Malware Resistant Data Protection in Hyper-connected Networks: A survey

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions.Comment: 30 pages, 9 figures, 7 tables, no where submitted ye

Smart techniques and tools to detect Steganography - a viable practice to Security Office Department

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementInternet is today a commodity and a way for being connect to the world. It is through Internet is where most of the information is shared and where people run their businesses. However, there are some people that make a malicious use of it. Cyberattacks have been increasing all over the recent years, targeting people and organizations, looking to perform illegal actions. Cyber criminals are always looking for new ways to deliver malware to victims to launch an attack. Millions of users share images and photos on their social networks and generally users find them safe to use. Contrary to what most people think, images can contain a malicious payload and perform harmful actions. Steganography is the technique of hiding data, which, combined with media files, can be used to place malicious code. This problem, leveraged by the continuous media file sharing through massive use of digital platforms, may become a worldwide threat in malicious content sharing. Like phishing, people and organizations must be trained to suspect about inappropriate content and implement the proper set of actions to reduce probability of infections when accessing files supposed to be inoffensive. The aim of this study will try to help people and organizations by trying to set a toolbox where it can be possible to get some tools and techniques to assist in dealing with this kind of situations. A theoretical overview will be performed over other concepts such as Steganalysis, touching also Deep Learning and in Machine Learning to assess which is the range of its applicability in find solutions in detection and facing these situations. In addition, understanding the current main technologies, architectures and users’ hurdles will play an important role in designing and developing the proposed toolbox artifact

Robust behavioral malware detection

Computer security attacks evolve to evade deployed defenses. Recent attacks have ranged from exploiting generic software vulnerabilities in memory-unsafe languages such as buffer overflows and format string vulnerabilities to exploiting logic errors in web applications, through means such as SQL injection and cross-site scripting. Furthermore, recent attacks have focused on escalating privileges and stealing sensitive information by exploiting new hardware or operating system (OS) interfaces. Computer security attacks are also now relying on social engineering techniques to run malicious programs on victims' machines; instances of such abuse include phishing and watering hole attacks, both of which trick people into running malicious code or divulging confidential information. Thus, traditional computer security methods, such as OS confinement and program analysis, will not prevent new attacks that do not violate OS confinement or present illegal program behaviors. Another challenge is that traditional security approaches have large trusted code bases (TCBs), which include hardware, OSs, and other software components that implement authentication and authorization logic across a distributed system. This is a vulnerable area because these components are complex and often contain vulnerabilities that undermine the overall system's integrity or confidentiality. Evasive attacks on vulnerable systems -- especially in instances where trusted components turn malicious -- inspire the creation of defenses that can augment formally specified mechanisms against known threats. Specifically, this thesis advances the state of the art in behavioral malware detection -- detecting previously unknown malware in the very early stages of infection within an enterprise network. Here we assess three fundamental insights of modern-day attacks and then describe a cross-layer defense against such attacks. First, we make a low-level machine state visible to behavioral analysis, significantly minimizing the TCB and its associated vulnerabilities. Specifically, our behavioral detector utilizes an executable code's dynamic properties, with architectural and micro-architectural states as input. Second, we evaluate behavioral detectors against adaptive adversaries. For this purpose, we introduce a new metric to determine a detector's robustness against malware modifications, which serves as a step toward explainability of machine learning-based malware detectors. Finally, we exploit the fact that attacks spread through only a limited number of vectors and propose new techniques to analyze the resulting dynamic correlations created among machines. These insights show that behavioral detectors can efficiently protect both individual devices and end hosts within enterprise networks. We present three types of such behavioral detectors. Sherlock protects resource-constrained devices, such as mobile phones and Internet-of-things (IoT) devices, without modifying the software/hardware stack. Sherlock's supervised and unsupervised versions outperform prior work by 24.7% and 12.5% (area under the curve (AUC) metric), respectively, and detects stealthy malware that often evades static analysis tools. The second behavioral detector, Shape-GD, protects devices within an enterprise network. It monitors devices on the network, aggregates data from weak local detectors, overlays that with network-level information, and then makes early, robust predictions regarding malicious activity. Shape-GD achieves its goals by exploiting latent attack semantics. Specifically, it analyzes communication patterns across multiple devices, partitioning them into neighborhoods. Devices within the same neighborhood are likely to be exposed to the same attack vector. Furthermore, we hypothesize that the conditional distribution of false positives is different from that of true positives; i.e., given a neighborhood of nodes, we can compute the aggregate distributional shape of alert feature vectors from the neighborhood itself and provide robust labels. We evaluate Shape-GD by emulating a large community of Windows systems using the system call traces from a few thousand malicious and benign applications; we simulate both a phishing attack in a corporate email network as well as a watering hole attack through a popular website. In both scenarios, Shape-GD identifies malware early on (~100 infected nodes in a ~100K-node system for watering hole attacks, and ~10 of ~1,000 for phishing attacks) and robustly (with ~100% global true-positive and ~1% global false-positive rates). The third behavioral detector, Centurion, detects malware across machines monitored by an anti-virus company. It is able to analyze behavior from 5 million Symantec client machines in real time and discovers malware by correlating file downloads across multiple machines. Compared with a recent local detector that analyzes metadata from file downloads, Centurion reduced the number of false positives from ~1M to ~110K and increased the true-positive rate by a factor of ~2.5. In addition, on average, Centurion detects malware 345 days earlier than commercial anti-virus products.Electrical and Computer Engineerin

On the Generation of Cyber Threat Intelligence: Malware and Network Traffic Analyses

In recent years, malware authors drastically changed their course on the subject of threat design and implementation. Malware authors, namely, hackers or cyber-terrorists perpetrate new forms of cyber-crimes involving more innovative hacking techniques. Being motivated by financial or political reasons, attackers target computer systems ranging from personal computers to organizations’ networks to collect and steal sensitive data as well as blackmail, scam people, or scupper IT infrastructures. Accordingly, IT security experts face new challenges, as they need to counter cyber-threats proactively. The challenge takes a continuous allure of a fight, where cyber-criminals are obsessed by the idea of outsmarting security defenses. As such, security experts have to elaborate an effective strategy to counter cyber-criminals. The generation of cyber-threat intelligence is of a paramount importance as stated in the following quote: “the field is owned by who owns the intelligence”. In this thesis, we address the problem of generating timely and relevant cyber-threat intelligence for the purpose of detection, prevention and mitigation of cyber-attacks. To do so, we initiate a research effort, which falls into: First, we analyze prominent cyber-crime toolkits to grasp the inner-secrets and workings of advanced threats. We dissect prominent malware like Zeus and Mariposa botnets to uncover their underlying techniques used to build a networked army of infected machines. Second, we investigate cyber-crime infrastructures, where we elaborate on the generation of a cyber-threat intelligence for situational awareness. We adapt a graph-theoretic approach to study infrastructures used by malware to perpetrate malicious activities. We build a scoring mechanism based on a page ranking algorithm to measure the badness of infrastructures’ elements, i.e., domains, IPs, domain owners, etc. In addition, we use the min-hashing technique to evaluate the level of sharing among cyber-threat infrastructures during a period of one year. Third, we use machine learning techniques to fingerprint malicious IP traffic. By fingerprinting, we mean detecting malicious network flows and their attribution to malware families. This research effort relies on a ground truth collected from the dynamic analysis of malware samples. Finally, we investigate the generation of cyber-threat intelligence from passive DNS streams. To this end, we design and implement a system that generates anomalies from passive DNS traffic. Due to the tremendous nature of DNS data, we build a system on top of a cluster computing framework, namely, Apache Spark [70]. The integrated analytic system has the ability to detect anomalies observed in DNS records, which are potentially generated by widespread cyber-threats

Appraisal of Cashless Policy on the Nigerian Financial System

The Central Bank of Nigeria (CBN) has been active in the inauguration of policies and schemes to foster the implementation of the cashless policy in Nigeria. However the current transition to cashless economy raises a lot of concerns with no substantial evidence yet to justify its implementation. This study was carried out in order to appraise the implementation of the cashless policy since its introduction into the Nigerian financial system in 2012 and also to examine the persistent challenges facing its implementation. In view of the above stated objective, primary data were collected with the aid of the questionnaire, which was randomly administered to 120 respondents ranging from First Bank, Zenith Bank and United Bank for Africa. The banks were selected based on their total assets and the information collected covered the activities of the CBN and that of these banks towards implementation of the cashless policy from 2012 till date.The data collected were presented and analyzed with the aid of the Statistical Package for Social Sciences (SPSS) using descriptive statistics and one-sample t-test. The results led to the conclusion that despite the need to operate cashless transactions dominating the modern Nigerian economy, the cashless policy will have the desired impact only if a lot is done to ensure the implementation of an effective cashless system

IoT-MQTT based denial of service attack modelling and detection

Internet of Things (IoT) is poised to transform the quality of life and provide new business opportunities with its wide range of applications. However, the bene_ts of this emerging paradigm are coupled with serious cyber security issues. The lack of strong cyber security measures in protecting IoT systems can result in cyber attacks targeting all the layers of IoT architecture which includes the IoT devices, the IoT communication protocols and the services accessing the IoT data. Various IoT malware such as Mirai, BASHLITE and BrickBot show an already rising IoT device based attacks as well as the usage of infected IoT devices to launch other cyber attacks. However, as sustained IoT deployment and functionality are heavily reliant on the use of e_ective data communication protocols, the attacks on other layers of IoT architecture are anticipated to increase. In the IoT landscape, the publish/- subscribe based Message Queuing Telemetry Transport (MQTT) protocol is widely popular. Hence, cyber security threats against the MQTT protocol are projected to rise at par with its increasing use by IoT manufacturers. In particular, the Internet exposed MQTT brokers are vulnerable to protocolbased Application Layer Denial of Service (DoS) attacks, which have been known to cause wide spread service disruptions in legacy systems. In this thesis, we propose Application Layer based DoS attacks that target the authentication and authorisation mechanism of the the MQTT protocol. In addition, we also propose an MQTT protocol attack detection framework based on machine learning. Through extensive experiments, we demonstrate the impact of authentication and authorisation DoS attacks on three opensource MQTT brokers. Based on the proposed DoS attack scenarios, an IoT-MQTT attack dataset was generated to evaluate the e_ectiveness of the proposed framework to detect these malicious attacks. The DoS attack evaluation results obtained indicate that such attacks can overwhelm the MQTT brokers resources even when legitimate access to it was denied and resources were restricted. The evaluations also indicate that the proposed DoS attack scenarios can signi_cantly increase the MQTT message delay, especially in QoS2 messages causing heavy tail latencies. In addition, the proposed MQTT features showed high attack detection accuracy compared to simply using TCP based features to detect MQTT based attacks. It was also observed that the protocol _eld size and length based features drastically reduced the false positive rates and hence, are suitable for detecting IoT based attacks

Security for Service-Oriented On-Demand Grid Computing

Grid Computing ist mittlerweile zu einem etablierten Standard für das verteilte Höchstleistungsrechnen geworden. Während die erste Generation von Grid Middleware-Systemen noch mit proprietären Schnittstellen gearbeitet hat, wurde durch die Einführung von service-orientierten Standards wie WSDL und SOAP durch die Open Grid Services Architecture (OGSA) die Interoperabilität von Grids signifikant erhöht. Dies hat den Weg für mehrere nationale und internationale Grid-Projekten bereitet, in denen eine groß e Anzahl von akademischen und eine wachsende Anzahl von industriellen Anwendungen im Grid ausgeführt werden, die die bedarfsgesteuerte (on-demand) Provisionierung und Nutzung von Ressourcen erfordern. Bedarfsgesteuerte Grids zeichnen sich dadurch aus, dass sowohl die Software, als auch die Benutzer einer starken Fluktuation unterliegen. Weiterhin sind sowohl die Software, als auch die Daten, auf denen operiert wird, meist proprietär und haben einen hohen finanziellen Wert. Dies steht in starkem Kontrast zu den heutigen Grid-Anwendungen im akademischen Umfeld, die meist offen im Quellcode vorliegen bzw. frei verfügbar sind. Um den Ansprüchen einer bedarfsgesteuerten Grid-Nutzung gerecht zu werden, muss das Grid administrative Komponenten anbieten, mit denen Anwender autonom Software installieren können, selbst wenn diese Root-Rechte benötigen. Zur gleichen Zeit muss die Sicherheit des Grids erhöht werden, um Software, Daten und Meta-Daten der kommerziellen Anwender zu schützen. Dies würde es dem Grid auch erlauben als Basistechnologie für das gerade entstehende Gebiet des Cloud Computings zu dienen, wo ähnliche Anforderungen existieren. Wie es bei den meisten komplexen IT-Systemen der Fall ist, sind auch in traditionellen Grid Middlewares Schwachstellen zu finden, die durch die geforderten Erweiterungen der administrativen Möglichkeiten potentiell zu einem noch größ erem Problem werden. Die Schwachstellen in der Grid Middleware öffnen einen homogenen Angriffsvektor auf die ansonsten heterogenen und meist privaten Cluster-Umgebungen. Hinzu kommt, dass anders als bei den privaten Cluster-Umgebungen und kleinen akademischen Grid-Projekten die angestrebten groß en und offenen Grid-Landschaften die Administratoren mit gänzlich unbekannten Benutzern und Verhaltenstrukturen konfrontieren. Dies macht das Erkennen von böswilligem Verhalten um ein Vielfaches schwerer. Als Konsequenz werden Grid-Systeme ein immer attraktivere Ziele für Angreifer, da standardisierte Zugriffsmöglichkeiten Angriffe auf eine groß e Anzahl von Maschinen und Daten von potentiell hohem finanziellen Wert ermöglichen. Während die Rechenkapazität, die Bandbreite und der Speicherplatz an sich schon attraktive Ziele darstellen können, sind die im Grid enthaltene Software und die gespeicherten Daten viel kritischere Ressourcen. Modelldaten für die neuesten Crash-Test Simulationen, eine industrielle Fluid-Simulation, oder Rechnungsdaten von Kunden haben einen beträchtlichen Wert und müssen geschützt werden. Wenn ein Grid-Anbieter nicht für die Sicherheit von Software, Daten und Meta-Daten sorgen kann, wird die industrielle Verbreitung der offenen Grid-Technologie nicht stattfinden. Die Notwendigkeit von strikten Sicherheitsmechanismen muss mit der diametral entgegengesetzten Forderung nach einfacher und schneller Integration von neuer Software und neuen Kunden in Einklang gebracht werden. In dieser Arbeit werden neue Ansätze zur Verbesserung der Sicherheit und Nutzbarkeit von service-orientiertem bedarfsgesteuertem Grid Computing vorgestellt. Sie ermöglichen eine autonome und sichere Installation und Nutzung von komplexer, service-orientierter und traditioneller Software auf gemeinsam genutzen Ressourcen. Neue Sicherheitsmechanismen schützen Software, Daten und Meta-Daten der Anwender vor anderen Anwendern und vor externen Angreifern. Das System basiert auf Betriebssystemvirtualisierungstechnologien und bietet dynamische Erstellungs- und Installationsfunktionalitäten für virtuelle Images in einer sicheren Umgebung, in der automatisierte Mechanismen anwenderspezifische Firewall-Regeln setzen, um anwenderbezogene Netzwerkpartitionen zu erschaffen. Die Grid-Umgebung wird selbst in mehrere Bereiche unterteilt, damit die Kompromittierung von einzelnen Komponenten nicht so leicht zu einer Gefährdung des gesamten Systems führen kann. Die Grid-Headnode und der Image-Erzeugungsserver werden jeweils in einzelne Bereiche dieser demilitarisierten Zone positioniert. Um die sichere Anbindung von existierenden Geschäftsanwendungen zu ermöglichen, werden der BPEL-Standard (Business Process Execution Language) und eine Workflow-Ausführungseinheit um Grid-Sicherheitskonzepte erweitert. Die Erweiterung erlaubt eine nahtlose Integration von geschützten Grid Services mit existierenden Web Services. Die Workflow-Ausführungseinheit bietet die Erzeugung und die Erneuerung (im Falle von lange laufenden Anwendungen) von Proxy-Zertifikaten. Der Ansatz ermöglicht die sichere gemeinsame Ausführung von neuen, fein-granularen, service-orientierten Grid Anwendungen zusammen mit traditionellen Batch- und Job-Farming Anwendungen. Dies wird durch die Integration des vorgestellten Grid Sandboxing-Systems in existierende Cluster Scheduling Systeme erreicht. Eine innovative Server-Rotationsstrategie sorgt für weitere Sicherheit für den Grid Headnode Server, in dem transparent das virtuelle Server Image erneuert wird und damit auch unbekannte und unentdeckte Angriffe neutralisiert werden. Um die Angriffe, die nicht verhindert werden konnten, zu erkennen, wird ein neuartiges Intrusion Detection System vorgestellt, das auf Basis von Datenstrom-Datenbanksystemen funktioniert. Als letzte Neuerung dieser Arbeit wird eine Erweiterung des modellgetriebenen Softwareentwicklungsprozesses eingeführt, die eine automatisierte Generierung von sicheren Grid Services ermöglicht, um die komplexe und damit unsichere manuelle Erstellung von Grid Services zu ersetzen. Eine prototypische Implementierung der Konzepte wird auf Basis des Globus Toolkits 4, der Sun Grid Engine und der ActiveBPEL Engine vorgestellt. Die modellgetriebene Entwicklungsumgebung wurde in Eclipse für das Globus Toolkit 4 realisiert. Experimentelle Resultate und eine Evaluation der kritischen Komponenten des vorgestellten neuen Grids werden präsentiert. Die vorgestellten Sicherheitsmechanismem sollen die nächste Phase der Evolution des Grid Computing in einer sicheren Umgebung ermöglichen

State-of-the-Art Sensors Technology in Spain 2015: Volume 1

This book provides a comprehensive overview of state-of-the-art sensors technology in specific leading areas. Industrial researchers, engineers and professionals can find information on the most advanced technologies and developments, together with data processing. Further research covers specific devices and technologies that capture and distribute data to be processed by applying dedicated techniques or procedures, which is where sensors play the most important role. The book provides insights and solutions for different problems covering a broad spectrum of possibilities, thanks to a set of applications and solutions based on sensory technologies. Topics include: • Signal analysis for spectral power • 3D precise measurements • Electromagnetic propagation • Drugs detection • e-health environments based on social sensor networks • Robots in wireless environments, navigation, teleoperation, object grasping, demining • Wireless sensor networks • Industrial IoT • Insights in smart cities • Voice recognition • FPGA interfaces • Flight mill device for measurements on insects • Optical systems: UV, LEDs, lasers, fiber optics • Machine vision • Power dissipation • Liquid level in fuel tanks • Parabolic solar tracker • Force sensors • Control for a twin roto

UMSL Bulletin 2018-2019

The University Bulletin/Course Catalog 2018-2019 Edition.https://irl.umsl.edu/bulletin/1082/thumbnail.jp

UMSL Bulletin 2019-2020

The University Bulletin/Course Catalog 2019-2020 Edition.https://irl.umsl.edu/bulletin/1083/thumbnail.jp