Data Exfiltration:A Review of External Attack Vectors and Countermeasures

AbstractContext One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework

Secure policies for the distributed virtual machines in mobile cloud computing

Mobile Cloud Computing (MCC) is a combination of cloud computing and mobile computing through wireless technology in order to overcome mobile devices' resource limitations. In MCC, virtualization plays a key role whereas the cloud resources are shared among many users to help them achieve an efficient performance and exploiting the maximum capacity of the cloud’s servers. However, the lack of security aspect impedes the benefits of virtualization techniques, whereby malicious users can violate and damage sensitive data in distributed Virtual Machines (VMs). Thus, this study aims to provide protection of distributed VMs and mobile user’s sensitive data in terms of security and privacy. This study proposes an approach based on cloud proxy known as Proxy-3S that combines three security policies for VMs; user’s access control, secure allocation, and secure communication. The Proxy-3S keeps the distributed VMs safe in different servers on the cloud. It enhances the grants access authorization for permitted distributed intensive applications’ tasks. Furthermore, an algorithm that enables secure communication among distributed VMs and protection of sensitive data in VMs on the cloud is proposed. A prototype is implemented on a NetworkCloudSim simulator to manage VMs security and data confidentiality automatically. Several experiments were conducted using real-world healthcare distributed application in terms of efficiency, coverage and execution time. The experiments show that the proposed approach achieved lower attacker’s efficiency and coverage ratios; equal to 0.35 and 0.41 respectively in all experimented configurations compared with existing works. In addition, the execution time of the proposed approach is satisfactory ranging from 441ms to 467ms of small and large cloud configurations. This study serves to provide integrity and confidentiality in exchanging sensitive information among multistakeholder in distributed mobile applications

From security to assurance in the cloud: a survey

The cloud computing paradigm has become a mainstream solution for the deployment of business processes and applications. In the public cloud vision, infrastructure, platform, and software services are provisioned to tenants (i.e., customers and service providers) on a pay-as-you-go basis. Cloud tenants can use cloud resources at lower prices, and higher performance and flexibility, than traditional on-premises resources, without having to care about infrastructure management. Still, cloud tenants remain concerned with the cloud's level of service and the nonfunctional properties their applications can count on. In the last few years, the research community has been focusing on the nonfunctional aspects of the cloud paradigm, among which cloud security stands out. Several approaches to security have been described and summarized in general surveys on cloud security techniques. The survey in this article focuses on the interface between cloud security and cloud security assurance. First, we provide an overview of the state of the art on cloud security. Then, we introduce the notion of cloud security assurance and analyze its growing impact on cloud security approaches. Finally, we present some recommendations for the development of next-generation cloud security and assurance solutions

Improved Detection for Advanced Polymorphic Malware

Malicious Software (malware) attacks across the internet are increasing at an alarming rate. Cyber-attacks have become increasingly more sophisticated and targeted. These targeted attacks are aimed at compromising networks, stealing personal financial information and removing sensitive data or disrupting operations. Current malware detection approaches work well for previously known signatures. However, malware developers utilize techniques to mutate and change software properties (signatures) to avoid and evade detection. Polymorphic malware is practically undetectable with signature-based defensive technologies. Today’s effective detection rate for polymorphic malware detection ranges from 68.75% to 81.25%. New techniques are needed to improve malware detection rates. Improved detection of polymorphic malware can only be accomplished by extracting features beyond the signature realm. Targeted detection for polymorphic malware must rely upon extracting key features and characteristics for advanced analysis. Traditionally, malware researchers have relied on limited dimensional features such as behavior (dynamic) or source/execution code analysis (static). This study’s focus was to extract and evaluate a limited set of multidimensional topological data in order to improve detection for polymorphic malware. This study used multidimensional analysis (file properties, static and dynamic analysis) with machine learning algorithms to improve malware detection. This research demonstrated improved polymorphic malware detection can be achieved with machine learning. This study conducted a number of experiments using a standard experimental testing protocol. This study utilized three advanced algorithms (Metabagging (MB), Instance Based k-Means (IBk) and Deep Learning Multi-Layer Perceptron) with a limited set of multidimensional data. Experimental results delivered detection results above 99.43%. In addition, the experiments delivered near zero false positives. The study’s approach was based on single case experimental design, a well-accepted protocol for progressive testing. The study constructed a prototype to automate feature extraction, assemble files for analysis, and analyze results through multiple clustering algorithms. The study performed an evaluation of large malware sample datasets to understand effectiveness across a wide range of malware. The study developed an integrated framework which automated feature extraction for multidimensional analysis. The feature extraction framework consisted of four modules: 1) a pre-process module that extracts and generates topological features based on static analysis of machine code and file characteristics, 2) a behavioral analysis module that extracts behavioral characteristics based on file execution (dynamic analysis), 3) an input file construction and submission module, and 4) a machine learning module that employs various advanced algorithms. As with most studies, careful attention was paid to false positive and false negative rates which reduce their overall detection accuracy and effectiveness. This study provided a novel approach to expand the malware body of knowledge and improve the detection for polymorphic malware targeting Microsoft operating systems

Security and Privacy Threats on Mobile Devices through Side-Channels Analysis

In recent years, mobile devices (such as smartphones and tablets) have become essential tools in everyday life for billions of people all around the world. Users continuously carry such devices with them and use them for daily communication activities and social network interactions. Hence, such devices contain a huge amount of private and sensitive information. For this reason, mobile devices become popular targets of attacks. In most attack settings, the adversary aims to take local or remote control of a device to access user sensitive information. However, such violations are not easy to carry out since they need to leverage a vulnerability of the system or a careless user (i.e., install a malware app from an unreliable source). A different approach that does not have these shortcomings is the side-channels analysis. In fact, side-channels are physical phenomenon that can be measured from both inside or outside a device. They are mostly due to the user interaction with a mobile device, but also to the context in which the device is used, hence they can reveal sensitive user information such as identity and habits, environment, and operating system itself. Hence, this approach consists of inferring private information that is leaked by a mobile device through a side-channel. Besides, side-channel information is also extremely valuable to enforce security mechanisms such as user authentication, intrusion and information leaks detection. This dissertation investigates novel security and privacy challenges on the analysis of side-channels of mobile devices. This thesis is composed of three parts, each focused on a different side-channel: (i) the usage of network traffic analysis to infer user private information; (ii) the energy consumption of mobile devices during battery recharge as a way to identify a user and as a covert channel to exfiltrate data; and (iii) the possible security application of data collected from built-in sensors in mobile devices to authenticate the user and to evade sandbox detection by malware. In the first part of this dissertation, we consider an adversary who is able to eavesdrop the network traffic of the device on the network side (e.g., controlling a WiFi access point). The fact that the network traffic is often encrypted makes the attack even more challenging. Our work proves that it is possible to leverage machine learning techniques to identify user activity and apps installed on mobile devices analyzing the encrypted network traffic they produce. Such insights are becoming a very attractive data gathering technique for adversaries, network administrators, investigators and marketing agencies. In the second part of this thesis, we investigate the analysis of electric energy consumption. In this case, an adversary is able to measure with a power monitor the amount of energy supplied to a mobile device. In fact, we observed that the usage of mobile device resources (e.g., CPU, network capabilities) directly impacts the amount of energy retrieved from the supplier, i.e., USB port for smartphones, wall-socket for laptops. Leveraging energy traces, we are able to recognize a specific laptop user among a group and detect intruders (i.e., user not belonging to the group). Moreover, we show the feasibility of a covert channel to exfiltrate user data which relies on temporized energy consumption bursts. In the last part of this dissertation, we present a side-channel that can be measured within the mobile device itself. Such channel consists of data collected from the sensors a mobile device is equipped with (e.g., accelerometer, gyroscope). First, we present DELTA, a novel tool that collects data from such sensors, and logs user and operating system events. Then, we develop MIRAGE, a framework that relies on sensors data to enhance sandboxes against malware analysis evasion

Deteção de propagação de ameaças e exfiltração de dados em redes empresariais

Modern corporations face nowadays multiple threats within their networks. In an era where companies are tightly dependent on information, these threats can seriously compromise the safety and integrity of sensitive data. Unauthorized access and illicit programs comprise a way of penetrating the corporate networks, able to traversing and propagating to other terminals across the private network, in search of confidential data and business secrets. The efficiency of traditional security defenses are being questioned with the number of data breaches occurred nowadays, being essential the development of new active monitoring systems with artificial intelligence capable to achieve almost perfect detection in very short time frames. However, network monitoring and storage of network activity records are restricted and limited by legal laws and privacy strategies, like encryption, aiming to protect the confidentiality of private parties. This dissertation proposes methodologies to infer behavior patterns and disclose anomalies from network traffic analysis, detecting slight variations compared with the normal profile. Bounded by network OSI layers 1 to 4, raw data are modeled in features, representing network observations, and posteriorly, processed by machine learning algorithms to classify network activity. Assuming the inevitability of a network terminal to be compromised, this work comprises two scenarios: a self-spreading force that propagates over internal network and a data exfiltration charge which dispatch confidential info to the public network. Although features and modeling processes have been tested for these two cases, it is a generic operation that can be used in more complex scenarios as well as in different domains. The last chapter describes the proof of concept scenario and how data was generated, along with some evaluation metrics to perceive the model’s performance. The tests manifested promising results, ranging from 96% to 99% for the propagation case and 86% to 97% regarding data exfiltration.Nos dias de hoje, várias organizações enfrentam múltiplas ameaças no interior da sua rede. Numa época onde as empresas dependem cada vez mais da informação, estas ameaças podem compremeter seriamente a segurança e a integridade de dados confidenciais. O acesso não autorizado e o uso de programas ilícitos constituem uma forma de penetrar e ultrapassar as barreiras organizacionais, sendo capazes de propagarem-se para outros terminais presentes no interior da rede privada com o intuito de atingir dados confidenciais e segredos comerciais. A eficiência da segurança oferecida pelos sistemas de defesa tradicionais está a ser posta em causa devido ao elevado número de ataques de divulgação de dados sofridos pelas empresas. Desta forma, o desenvolvimento de novos sistemas de monitorização ativos usando inteligência artificial é crucial na medida de atingir uma deteção mais precisa em curtos períodos de tempo. No entanto, a monitorização e o armazenamento dos registos da atividade da rede são restritos e limitados por questões legais e estratégias de privacidade, como a cifra dos dados, visando proteger a confidencialidade das entidades. Esta dissertação propõe metodologias para inferir padrões de comportamento e revelar anomalias através da análise de tráfego que passa na rede, detetando pequenas variações em comparação com o perfil normal de atividade. Delimitado pelas camadas de rede OSI 1 a 4, os dados em bruto são modelados em features, representando observações de rede e, posteriormente, processados por algoritmos de machine learning para classificar a atividade de rede. Assumindo a inevitabilidade de um terminal ser comprometido, este trabalho compreende dois cenários: um ataque que se auto-propaga sobre a rede interna e uma tentativa de exfiltração de dados que envia informações para a rede pública. Embora os processos de criação de features e de modelação tenham sido testados para estes dois casos, é uma operação genérica que pode ser utilizada em cenários mais complexos, bem como em domínios diferentes. O último capítulo inclui uma prova de conceito e descreve o método de criação dos dados, com a utilização de algumas métricas de avaliação de forma a espelhar a performance do modelo. Os testes mostraram resultados promissores, variando entre 96% e 99% para o caso da propagação e entre 86% e 97% relativamente ao roubo de dados.Mestrado em Engenharia de Computadores e Telemátic

Federated Agentless Detection of Endpoints Using Behavioral and Characteristic Modeling

During the past two decades computer networks and security have evolved that, even though we use the same TCP/IP stack, network traffic behaviors and security needs have significantly changed. To secure modern computer networks, complete and accurate data must be gathered in a structured manner pertaining to the network and endpoint behavior. Security operations teams struggle to keep up with the ever-increasing number of devices and network attacks daily. Often the security aspect of networks gets managed reactively instead of providing proactive protection. Data collected at the backbone are becoming inadequate during security incidents. Incident response teams require data that is reliably attributed to each individual endpoint over time. With the current state of dissociated data collected from networks using different tools it is challenging to correlate the necessary data to find origin and propagation of attacks within the network. Critical indicators of compromise may go undetected due to the drawbacks of current data collection systems leaving endpoints vulnerable to attacks. Proliferation of distributed organizations demand distributed federated security solutions. Without robust data collection systems that are capable of transcending architectural and computational challenges, it is becoming increasingly difficult to provide endpoint protection at scale. This research focuses on reliable agentless endpoint detection and traffic attribution in federated networks using behavioral and characteristic modeling for incident response

A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner

Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR