solution can be used for NoSQL data store which doesn't t include many access control features, it aims basically to ensure fine grained access control considering purpose as the main parameter, we will focus on access control in document level, and apply this approach to MongoDB which is the most used NoSQL data store.

Jeong, Junho, Son, Yunsik, Oh, Seman.  2017.  The X86/64 Binary Code to Smart Intermediate Language Translation for Software Weakness. Proceedings of the International Conference on Advances in Image Processing. :129–134.

Today, the proportion of software in society as a whole is steadily increasing. In addition to size of software increasing, the number of cases dealing with personal information is also increasing. This shows the importance of weekly software security verification. However, software security is very difficult in cases where libraries do not have source code. To solve this problem, it is necessary to develop a technique for checking existing binary security weaknesses. To this end, techniques for analyzing security weaknesses using intermediate languages are actively being discussed. In this paper, we propose a system that translate binary code to intermediate language to effectively analyze existing security weaknesses within binary code.

Khanuja, H., Suratkar, S.S..  2014.  #x201C;Role of metadata in forensic analysis of database attacks #x201C;. Advance Computing Conference (IACC), 2014 IEEE International. :457-462.

With the spectacular increase in online activities like e-transactions, security and privacy issues are at the peak with respect to their significance. Large numbers of database security breaches are occurring at a very high rate on daily basis. So, there is a crucial need in the field of database forensics to make several redundant copies of sensitive data found in database server artifacts, audit logs, cache, table storage etc. for analysis purposes. Large volume of metadata is available in database infrastructure for investigation purposes but most of the effort lies in the retrieval and analysis of that information from computing systems. Thus, in this paper we mainly focus on the significance of metadata in database forensics. We proposed a system here to perform forensics analysis of database by generating its metadata file independent of the DBMS system used. We also aim to generate the digital evidence against criminals for presenting it in the court of law in the form of who, when, why, what, how and where did the fraudulent transaction occur. Thus, we are presenting a system to detect major database attacks as well as anti-forensics attacks by developing an open source database forensics tool. Eventually, we are pointing out the challenges in the field of forensics and how these challenges can be used as opportunities to stimulate the areas of database forensics.

Moukarzel, M., Eisenbarth, T., Sunar, B..  2017.  \#x03BC;Leech: A Side-Channel Evaluation Platform for IoT. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). :25–28.

We propose $μ$Leech, a new embedded trusted platform module for next generation power scavenging devices. Such power scavenging devices are already widely deployed. For instance, the Square point-of-sale reader uses the microphone/speaker interface of a smartphone for communications and as power supply. While such devices are used as trusted devices in security critical applications in the wild, they have not been properly evaluated yet. $μ$Leech can securely store keys and provide cryptographic services to any connected smart phone. Our design also facilitates physical security analysis by providing interfaces to facilitate acquisition of power traces and clock manipulation attacks. Thus $μ$Leech empowers security researchers to analyze leakage in next generation embedded and IoT devices and to evaluate countermeasures before deployment.