Design and Deployment of Network Testbed for Web Data Security

In recent years, the cyber security scenario has transformed predominantly from conventional response-based security mechanisms to proactive security strategies. And this transformation is still continuing which is shifting it from proactive security strategies to cyber immunity which eliminates the cyber threats by introducing stringent and adaptive security measures. In the process of developing new security algorithms/procedures, accurate modelling and effective simulation play a vital role for the robustness and effectiveness of proposed system. It is also necessary to analyze the behaviour of proposed system against multiple types of known cyber attacks. This paper focuses on the existing network testbeds for an effective analysis and monitoring while proposing a new network testbed for examining new security concepts like cyber immunity. The proposed network testbed is designed to incorporate the methods and procedures of Nature-inspired Cyber Security to accommodate the adaptive responses against the sophisticated and ever-advancing cyber attacks. The proposed testbed provides customizable analytical tool to design, test and examine the new security algorithms through a rich set of attack scenarios. It also allows developers to design, implement, and evaluate their defensive techniques with library support.

Effects of ‘Digital’ Country’s Information Security on Political Stability

In this day and age, information security is becoming a priority not only in the system of international economic relations but also at the state level. This study aims to study the effect of a ‘digital’ country’s information security on its political stability through quantitative analysis. The study is a mixed research design with a focus on the Russian Federation and the Republic of Kazakhstan. Its methodological basis is represented by the collection and analysis of data on the level and nature of cybersecurity threats (Global Cybersecurity Index, the number of cyber incidents) and on the level of political stability (Political Stability and Absence of Violence/Terrorism indicator of the Worldwide Governance Index). The results of the study show that Russia with a GCI 2020 score of 98.06 and Kazakhstan with a GCI score of 93.15 have relatively low levels of political stability. This is evidenced by their 45.7 and 25.7 percentile ranks on Political Stability and Absence of Violence/Terrorism and a high frequency of offenses using information and communication technologies. Findings suggest that with a high level of commitment to information security, the growth in cyber incidents will not necessarily affect political stability. The obtained findings provide countries an insight into cybersecurity within the national system as well as present a great deal of data on best practices to work through gaps in the national culture of cybersecurity at the state level. The results and methodology of this study can be used by officials to develop information security strategies and tactics, as well as by other researchers for quantitative analysis of the relationship between information security and political stability of different countries and regions.

An Enhanced Sybil Guard to Detect Bots in Online Social Networks

Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.

Phisher Fighter: Website Phishing Detection System Based on URL and Term Frequency-Inverse Document Frequency Values

Fundamentally, phishing is a common cybercrime that is indulged by the intruders or hackers on naive and credible individuals and make them to reveal their unique and sensitive information through fictitious websites. The primary intension of this kind of cybercrime is to gain access to the ad hominem or classified information from the recipients. The obtained data comprises of information that can very well utilized to recognize an individual. The purloined personal or sensitive information is commonly marketed in the online dark market and subsequently these information will be bought by the personal identity brigands. Depending upon the sensitivity and the importance of the stolen information, the price of a single piece of purloined information would vary from few dollars to thousands of dollars. Machine learning (ML) as well as Deep Learning (DL) are powerful methods to analyse and endeavour against these phishing attacks. A machine learning based phishing detection system is proposed to protect the website and users from such attacks. In order to optimize the results in a better way, the TF-IDF (Term Frequency-Inverse Document Frequency) value of webpages is employed within the system. ML methods such as LR (Logistic Regression), RF (Random Forest), SVM (Support Vector Machine), NB (Naive Bayes) and SGD (Stochastic Gradient Descent) are applied for training and testing the obtained dataset. Henceforth, a robust phishing website detection system is developed with 90.68% accuracy.

A Review on Audio Encryption Algorithms Using Chaos Maps-Based Techniques

Due to the quick improvement in digital communications and multimedia applications during recent periods up to the current time, data protection of digital data such as image, audio and video becomes a significant challenge. The security of audio data that transfer through different networks was rated as a preferred research field in the preceding years. This review covers the recent contribution for audio encryption and gives the most evaluations for audio encryption algorithm involving security analysis, computational complexity and quality analysis and their requirements. This paper fundamentally concentrates on displaying the different types of audio encryption and decryption techniques based on chaotic maps. Digital and analog audio algorithms were displayed, discussed and compared with the illustration of the important features and drawbacks. Various digital and audio proposed projects for audio encryption using chaotic maps have been covered, which they showed extreme sensitivity to initial conditions, unpredictability and conducting in a quasi-random manner. A comparison among the proposed algorithms in the key space, chaotic maps sensitivity and statistical analysis were provided.

Adaptive Matrix Pattern Steganography on RGB Images

Almost all spatial domain image steganography methods rely on modifying the Least Significant Bits (LSB) of each pixel to minimize the visual distortions. However, these methods are susceptible to LSB blind attacks and quantitative steganalyses. This paper presents an adaptive spatial domain image steganography algorithm for hiding digital media based on matrix patterns, named “Adaptive Matrix Pattern” (AMP). The AMP method increases the security of the steganography scheme of largely hidden messages since it adaptively generates a unique codebook matrix pattern for each ASCII character in each image block. Therefore, each ASCII character gets a different codebook matrix pattern even in different regions of the same image. Moreover, it uses a preprocessing algorithm to identify the most suitable image blocks for hiding purposes. The resulting stego-images are robust against LSB blind attacks since the middle bits of green and blue channels generate matrix patterns and hiding secrets, respectively. Experimental results show that AMP is robust against quantitative steganalyses. Additionally, the quality of stego-images, based on the peak signal-to-noise ratio metric, remains high in both stego-RGB-image and in the stego-blue-channel. Finally, the AMP method provides a high hiding capacity, up to 1.33 bits per pixel.

Boosted Performances of NTRUencrypt Post-Quantum Cryptosystem

The bottleneck of all cryptosystems is the difficulty of the computational complexity of the polynomials multiplication, vectors multiplication, etc. Thus most of them use some algorithms to reduce the complexity of the multiplication like NTT, Montgomery, CRT, and Karatsuba algorithms, etc. We contribute by creating a new release of NTRUencrypt1024 with great improvement, by using our own polynomials multiplication algorithm operate in the ring of the form Rq=Zq[X]/(XN+1), combined to Montgomery algorithm rather than using the NTT algorithm as used by the original version. We obtained a good result, our implementation outperforms the original one by speed-up of a factor up to (X10) for encryption and a factor up to (X11) for decryption functions. We note that our improved implementation used the latest hash function standard SHA-3, and reduce the size of the public key, private key, and cipher-text from 4097 bytes to 2049 bytes with the same security level.

Cyberbullying Detection in Social Networks: Artificial Intelligence Approach

Over the past decade, digital communication has reached a massive scale globally. Unfortunately, cyberbullying has become prevalent, with perpetrators hiding behind the mask of relative internet anonymity. In this work, efforts were made to review prominent classification algorithms and also to propose an ensemble model for identifying cases of cyberbullying, using Twitter datasets. The algorithms used for evaluation are Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Linear Support Vector Classifier, Adaptive Boosting, Stochastic Gradient Descent and Bagging classifiers. Through experimentations, comparisons were made with the classifiers against four metrics: accuracy, precision, recall and F1 score. The results reveal the performances of all the algorithms used with their corresponding metrics. The ensemble model generated better results while Linear Support Vector Classifier (SVC) was the least effective of all. Random Forest classifier has shown to be the best performing classifier with medians of 0.77, 0.73 and 0.94 across the datasets. The ensemble model has shown to improve the results of its constituent classifiers with medians of 0.77, 0.66 and 0.94, as against the 0.59, 0.42 and 0.86 of Linear Support Vector Classifier.

Evaluating and Improving a Content Delivery Network (CDN) Workflow using Stochastic Modelling

Content Delivery Networks (CDN) are the backbone of Internet. A lot of research has been done to make CDNs more reliable. Despite that, the world has suffered from CDN inefficiencies quite a few times, not just due to external hacking attempts but due to internal failures as well. In this research work the authors have analyzed the performance of a content delivery network through various reliability measures. Considering a basic CDN workflow they have calculated the reliability and availability of the proposed multi-state system using Markov process and Laplace transformation. Software/Hardware failures in any network component can affect the reliability of the whole system. Therefore, the authors have analyzed the obtained results to find major causes of failures in the system, which when avoided, can lead to a faster and more efficient distribution network.

Exploring The Correlation between Cyber Security Awareness, Protection Measures and the State of Victimhood: The Case Study of Ambo University’s Academic Staffs

The advancement of information communication technology has triggered a revolution in using the Internet for legitimate educational purposes on university campuses. Therefore, the Internet has changed the way of human communication and contributed to the development of mankind. On the other hand it is regrettable that its revolution has helped malicious users to exploit it for the malign purpose to commit a cyberspace crime that has in turn negatively affected fellow users who were preyed on by cyber predators. This work aimed to examine the awareness of cybersecurity, the measures taken to protect against cyberattacks and the state of victimization among professors at Ambo University. Thus, the present study comes up with the following findings. First, the result shows that the respondents’ cybersecurity awareness was significantly influenced by cyber-crime victimization, fields of study, and protection measures. Second, the current study also depicts that the respondents’ protection measures were connected to and influenced by cyber-crime victimization, education level, and cyber-security awareness. Finally, the study’s findings show that being a cyber-crime victim has been linked to predictors’ variables: protection measures and the level of cybersecurity awareness.