Showing papers in "Journal of Information Security in 2019"

An Intelligent Model for Online Recruitment Fraud Detection

Abstract: This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments. This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF). The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept. The researcher proposed the detection model to achieve the objectives of this study. For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed. A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model. Pre-processing step had been applied before the selection and classification adoptions. The results showed an obtained accuracy of 97.41%. Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.

A Secure Image Steganography Using Advanced Encryption Standard and Discrete Cosine Transform

Abstract: Cryptography and Steganography are two prominent techniques to obtain secure communication over the shared media like the Internet. Steganography is slightly ahead of cryptography because of its stealthy characteristics. In this paper, a new method has been proposed which combines cryptography and steganography to ensure even more secure communication. The Advanced Encryption Standard (AES) in spatial domain of the carrier/cover image and Least Significant Bit (LSB) replacement in the transformed domain of the same image has been used after performing a Discrete Cosine Transform (DCT) on the pixels. An additional layer of security has been introduced by applying XOR operation on the AES encrypted message with the pixel values of the carrier image. The Peak Signal to Noise Ratio (PSNR) of the proposed algorithm is better than most of the similar algorithms. With better PSNR, the proposed method depicts a three layer of security of the information and error free decryption.

Security of Password Hashing in Cloud

Abstract: Though the History of using password in computing can be traced back to as far as mid of last century little focus has been implied on how to securely store and retrieve password to authenticate and authorize services to the end users. In this paper the current security of various password hashing schemes that are in use today will be investigated through practical proof of concept-GPU based, password hash dump cracking using the power of cloud computing. We will be providing comparison on different password hashing cracking time using the cloud GPU power in AWS. The focus of this paper is to show the possible use of cloud computing in cracking hash dumps and the way to countermeasures them by using secure hashing algorithm and using complex passwords.

Design and Development of a Novel Symmetric Algorithm for Enhancing Data Security in Cloud Computing

Abstract: Cloud computing is a kind of computing that depends on shared figuring assets instead of having nearby servers or individual gadgets to deal with applications. Technology is moving to the cloud more and more. It’s not just a trend, the shift away from ancient package models to package as service has steadily gained momentum over the last ten years. Looking forward, the following decade of cloud computing guarantees significantly more approaches to work from anyplace, utilizing cell phones. Cloud computing focused on better performances, better scalability and resource consumption but it also has some security issue with the data stored in it. The proposed algorithm intents to come with some solutions that will reduce the security threats and ensure far better security to the data stored in cloud.

Android Security and Its Rooting—A Possible Improvement of Its Security Architecture

Abstract: The advent of technology brought forth a myriad of developments that have streamlined the manner through which people operate. With the growing need to be at the forefront of communication and information, people have resorted to the use of mobile phones with a great percentile preferring android oriented systems. Similarly, the systems are susceptible to the various threats posed by technology with due summations showing that security flaws and unauthorized access to sensitive data pose a huge threat to the overarching efficacy of the android systems. The research presented lays a primal focus on how users can improve intrinsic android features through the use of Google services, rooting, custom kernels and ROM techniques. The research also focused on how Android security features can be improved when using or installing applications. Results indicate that the rooting process is the most conclusive and safest scheme. Summations drawn are indicative of the fact that system security is a moot research topic that requires further research into how it can be improved.

Associated Risks in Mobile Applications Permissions

Abstract: Mobile applications affect user’s privacy based on the granted application’s permissions as attackers exploit mobile application permissions in Android and other mobile operating systems. This research divides permissions based on Google’s classification of dangerous permissions into three groups. The first group contains the permissions that can access user’s private data such as reading call log. The second group contains the permissions that can modify user’s data such as modifying the numbers in contacts. The third group contains the remaining permissions which can track the location, and use the microphone and other sensitive issues that can spy on the user. This research is supported by a study that was conducted on 100 participants in Saudi Arabia to show the level of users’ awareness of associated risks in mobile applications permissions. Associations among the collected data are also analyzed. This research fills the gap in user’s awareness by providing best practices in addition to developing a new mobile application to help users decide whether an application is safe to be installed and used or not. This application is called “Sparrow” and is available in Google Play Store.

Quantitative Evaluation of Cyber-Attacks on a Hypothetical School Computer Network

Abstract: This paper presents the attack tree modeling technique of quantifying cyber-attacks on a hypothetical school network system. Attack trees are constructed by decomposing the path in the network system where attacks are plausible. Considered for the network system are two possible network attack paths. One network path represents an attack through the Internet, and the other represents an attack through the Wireless Access Points (WAPs) in the school network. The probabilities of success of the events, that is, 1) the attack payoff, and 2) the commitment of the attacker to infiltrate the network are estimated for the leaf nodes. These are used to calculate the Returns on Attacks (ROAs) at the Root Nodes. For Phase I, the “As Is” network, the ROA values for both attack paths, are higher than 7 (8.00 and 9.35 respectively), which are high values and unacceptable operationally. In Phase II, countermeasures are implemented, and the two attack trees reevaluated. The probabilities of success of the events, the attack payoff and the commitment of the attacker are then re-estimated. Also, the Returns on Attacks (ROAs) for the Root Nodes are re-assessed after executing the countermeasures. For one attack tree, the ROA value of the Root Node was reduced to 4.83 from 8.0, while, for the other attack tree, the ROA value of the Root Node changed to 3.30 from 9.35. ROA values of 4.83 and 3.30 are acceptable as they fall within the medium value range. The efficacy of this method whereby, attack trees are deployed to mitigate computer network risks, as well as using it to assess the vulnerability of computer networks is quantitatively substantiated.

The Hazards of Misusing the Smart Contract: An AHP Approach to Its Risk

Abstract: This article explores four critical groups of systematic risk embedded in smart contract employment using the analytic hierarchy process (AHP). The four principal risk analysis groups include: 1) transparency in the light of corporate governance 2) IT security 3) contract management automation and 4) legality. The AHP assists both decision-makers and stakeholders alike in the evaluation process essential for identifying potential technological constraints posed within a permissioned blockchain environment using peer-to-peer format in the absence of digital currency. Based upon critical assessment, the AHP methodology enables pairwise comparisons among different features and consequently increases the knowledge regarding these attributes in light of the software’s risk assessment.

Improved Smartphone Application for Remote Access by Network Administrators

Abstract: This research attempts the implementation of an improved smartphone application for remote system administration. The work was motivated by the inability of network administrators to access their virtual servers from a remote location without worrying about the security implications, inaccurate and unreliable reports from a third party whenever he is out of town. The cloud server can be monitored and administered because various task such as creating users, manage users (grant access, block or delete users), restart server and shutdown server can be handled by the remote system administrator. This will involve of securing the system with a one-way hashing of encrypted password and a device ID for only whitelisted devices to be granted access. It will be observed that remote access for system administration can be implemented using a smartphone app based on a Point-to-Point Protocol and also reveal the advantages of PPP protocol, therefore making the enormous responsibilities of a remote system administrator much easier to accomplish.

Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm)

Abstract: In today’s world, computer network is evolving very rapidly. Most public or/and private companies set up their own local networks system for the purpose of promoting communication and data sharing within the companies. Unfortunately, their data and local networks system are under risks. With the advanced computer networks, the unauthorized users attempt to access their local networks system so as to compromise the integrity, confidentiality and availability of resources. Multiple methods and approaches have to be applied to protect their data and local networks system against malicious attacks. The main aim of our paper is to provide an intrusion detection system based on soft computing algorithms such as Self Organizing Feature Map Artificial Neural Network and Genetic Algorithm to network intrusion detection system. KDD Cup 99 and 1998 DARPA dataset were employed for training and testing the intrusion detection rules. However, GA’s traditional Fitness Function was improved in order to evaluate the efficiency and effectiveness of the algorithm in classifying network attacks from KDD Cup 99 and 1998 DARPA dataset. SOFM ANN and GA training parameters were discussed and implemented for performance evaluation. The experimental results demonstrated that SOFM ANN achieved better performance than GA, where in SOFM ANN high attack detection rate is 99.98%, 99.89%, 100%, 100%, 100% and low false positive rate is 0.01%, 0.1%, 0%, 0%, 0% for DoS, R2L, Probe, U2R attacks, and Normal traffic respectively.

Multi-Value Sequence Generated over Sub Extension Field and Its Properties

Abstract: Pseudo-random sequences with long period, low correlation, high linear complexity, and uniform distribution of bit patterns are widely used in the field of information security and cryptography. This paper proposes an approach for generating a pseudo-random multi-value sequence (including a binary sequence) by utilizing a primitive polynomial, trace function, and k-th power residue symbol over the sub extension field. All our previous sequences are defined over the prime field, whereas, proposed sequence in this paper is defined over the sub extension field. Thus, it’s a new and innovative perception to consider the sub extension field during the sequence generation procedure. By considering the sub extension field, two notable outcomes are: proposed sequence holds higher linear complexity and more uniform distribution of bit patterns compared to our previous work which defined over the prime field. Additionally, other important properties of the proposed multi-value sequence such as period, autocorrelation, and cross-correlation are theoretically shown along with some experimental results.

A Comparative Study and Performance Analysis of ATM Card Fraud Detection Techniques

Abstract: ATM card fraud is increasing gradually with the expansion of modern technology and global communication. In the whole world, it is resulting in the loss of billions of dollars each year. Fraud detection systems have become essential for all ATM card issuing banks to minimize their losses. The main goals are, firstly, to review alternative techniques that have been used in fraud detection and secondly compare and analyze these techniques that are already used in ATM card fraud detection. Recently different card security systems used different fraud detection techniques; these techniques are based on neural network, genetic algorithm, hidden Markov model, Bayesian network, decision tree, clustering method, support vector machine, etc. According to our survey, the most important parameters used for comparing these fraud detection systems are accuracy, speed and cost of fraud detection. This study is very useful for any ATM card provider to choose an appropriate solution for fraud detection problem and also enable us to build a hybrid approach for developing some effective algorithms which can perform properly on fraud detection mechanism.

Systematizing IT Risks

Abstract: IT risks — risks associated with the operation or use of information technology — have taken on great importance in business, and IT risk management is accordingly important in the science and practice of information management. Therefore, it is necessary to systematize IT risks in order to plan, manage and control for different risk-specific measures. In order to choose and implement suitable measures for managing IT risks, effect-based and causebased procedures are necessary. These procedures are explained in detail for IT security risks because of their special importance.

Unlink Attack Defense Method Based on New Chunk Structure

Abstract: The Unlink attack is a way of attacking the heap overflow vulnerability under the Linux platform. However, because the heap overflow data seldom directly leads to program control flow hijacking and related protection mechanism limitations, the existing detection technology is difficult to judge whether the program meets the heap overflow attack condition. There are certain inspection measures in the existing unlink mechanism, but with carefully constructing the contents of the heap, you can bypass the inspection measures. The unlink mechanism must be triggered with the free function, and this principle is similar to function-exit of stacks. The paper obtains the inspiration through the canary protection mechanism in the stack, adds it to the chunk structure, encrypts the canary value, and defends the unlink attack from the fundamental structure. The experimental results show that this method can effectively prevent the occurrence of unlink attacks and has the ability to detect common heap overflows.