All Your IP Are Belong to Us: An Analysis of Intellectual Property Rights as Applied to Malware

The cybersecurity and cybercrime industries are tied together in an arms race where both seek out new security vulnerabilities to exploit on offense or to remediate on defense. Malware (malicious software) offers one of the primary weapons pioneering new computer technologies on both sides. However, the average Internet user sees malware at best as an annoyance that is merely the price of surfing the web. It is clear that cybersecurity is a business and a successful one. The cybersecurity industry maintains copyrights and patents on our cyber defense technologies— antivirus software, firewalls, intrusion prevention systems, and more. There are no federal copyrights and patents on malware, even regarding the cybersecurity industry’s creations. From an intellectual property perspective, there is no difference between ordinary software and malicious software. Malware, as offensive software, can and should be protected, just as we protect our defensive software

Information Security and Cryptography-Encryption in Journalism

The purpose of this review paper is to garner knowledge about the information security and cryptography encryption practices implementation for journalistic work and its effectiveness in thwarting software security breaches in the wake of ‘Journalism After Snowden’. Systematic literature review for the ‘information security and cryptography encryption in journalism’ employed with an eye to synthesize existing practices in this field. For this, at first the existing approachable research article databases and search engines employed to download or get the abstract of relevant scientific articles which are then used for citation and summarization works in a systematic rigorous anatomization. Contingent upon them their analysis and synthesis employed to arrive at the findings. Research papers collated for the purpose of writing this review paper lighted up the vital issues related to investigative journalists’ safety practices promulgation inadequacies even after the UNESCO 2017 and 2022 guidelines for urgent instrumentalization needs of journalists on the part of its’ member States.Lattice Science Publication (LSP) © Copyright: All rights reserved

A Comparison of Clustering Techniques for Malware Analysis

In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering

USB Flashdrives Virus Detector Implemented in RaspberryPi

Práce je zaměřena na analýzu tématu okolo bezpečnosti na internetu a vytvoření detektoru virů na USB klíčenkách. Postupně jsou rozebírány a lehce nastíněny principy virů a antivirů. Ke zrealizování práce je využita platforma Raspberry Pi, jazyk Python a dostupné antivirové programy. Cílem je vytvoření automatického detektoru, který nepotřebuje interakci s uživatelem ke svému chodu.This thesis is focused on the analysis of internet security and the implementation of USB flashdrives virus detector. We will firstly analyze the basics of viruses and antiviruses and from gained knowledge we are going to create an automatic virus detector which doesn't need an user intervention. For impelementation will be used a platform Raspberry Pi and programming language Python.

Screen real estate ownership based mechanism for negotiating advertisement display

As popularity of online video grows, a number of models of advertising are emerging. It is typically the brokers – usually the operators of websites – who maintain the balance between content and advertising. Existing approaches focus primarily on personalizing advertisements for viewer segments, with minimal decision-making capacity for individual viewers. We take a resource ownership view on this problem. We view consumers’ attention space, which can be abstracted as a display screen for an engaged viewer, as precious resource owned by the viewer. Viewers pay for the content they wish to view in dollars, as well as in terms of their attention. Specifically, advertisers may make partial payment for a viewer’s content, in return for receiving the viewer’s attention to their advertising. Our approach, named “FlexAdSense”, is based on CyberOrgs model, which encapsulates distributed owned resources for multi-agent computations. We build a market of viewers’ attention space in which advertisers can trade, just as viewers can trade in a market of content. We have developed key mechanisms to give viewers flexible control over the display of advertisements in real time. Specific policies needed for automated negotiations can be plugged-in. This approach relaxes the exclusivity of the relationship between advertisers and brokers, and empowers viewers, enhancing their viewing experience. This thesis presents the rationale, design, implementation, and evaluation of FlexAdSense. Feature comparison with existing advertising mechanisms shows how FlexAdSense enables viewers to control with fine-grained flexibility. Experimental results demonstrate the scalability of the approach, as the number of viewers increases. A preliminary analysis of user overhead illustrates minimal attention overhead for viewers as they customize their policies

Cybersecurity Challenges Facing Sub Saharan Africa: Botswana Context

The Global Cybersecurity Index (GCI) of Botswana dropped from position 23 in 2014 to position 69 in 2017 with GCI scores of .176 and .430 respectively.  The mediocre GCI performance of Botswana resulted in modest GCI scores across all GCI competitive measures namely: legal, technical, and organizational structure, capacity building, and international cooperation. Generally, cybercrime exploits critical infrastructure systems, thereby placing the nation’s security, economy, public safety and health at risk.  The absence of a national cybersecurity policy framework that describes the current security posture, identifies and prioritizes opportunities for improvement, and communicates to stakeholders about cybersecurity risk, may exacerbate the delay in the execution of Botswana National Cybersecurity Strategy, which has been under development for more than 3 years.  The purpose of this qualitative multiple case study was to explore policy frameworks developing countries use to guide the development of cybersecurity policy and strategies organizations use to safeguard and combat cybercrime.  Fifteen senior managers from the University of Botswana, Ministry of Transport and Communication, Botswana Police Service, Attorney General’s Chambers, and representatives from the private sector participated in a focus group interview during the 3rd International Conference on Internet, Cybercrime, and Information Systems hosted by University of Botswana on 1st to 2nd November 2018.  Themes that emerged included awareness and training, fast tracking the approval of the National Cybersecurity policy, protecting government ICT infrastructure from incidents of cybercrime, building national computer emergency response teams and national security operations centers with appropriate governance structure, and the development of National Cybersecurity Policy to improve Botswana’s security posture and GCI performance

An approach to preventing spam using Access Codes with a combination of anti-spam mechanisms

Spam is becoming a more and more severe problem for individuals, networks, organisations and businesses. The losses caused by spam are billions of dollars every year. Research shows that spam contributes more than 80% of e-mails with an increased in its growth rate every year. Spam is not limited to emails; it has started affecting other technologies like VoIP, cellular and traditional telephony, and instant messaging services. None of the approaches (including legislative, collaborative, social awareness and technological) separately or in combination with other approaches, can prevent sufficient of the spam to be deemed a solution to the spam problem. The severity of the spam problem and the limitations of the state-of-the-Art solutions create a strong need for an efficient anti-spam mechanism that can prevent significant volumes of spam without showing any false positives. This can be achieved by an efficient anti-spam mechanism such as the proposed anti-spam mechanism known as "Spam Prevention using Access Codes", SPAC. SPAC targets spam from two angles i.e. to prevent/block spam and to discourage spammers by making the infrastructure environment very unpleasant for them. In addition to the idea of Access Codes, SPAC combines the ideas behind some of the key current technological anti-spam measures to increase effectiveness. The difference in this work is that SPAC uses those ideas effectively and combines them in a unique way which enables SPAC to acquire the good features of a number of technological anti-spam approaches without showing any of the drawbacks of these approaches. Sybil attacks, Dictionary attacks and address spoofing have no impact on the performance of SPAC. In fact SPAC functions in a similar way (i.e. as for unknown persons) for these sorts of attacks. An application known as the "SPAC application" has been developed to test the performance of the SPAC mechanism. The results obtained from various tests on the SPAC application show that SPAC has a clear edge over the existing anti-spam technological approaches

A Heuristic Featured Based Quantification Framework for Efficient Malware Detection. Measuring the Malicious intent of a file using anomaly probabilistic scoring and evidence combinational theory with fuzzy hashing for malware detection in Portable Executable files

Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection

An enhanced performance model for metamorphic computer virus classification and detectioN

Metamorphic computer virus employs various code mutation techniques to change its code to become new generations. These generations have similar behavior and functionality and yet, they could not be detected by most commercial antivirus because their solutions depend on a signature database and make use of string signature-based detection methods. However, the antivirus detection engine can be avoided by metamorphism techniques. The purpose of this study is to develop a performance model based on computer virus classification and detection. The model would also be able to examine portable executable files that would classify and detect metamorphic computer viruses. A Hidden Markov Model implemented on portable executable files was employed to classify and detect the metamorphic viruses. This proposed model that produce common virus statistical patterns was evaluated by comparing the results with previous related works and famous commercial antiviruses. This was done by investigating the metamorphic computer viruses and their features, and the existing classifications and detection methods. Specifically, this model was applied on binary format of portable executable files and it was able to classify if the files belonged to a virus family. Besides that, the performance of the model, practically implemented and tested, was also evaluated based on detection rate and overall accuracy. The findings indicated that the proposed model is able to classify and detect the metamorphic virus variants in portable executable file format with a high average of 99.7% detection rate. The implementation of the model is proven useful and applicable for antivirus programs