Biblio

Information-centric networking (ICN) has played an increasingly important role in the next generation network design. However, to make better use of request-response communication mode in the ICN network, revoke user privileges more efficiently and protect user privacy more safely, an effective access control mechanism is needed. In this paper, we propose IBSS (identity-based secret sharing), which achieves efficient content distribution by using improved Shamir's secret sharing method. At the same time, collusion attacks are avoided by associating polynomials' degree with the number of users. When authenticating user identity and transmitting content, IBE and IBS are introduced to achieve more efficient and secure identity encryption. From the experimental results, the scheme only introduces an acceptable delay in file retrieval, and it can request follow-up content very efficiently.

When disasters occur, they not only affect the human life. Therefore, communication in disaster management is very important. During the disaster recovery phase, the network infrastructure may be partially fragmented and mobile rescue operations may involve many teams with different roles which can dynamically change. Therefore, disaster management services require high flexibility both in terms of network infrastructure management and rescue group communication. Existing studies have shown that IP-based or traditional telephony solutions are not well-suited to deal with such flexible group communication and network management due to their connection-oriented communication, no built-in support for mobile devices, and no mechanism for network fragmentation. Recent studies show that information-centric networking offers scalable and flexible communication based on its name-based interest-oriented communication approach. However, considering the difficulty of deploying a new service on the existing network, the programmability and virtualization of the network are required. This paper presents our implementation of an information-centric disaster management system based on network function virtualization (vICSNF). We show a proof-of-concept system with a case study for Seoul disaster management services. The system achieves flexibility both in terms of network infrastructure management and rescue group communication. Obtained testbed results show that vICSNF achieves a low communication overhead compared to the IP-based approach and the auto-configuration of vICSNFs enables the quick deployment for disaster management services in disaster scenarios.

A Cyber Physical System (CPS) is a smart network system with actuators, embedded sensors, and processors to interact with the physical world by guaranteeing the performance and supporting real-time operations of safety critical applications. These systems drive innovation and are a source of competitive advantage in today’s challenging world. By observing the behavior of physical processes and activating actions, CPS can alter its behavior to make the physical environment perform better and more accurately. By definition, CPS basically has two major components including cyber systems and physical processes. Examples of CPS include autonomous transportation systems, robotics systems, medical monitoring, automatic pilot avionics, and smart grids. Advances in CPS will empower scalability, capability, usability, and adaptability, which will go beyond the simple systems of today. At the same time, CPS has also increased cybersecurity risks and attack surfaces. Cyber attackers can harm such systems from multiple sources while hiding their identities. As a result of sophisticated threat matrices, insufficient knowledge about threat patterns, and industrial network automation, CPS has become extremely insecure. Since such infrastructure is networked, attacks can be prompted easily without much human participation from remote locations, thereby making CPS more vulnerable to sophisticated cyber-attacks. In turn, large-scale data centers managing a huge volume of CPS data become vulnerable to cyber-attacks. To secure CPS, the role of security analytics and intelligence is significant. It brings together huge amounts of data to create threat patterns, which can be used to prevent cyber-attacks in a timely fashion. The primary objective of this Special Section in IEEE A CCESS is to collect a complementary and diverse set of articles, which demonstrate up-to-date information and innovative developments in the domain of security analytics and intelligence for CPS.

The growing prevalence of Internet-of-Things (IoT) technology has led to an increase in the development of heterogeneous smart applications. Smart applications may involve a collaborative participation between IoT devices. Participation of IoT devices for specific application requires a tamper-proof identity to be generated and stored, in order to completely represent the device, as well as to eliminate the possibility of identity spoofing and presence of rogue devices in a network. In this paper, we present a composite Identity-of-Things (IDoT) approach on IoT devices with permissioned blockchain implementation for distributed identity management model. Our proposed approach considers both application and device domains in generating the composite identity. In addition, the use of permissioned blockchain for identity storage and verification allows the identity to be immutable. A simulation has been carried out to demonstrate the application of the proposed identity management model.

Authenticating a person's identity has always been a challenge. While attempts are being made by government agencies to address this challenge, the citizens are being exposed to a new age problem of Identity management. The sharing of photocopies of identity cards in order to prove our identity is a common sight. From score-card to Aadhar-card, the details of our identity has reached many unauthorized hands during the years. In India the identity thefts accounts for 77% [1] of the fraud cases, and the threats are trending. Programs like e-Residency by Estonia[2], Bitnation using Ethereum[3] are being devised for an efficient Identity Management. Even the US Home Land Security is funding a research with an objective of “Design information security and privacy concepts on the Blockchain to support identity management capabilities that increase security and productivity while decreasing costs and security risks for the Homeland Security Enterprise (HSE).” [4] This paper will discuss the challenges specific to India around Identity Management, and the possible solution that the Distributed ledger, hashing algorithms and smart contracts can offer. The logic of hashing the personal data, and controlling the distribution of identity using public-private keys with Blockchain technology will be discussed in this paper.

In this article, the writers suggested a scheme for analyzing the optimum crop cultivation based on Fuzzy Logic Network (Implementation of Fuzzy Logic Control in Predictive Analysis and Real Time Monitoring of Optimum Crop Cultivation) knowledge. The Fuzzy system is Fuzzy Logic's set. By using the soil, temperature, sunshine, precipitation and altitude value, the scheme can calculate the output of a certain crop. By using this scheme, the writers hope farmers can boost f arm output. This, thus will have an enormous effect on alleviating economical deficiency, strengthening rate of employment, the improvement of human resources and food security.

Internet of Things (IoT) systems are becoming widely used, which makes them to be a high-value target for both hackers and crackers. From gaining access to sensitive information to using them as bots for complex attacks, the variety of advantages after exploiting different security vulnerabilities makes the security of IoT devices to be one of the most challenging desideratum for cyber security experts. In this paper, we will propose a new IoT system, designed to ensure five data principles: confidentiality, integrity, availability, authentication and authorization. The innovative aspects are both the usage of a web-based communication and a custom dynamic data request structure.

Image Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.

Today, the huge amount of data was using in organizations around the world. This huge amount of data needs to process so that we can acquire useful information. Consequently, a number of industry enterprises discovered great information from shopper purchases found in any respect times. In data mining, the most important algorithms for find frequent item sets from large database is Apriori algorithm and discover the knowledge using the association rule. Apriori algorithm was wasted times for scanning the whole database and searching the frequent item sets and inefficient of memory requirement when large numbers of transactions are in consideration. The improved Apriori algorithm is adding and calculating third threshold may increase the overhead. So, in the aims of proposed research, Improved Apriori algorithm with LinkedList and hash tabled is used to mine frequent item sets from the transaction large amount of database. This method includes database is scanning with Improved Apriori algorithm and frequent 1-item sets counts with using the hash table. Then, in the linked list saved the next frequent item sets and scanning the database. The hash table used to produce the frequent 2-item sets Therefore, the database scans the only two times and necessary less processing time and memory space.

Generative Adversarial Network (GAN) and its variants have shown promising results in generating synthetic data. However, the issues with GANs are: (i) the learning happens around the training samples and the model often ends up remembering them, consequently, compromising the privacy of individual samples - this becomes a major concern when GANs are applied to training data including personally identifiable information, (ii) the randomness in generated data - there is no control over the specificity of generated samples. To address these issues, we propose imdpGAN-an information maximizing differentially private Generative Adversarial Network. It is an end-to-end framework that simultaneously achieves privacy protection and learns latent representations. With experiments on MNIST dataset, we show that imdpGAN preserves the privacy of the individual data point, and learns latent codes to control the specificity of the generated samples. We perform binary classification on digit pairs to show the utility versus privacy trade-off. The classification accuracy decreases as we increase privacy levels in the framework. We also experimentally show that the training process of imdpGAN is stable but experience a 10-fold time increase as compared with other GAN frameworks. Finally, we extend imdpGAN framework to CelebA dataset to show how the privacy and learned representations can be used to control the specificity of the output.

As the computer architecture technology evolves, communication protocols have been demanded not only having reliable security but also flexible functionality. Advanced cryptography has been expected as a new generation cryptography which suffices such the requirements. A pairing is one of the key technologies of the cryptography and the pairing has been known as having a substantial amount of construction parameters. Recently, the elliptic curve with embedding degree 14 is evaluated as one of the efficient curves for pairing. In the paper, we implement an optimal ate pairing on the elliptic curve by applying several variants of multiplication algorithms of extension field of degree 7 on multiple devices. The best multiplication algorithm among the candidates is derived. Besides, for efficient calculations, we propose a pseudo 7-sparse algorithm and a fast calculation method of final exponentiation. As a result, we discover the proper multiplication algorithm bases on the rate of addition and multiplications on several different computer platforms. Our proposed pseudo 7-sparse algorithm is approximately 1.54% faster than a regular algorithm on almost all tested platforms. Eventually, for the total execution time of pairing we record 9.33ms on Corei5-9500.

Hyperbox classifier is an efficient tool for modern pattern classification problems due to its transparency and rigorous use of Euclidian geometry. Fuzzy min-max (FMM) network efficiently implements the hyperbox classifier, and has been modified several times to yield better classification accuracy. However, the obtained accuracy is not up to the mark. Therefore, in this paper, a new improved FMM (IFMM) network is proposed to increase the accuracy rate. In the proposed IFMM network, a modified constraint is employed to check the expandability of a hyperbox. It also uses semiperimeter of the hyperbox along with k-nearest mechanism to select the expandable hyperbox. In the proposed IFMM, the contraction rules of conventional FMM and enhanced FMM (EFMM) are also modified using semiperimeter of a hyperbox in order to balance the size of both overlapped hyperboxes. Experimental results show that the proposed IFMM network outperforms the FMM, k-nearest FMM, and EFMM by yielding more accuracy rate with less number of hyperboxes. The proposed methods are also applied to histopathological images to know the best magnification factor for classification.

Cloud computing is a vast area within which large amounts of data are exchanged through cloud services and has fully grown with its on-demand technology. Due to these versatile cloud services, sensitive data will be stored on cloud storage servers and it is also used to dynamically control a number of problems: security, privacy, data privacy, data sharing, and integrity across cloud servers. Moreover, the legitimacy and control of data access should be maintained in this extended environment. So, one of the most important concepts of cryptographic techniques in cloud computing environment is Attribute Based Encryption (ABE). In this research work, data auditing or integrity checking is considered as an area of concern for securing th cloud storage. In data auditing approach, an auditor inspects and verifies the data file integrity without having any knowledge about the content of file and sends the verification report to the data owner. In this research, Elliptical Curve Modified RSA (ECMRSA) is proposed along with Modified MD5 algorithm which is used for attribute-based cloud data integrity verification, in which data user or owner uploads their encrypted data files at cloud data server and send the auditing request to the Third-Party Auditor (TPA) for verification of their data files. The Third-Party Auditor (TPA) challenges the data server for ensuring the integrity of data files on behalf of the data owners. After verification of integrity of data file auditor sends the audit report to the owner. The proposed algorithm integrates the auditing scheme with public key encryption with homomorphic algorithm which generates digital signature or hash values of data files on encrypted files. The result analysis is performed on time complexity by evaluating encryption time, GenProof time and VerifyProof Time and achieved improvement in resolving time complexity as compared to existing techiques.

Steganography is a data hiding technique, which is generally used to hide the data within a file to avoid detection. It is used in the police department, detective investigation, and medical fields as well as in many more fields. Various techniques have been proposed over the years for Image Steganography and also attackers or hackers have developed many decoding tools to break these techniques to retrieve data. In this paper, CAPTCHA codes are used to ensure that the receiver is the intended receiver and not any machine. Here a randomized CAPTCHA code is created to provide additional security to communicate with the authenticated user and used Image Steganography to achieve confidentiality. For achieving secret and reliable communication, encryption and decryption mechanism is performed; hence a machine cannot decode it using any predefined algorithm. Once a secure connection has been established with the intended receiver, the original message is transmitted using the LSB algorithm, which uses the RGB color spectrum to hide the image data ensuring additional encryption.

The resistance to attacks aimed to break CAPTCHA challenges and the effectiveness, efficiency and satisfaction of human users in solving them called usability are the two major concerns while designing CAPTCHA schemes. User-friendliness, universality, and accessibility are related dimensions of usability, which must also be addressed adequately. With recent advances in segmentation and optical character recognition techniques, complex distortions, degradations and transformations are added to text-based CAPTCHA challenges resulting in their reduced usability. The extent of these deformations can be decreased if some additional security mechanism is incorporated in such challenges. This paper proposes an additional security mechanism that can add an extra layer of protection to any text-based CAPTCHA challenge, making it more challenging for bots and scripts that might be used to attack websites and web applications. It proposes the use of hidden text-boxes for user entry of CAPTCHA string which serves as honeypots for bots and automated scripts. The honeypot technique is used to trick bots and automated scripts into filling up input fields which legitimate human users cannot fill in. The paper reports implementation of honeypot technique and results of tests carried out over three months during which form submissions were logged for analysis. The results demonstrated great effectiveness of honeypots technique to improve security control and usability of text-based CAPTCHA challenges.

Nowadays, the emerging Internet-of-Things (IoT) emphasize the need for the security of network-connected devices. Additionally, there are two types of services in IoT devices that are easily exploited by attackers, weak authentication services (e.g., SSH/Telnet) and exploited services using command injection. Based on this observation, we propose IoTCMal, a hybrid IoT honeypot framework for capturing more comprehensive malicious samples aiming at IoT devices. The key novelty of IoTC-MAL is three-fold: (i) it provides a high-interactive component with common vulnerable service in real IoT device by utilizing traffic forwarding technique; (ii) it also contains a low-interactive component with Telnet/SSH service by running in virtual environment. (iii) Distinct from traditional low-interactive IoT honeypots[1], which only analyze family categories of malicious samples, IoTCMal primarily focuses on homology analysis of malicious samples. We deployed IoTCMal on 36 VPS1 instances distributed in 13 cities of 6 countries. By analyzing the malware binaries captured from IoTCMal, we discover 8 malware families controlled by at least 11 groups of attackers, which mainly launched DDoS attacks and digital currency mining. Among them, about 60% of the captured malicious samples ran in ARM or MIPs architectures, which are widely used in IoT devices.

We observe that operations performed by standard compilers harm fuzzing because the optimizations and the Intermediate Representation (IR) lead to transformations that improve execution speed at the expense of fuzzing. To remedy this problem, we propose `controlled compilation', a set of techniques to automatically re-factor a program's source code and cherry pick beneficial compiler optimizations to improve fuzzing. We design, implement and evaluate controlled compilation by building a new toolchain with Clang/LLVM. We perform an evaluation on 10 open source projects and compare the results of AFL to state-of-the-art grey-box fuzzers and concolic fuzzers. We show that when programs are compiled with this new toolchain, AFL covers 30 % new code on average and finds 21 additional bugs in real world programs. Our study reveals that controlled compilation often covers more code and finds more bugs than state-of-the-art fuzzing techniques, without the need to write a fuzzer from scratch or resort to advanced techniques. We identify two main reasons to explain why. First, it has proven difficult for researchers to appropriately configure existing fuzzers such as AFL. To address this problem, we provide guidelines and new LLVM passes to help automate AFL's configuration. This will enable researchers to perform a fairer comparison with AFL. Second, we find that current coverage-based evaluation measures (e.g. the total number of visited lines, edges or BBs) are inadequate because they lose valuable information such as which parts of a program a fuzzer actually visits and how consistently it does so. Coverage is considered a useful metric to evaluate a fuzzer's performance and devise a fuzzing strategy. However, the lack of a standard methodology for evaluating coverage remains a problem. To address this, we propose a rigorous evaluation methodology based on `qualitative coverage'. Qualitative coverage uniquely identifies each program line to help understand which lines are commonly visited by different fuzzers vs. which lines are visited only by a particular fuzzer. Throughout our study, we show the benefits of this new evaluation methodology. For example we provide valuable insights into the consistency of fuzzers, i.e. their ability to cover the same code or find the same bug across multiple independent runs. Overall, our evaluation methodology based on qualitative coverage helps to understand if a fuzzer performs better, worse, or is complementary to another fuzzer. This helps security practitioners adjust their fuzzing strategies.

One of the critical threats menacing the Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) is the Spectrum Sensing Data Falsification (SSDF) reports, which can deceive the decision of Fusion Center (FC) about the Primary User (PU) spectrum accessibility. In CSS, each CR user performs Energy Detection (ED) technique to detect the status of licensed frequency bands of the PU. This paper investigates the performance of different hard-decision fusion schemes (OR-rule, AND-rule, and MAJORITY-rule) in the presence of Always Yes and Always No Malicious User (AYMU and ANMU) over Rayleigh and Gaussian channels. More precisely, comparative study is conducted to evaluate the impact of such malicious users in CSS on the performance of various hard data combining rules in terms of miss detection and false alarm probabilities. Furthermore, computer simulations are carried out to show that the hard-decision fusion scheme with MAJORITY-rule is the best among hard-decision combination under AYMU attacks, OR-rule has the best detection performance under ANMU.

Electronic voting systems have enhanced efficiency in student elections management in universities, supporting such elections to become less expensive, logistically simple, with higher accuracy levels as compared to manually conducted elections. However, e-voting systems that are confined to campus hall voting inhibits access to eligible voters who are away from campus. This study examined the challenges of lack of wide access and impersonation of voter in the student elections of 2018 in Kabarak University. The main objective of this study was therefore to upgrade the offline electronic voting system through developing a secure online voting system and deploying the system for use in the 2019 student elections at Kabarak University. The resultant system and development process employed demonstrate the applicability of a secure online voting not only in the higher education context, but also in other democracies where infusion of online access and authentication in the voting processes is a requisite.

Along with the development of the Windows operating system, browser applications to surf the internet are also growing rapidly. The most widely used browsers today are Google Chrome and Mozilla Firefox. Both browsers have a username and password management feature that makes users login to a website easily, but saving usernames and passwords in the browser is quite dangerous because the stored data can be hacked using brute force attacks or read through a program. One way to get a username and password in the browser is to use a program that can read Google Chrome and Mozilla Firefox login data from the computer's internal storage and then show those data. In this study, an attack will be carried out by implementing Rubber Ducky using BadUSB to run the ChromePass and PasswordFox program and the PowerShell script using the Arduino Pro Micro Leonardo device as a USB Password Stealer. The results obtained from this study are the username and password on Google Chrome and Mozilla Firefox successfully obtained when the USB is connected to the target device, the average time of the attack is 14 seconds then sending it to the author's email.

Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naïve Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.

Today our world benefits from Internet of Things (IoT) technology; however, new security problems arise when these IoT devices are introduced into our homes. Because many of these IoT devices have access to the Internet and they have little to no security, they make our smart homes highly vulnerable to compromise. Some of the threats include IoT botnets and generic confidentiality, integrity, and availability (CIA) attacks. Our research explores botnet detection by experimenting with supervised machine learning and deep-learning classifiers. Further, our approach assesses classifier performance on unbalanced datasets that contain benign data, mixed in with small amounts of malicious data. We demonstrate that the classifiers can separate malicious activity from benign activity within a small IoT network dataset. The classifiers can also separate malicious activity from benign activity in increasingly larger datasets. Our experiments have demonstrated incremental improvement in results for (1) accuracy, (2) probability of detection, and (3) probability of false alarm. The best performance results include 99.9% accuracy, 99.8% probability of detection, and 0% probability of false alarm. This paper also demonstrates how the performance of these classifiers increases, as IoT training datasets become larger and larger.

Data driven classification models are useful to assess quality of manufactured electronics. Because decisions are taken based on the models, their veracity is relevant, covering aspects such as accuracy, transparency and clarity. The proposed BB-Stepwise algorithm aims to improve the classification model transparency and accuracy of black box models. K-Nearest Neighbours (KNN) is a black box model which is easy to implement and has achieved good classification performance in different applications. In this paper KNN-Stepwise is illustrated for fault detection of electronics devices. The results achieved shows that the proposed algorithm was able to improve the accuracy, veracity and transparency of KNN models and achieve higher transparency and clarity, and at least similar accuracy than when using Decision Tree models.

Individualization of anonymous identities using artificial intelligence - enables innovative human-computer interaction through the personalization of communication which is, at the same time, individual and anonymous. This paper presents possible approach for individualization of anonymous identities in real time. It uses computer vision and artificial intelligence to automatically detect and recognize person's age group, gender, human body measures, proportions and other specific personal characteristics. Collected data constitutes the so-called person's biometric footprint and are linked to a unique (but still anonymous) identity that is recorded in the computer system, along with other information that make up the profile of the person. Identity anonymization can be achieved by appropriate asymmetric encryption of the biometric footprint (with no additional personal information being stored) and integrity can be ensured using blockchain technology. Data collected in this manner is GDPR compliant.

Windows is one of the popular operating systems in use today, while Universal Serial Bus (USB) is one of the mechanisms used by many people with practical plug and play functions. USB has long been used as a vector of attacks on computers. One method of attack is Keylogger. The Keylogger can take advantage of existing vulnerabilities in the Windows 10 operating system attacks carried out in the form of recording computer keystroke activity without the victim knowing. In this research, an attack will be carried out by running a Powershell Script using BadUSB to be able to activate the Keylogger program. The script is embedded in the Arduino Pro Micro device. The results obtained in the Keyboard Injection Attack research using Arduino Pro Micro were successfully carried out with an average time needed to run the keylogger is 7.474 seconds with a computer connected to the internet. The results of the keylogger will be sent to the attacker via email.