Combating mobile spam through Botnet detection using artificial immune systems

Malicious software (malware) infects large numbers of mobile devices. Once infected these mobile devices may be involved in many kinds of online criminal activity, including identity theft, unsolicited commercial SMS messages, scams and massive coordinated attacks. Until recently, mobile networks have been relatively isolated from the Internet, so there has been little need to protect them against Botnets. Mobile networks are now well integrated with the internet, so threats on the internet, such as Botnets, have started to migrate to mobile networks. This paper studies the potential threat of Botnets based on mobile networks, and proposes the use of computational intelligence techniques to detect Botnets. We then simulate mobile Bot detection by detecting anomalies using an artificial immune system implementation on an Android device.http://www.jucs.org/;internal&action=noaction&Parameter=120816403095

A Review on mobile SMS Spam filtering techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Awareness and perception of phishing variants from Policing, Computing and Criminology students in Canterbury Christ Church University

This study focuses on gauging awareness of different phishing communication students in the School of Law, Policing and Social Sciences and the School of Engineering, Technology and Design in Canterbury Christ Church University and their perception of different phishing variants. There is an exploration of the underlying factors in which students fall victim to different types of phishing attacks from questionnaires and a focus group. The students’ perception of different types of phishing variants was varied from the focus group and anonymised questionnaires. A total of 177 respondents participated in anonymised questionnaires in the study. Students were asked a mixture of scenario-based questions on different phishing attacks, their awareness levels of security tools that can be used against some phishing variants, and if they received any phishing emails in the past. Additionally, 6 computing students in a focus group discussed different types of phishing attacks and recommended potential security countermeasures against them. The vulnerabilities and issues of anti-phishing software, firewalls, and internet browsers that have security toolbars are explained in the study against different types of phishing attacks. The focus group was with computing students and their knowledge about certain phishing variants was limited. The discussion within the focus group was gauging the computing students' understanding and awareness of phishing variants. The questionnaire data collection sample was with first year criminology and final year policing students which may have influenced the results of the questionnaire in terms of their understanding, security countermeasures, and how they identify certain phishing variants. The anonymised questionnaire awareness levels on different types of phishing fluctuated in terms of lack of awareness on certain phishing variants. Some criminology and policing students either did not know about phishing variants or had limited knowledge about different types of phishing communication, security countermeasures, the identifying features of a phishing message, and the precautions they should take against phishing variants from fraudsters

MBotCS: A mobile botnet detection system based on machine learning

As the use of mobile devices spreads dramatically, hackers have started making use of mobile botnets to steal user information or perform other malicious attacks. To address this problem, in this paper we propose a mobile botnet detection system, called MBotCS. MBotCS can detect mobile device traffic indicative of the presence of a mobile botnet based on prior training using machine learning techniques. Our approach has been evaluated using real mobile device traffic captured from Android mobile devices, running normal apps and mobile botnets. In the evaluation, we investigated the use of 5 machine learning classifier algorithms and a group of machine learning box algorithms with different validation schemes. We have also evaluated the effect of our approach with respect to its effect on the overall performance and battery consumption of mobile devices

GUIDE FOR THE COLLECTION OF INSTRUSION DATA FOR MALWARE ANALYSIS AND DETECTION IN THE BUILD AND DEPLOYMENT PHASE

During the COVID-19 pandemic, when most businesses were not equipped for remote work and cloud computing, we saw a significant surge in ransomware attacks. This study aims to utilize machine learning and artificial intelligence to prevent known and unknown malware threats from being exploited by threat actors when developers build and deploy applications to the cloud. This study demonstrated an experimental quantitative research design using Aqua. The experiment\u27s sample is a Docker image. Aqua checked the Docker image for malware, sensitive data, Critical/High vulnerabilities, misconfiguration, and OSS license. The data collection approach is experimental. Our analysis of the experiment demonstrated how unapproved images were prevented from running anywhere in our environment based on known vulnerabilities, embedded secrets, OSS licensing, dynamic threat analysis, and secure image configuration. In addition to the experiment, the forensic data collected in the build and deployment phase are exploitable vulnerability, Critical/High Vulnerability Score, Misconfiguration, Sensitive Data, and Root User (Super User). Since Aqua generates a detailed audit record for every event during risk assessment and runtime, we viewed two events on the Audit page for our experiment. One of the events caused an alert due to two failed controls (Vulnerability Score, Super User), and the other was a successful event meaning that the image is secure to deploy in the production environment. The primary finding for our study is the forensic data associated with the two events on the Audit page in Aqua. In addition, Aqua validated our security controls and runtime policies based on the forensic data with both events on the Audit page. Finally, the study’s conclusions will mitigate the likelihood that organizations will fall victim to ransomware by mitigating and preventing the total damage caused by a malware attack

The effects of security protocols on cybercrime at Ahmadu Bello University, Zaria, Nigeria.

Masters Degree. University of KwaZulu-Natal, Durban.The use of Information Communication Technology (ICT) within the educational sector is increasing rapidly. University systems are becoming increasingly dependent on computerized information systems (CIS) in order to carry out their daily routine. Moreover, CIS no longer process staff records and financial data only, as they once did. Nowadays, universities use CIS to assist in automating the overall system. This automation includes the use of multiple databases, data detail periodicity (i.e. gender, race/ethnicity, enrollment, degrees granted, and program major), record identification (e.g. social security number ‘SSN’), linking to other databases (i.e. linking unit record data with external databases such as university and employment data). The increasing demand and exposure to Internet resources and infrastructure by individuals and universities have made IT infrastructure easy targets for cybercriminals who employ sophisticated attacks such as Advanced Persistent Threats, Distributed Denial of Service attacks and Botnets in order to steal confidential data, identities of individuals and money. Hence, in order to stay in business, universities realise that it is imperative to secure vital Information Systems from easily being exploited by emerging and existing forms of cybercrimes. This study was conducted to determine and evaluate the various forms of cybercrimes and their consequences on the university network at Ahmadu Bello University, Zaria. The study was also aimed at proposing means of mitigating cybercrimes and their effects on the university network. Hence, an exploratory research design supported by qualitative research approach was used in this study. Staff of the Institute of Computing, Information and Communication technology (ICICT) were interviewed. The findings of the study present different security measures, and security tools that can be used to effectively mitigate cybercrimes. It was found that social engineering, denial of service attacks, website defacement were among the types of cybercrimes occurring on the university network. It is therefore recommended that behavioural approach in a form of motivation of staff behaviour, salary increases, and cash incentive to reduce cybercrime perpetrated by these staff

Umělá inteligence v kybernetické bezpečnosti

Artifcial intelligence (AI) and machine learning (ML) have grown rapidly in recent years, and their applications in practice can be seen in many felds, ranging from facial recognition to image analysis. Recent developments in Artificial intelligence have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and likely to adapt AI techniques to their operations. This thesis presents advances made so far in the field of applying AI techniques in cybersecurity for combating against cyber threats, to demonstrate how this promising technology can be a useful tool for detection and prevention of cyberattacks. Furthermore, the research examines how transnational criminal organizations and cybercriminals may leverage developing AI technology to conduct more sophisticated criminal activities. Next, the research outlines the possible dynamic new kind of malware, called X-Ware and X-sWarm, which simulates the swarm system behaviour and integrates the neural network to operate more efficiently as a background for the forthcoming anti-malware solution. This research proposes how to record and visualize the behaviour of these type of malware when it propagates through the file system, computer network (virus process is known) or by observed data analysis (virus process is not known and we observe only the data from the system). Finally, a paradigm of an anti-malware solution, named Multi agent antivirus system has been proposed in the thesis that gives the insight to develop a more robust, adaptive and flexible defence system.Význam umělé inteligence (AI) a strojového učení (ML) v posledních letech rychle rostl a na jejich aplikacích lze vidět, že v mnoha oblastech, od rozpoznávání obličeje až po analýzu obrazu, byl učiněn velký pokrok. Poslední vývoj v oblasti umělé inteligence má obrovský potenciál jak pro obránce v oblasti kybernetické bezpečnosti, tak pro ůtočníky. AI se stává řešením v otázce obrany proti modernímu malware a hraje tak důležitou roli v detekci a prevenci hrozeb v digitálním prostoru. Naproti tomu kyberzločinci jsou si vědomi nových vyhlídek ve spojení s AI a pravděpodobně přizpůsobí tyto techniky novým generacím malware, vektorům útoku a celkově jejich operacím. Tato práce představuje dosavadní pokroky aplikace technik AI v oblasti kybernetické bezpečnosti. V této oblasti tzn. v boji proti kybernetickým hrozbám se ukázuje jako slibná technologie a užitečný nástroj pro detekci a prevenci kybernetických útoků. V práci si rovněž pokládme otázku, jak mohou nadnárodní zločinecké organizace a počítačoví zločinci využít vyvíjející se technologii umělé inteligence k provádění sofistikovanějších trestných činností. Konečně, výzkum nastíní možný nový druh malware, nazvaný X-Ware, který simuluje chování hejnového systému a integruje neuronovou síť tak, aby fungovala efektivněji a tak se celý X-Ware a X-sWarm dal použít nejen jako kybernetická zbraň na útok, ale i jako antivirové obranné řešení. Tento výzkum navrhuje, jak zaznamenat a vizualizovat chování X-Ware, když se šíří prostřednictvím systému souborů, sítí a to jak analýzou jeho dynamiky (proces je znám), tak analýzou dat (proces není znám, pozorujeme jen data). Nakonec bylo v disertační práci navrženo paradigma řešení proti malwaru, jež bylo nazváno „Multi agent antivirus system“. Tato práce tedy poskytuje pohled na vývoj robustnějšího, adaptivnějšího a flexibilnějšího obranného systému.460 - Katedra informatikyvyhově

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI\u27s potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains