Biblio
Attacks on cloud-computing services are becoming more prevalent with recent victims including Tesla, Aviva Insurance and SIM-card manufacturer Gemalto[1]. The risk posed to organisations from malicious insiders is becoming more widely known about and consequently many are now investing in hardware, software and new processes to try to detect these attacks. As for all types of attack vector, there will always be those which are not known about and those which are known about but remain exceptionally difficult to detect - particularly in a timely manner. We believe that insider attacks are of particular concern in a cloud-computing environment, and that cloud-service providers should enhance their ability to detect them by means of indirect detection. We propose a combined attack-tree and kill-chain based method for identifying multiple indirect detection measures. Specifically, the use of attack trees enables us to encapsulate all detection opportunities for insider attacks in cloud-service environments. Overlaying the attack tree on top of a kill chain in turn facilitates indirect detection opportunities higher-up the tree as well as allowing the provider to determine how far an attack has progressed once suspicious activity is detected. We demonstrate the method through consideration of a specific type of insider attack - that of attempting to capture virtual machines in transit within a cloud cluster via use of a network tap, however, the process discussed here applies equally to all cloud paradigms.
The world is witnessing an exceptional expansion in the cloud enabled services which is further growing day by day due to advancement & requirement of technology. However, the identification of vulnerabilities & its exploitation in the cloud computing will always be the major challenge and concern for any cloud computing system. To understand the challenges and its consequences and further provide mitigation techniques for the vulnerabilities, the identification of cloud specific vulnerabilities needs to be examined first and after identification of vulnerabilities a detailed taxonomy must be positioned. In this paper several cloud specific identified vulnerabilities have been studied which is listed by the NVD, ENISA CSA etc accordingly a unified taxonomy for security vulnerabilities has been prepared. In this paper we proposed a comprehensive taxonomy for cloud specific vulnerabilities on the basis of several parameters like attack vector, CVSS score, complexity etc which will be further act as input for the analysis and mitigation of cloud vulnerabilities. Scheming of Taxonomy of vulnerabilities is an effective way for cloud administrators, cloud mangers, cloud consumers and other stakeholders for identifying, understanding and addressing security risks.
Due to safety concerns and legislation implemented by various governments, the maritime sector adopted Automatic Identification System (AIS). Whilst governments and state agencies have an increasing reliance on AIS data, the underlying technology can be found to be fundamentally insecure. This study identifies and describes a number of potential attack vectors and suggests conceptual countermeasures to mitigate such attacks. With interception by Navy and Coast Guard as well as marine navigation and obstacle avoidance, the vulnerabilities within AIS call into question the multiple deployed overlapping AIS networks, and what the future holds for the protocol.
The nodes in Mobile Ad hoc Network (MANET) can self-assemble themselves, locomote unreservedly and can interact with one another without taking any help from a centralized authority or fixed infrastructure. Due to its continuously changing and self-organizing nature, MANET is vulnerable to a variety of attacks like spoofing attack, wormhole attack, black hole attack, etc. This paper compares and analyzes the repercussion of the wormhole attack on MANET's two common routing protocols of reactive category, specifically, Dynamic Source Routing (DSR) and Ad-hoc On-Demand Distance Vector (AODV) by increasing the number of wormhole tunnels in MANET. The results received by simulation will reveal that DSR is greatly affected by this attack. So, as a solution, a routing algorithm for DSR which is based on trust is proposed to prevent the routes from caching malicious nodes.
The need for data exchange and storage is currently increasing. The increased need for data exchange and storage also increases the need for data exchange devices and media. One of the most commonly used media exchanges and data storage is the USB Flash Drive. USB Flash Drive are widely used because they are easy to carry and have a fairly large storage. Unfortunately, this increased need is not directly proportional to an increase in awareness of device security, both for USB flash drive devices and computer devices that are used as primary storage devices. This research shows the threats that can arise from the use of USB Flash Drive devices. The threat that is used in this research is the fork bomb implemented on an Arduino Pro Micro device that is converted to a USB Flash drive. The purpose of the Fork Bomb is to damage the memory performance of the affected devices. As a result, memory performance to execute the process will slow down. The use of a USB Flash drive as an attack vector with the fork bomb method causes users to not be able to access the operating system that was attacked. The results obtained indicate that the USB Flash Drive can be used as a medium of Fork Bomb attack on the Windows operating system.
Nowadays, everyone is living in a digital world with various of virtual experiences and realities, but all of them may eventually cause real threats in our real world. Some of these threats have been born together with the first electronic mail service. Some of them might be considered as really basic and simple, compared to others that were developed and advanced in time to adapt themselves for the security defense mechanisms of the modern digital world. On a daily basis, more than 238.4 billion emails are sent worldwide, which makes more than 2.7 million emails per second, and these statistics are only from the publicly visible networks. Having that information and considering around 60% and above of all emails as threatening or not legitimate, is more than concerning. Unfortunately, even the modern security measures and systems are not capable to identify and prevent all the fraudulent content that is created and distributed every day. In this paper we will cover the most common attack vectors, involving the already mass email infrastructures, the required contra measures to minimize the impact over the corporate environments and what else should be developed to mitigate the modern sophisticated email attacks.
Advance persistent threat is a primary security concerns to the big organizations and its technical infrastructure, from cyber criminals seeking personal and financial information to state sponsored attacks designed to disrupt, compromising infrastructure, sidestepping security efforts thus causing serious damage to organizations. A skilled cybercriminal using multiple attack vectors and entry points navigates around the defenses, evading IDS/Firewall detection and breaching the network in no time. To understand the big picture, this paper analyses an approach to advanced persistent threat by doing the same things the bad guys do on a network setup. We will walk through various steps from foot-printing and reconnaissance, scanning networks, gaining access, maintaining access to finally clearing tracks, as in a real world attack. We will walk through different attack tools and exploits used in each phase and comparative study on their effectiveness, along with explaining their attack vectors and its countermeasures. We will conclude the paper by explaining the factors which actually qualify to be an Advance Persistent Threat.
In the network security risk assessment on critical information infrastructure of smart city, to describe attack vectors for predicting possible initial access is a challenging task. In this paper, an attack vector evaluation model based on weakness, path and action is proposed, and the formal representation and quantitative evaluation method are given. This method can support the assessment of attack vectors based on known and unknown weakness through combination of depend conditions. In addition, defense factors are also introduced, an attack vector evaluation model of integrated defense is proposed, and an application example of the model is given. The research work in this paper can provide a reference for the vulnerability assessment of attack vector.
Everyday., the DoS/DDoS attacks are increasing all over the world and the ways attackers are using changing continuously. This increase and variety on the attacks are affecting the governments, institutions, organizations and corporations in a bad way. Every successful attack is causing them to lose money and lose reputation in return. This paper presents an introduction to a method which can show what the attack and where the attack based on. This is tried to be achieved with using clustering algorithm DBSCAN on network traffic because of the change and variety in attack vectors.
The Internet of Things (IoT) holds great potential for productivity, quality control, supply chain efficiencies and overall business operations. However, with this broader connectivity, new vulnerabilities and attack vectors are being introduced, increasing opportunities for systems to be compromised by hackers and targeted attacks. These vulnerabilities pose severe threats to a myriad of IoT applications within areas such as manufacturing, healthcare, power and energy grids, transportation and commercial building management. While embedded OEMs offer technologies, such as hardware Trusted Platform Module (TPM), that deploy strong chain-of-trust and authentication mechanisms, still they struggle to protect against vulnerabilities introduced by vendors and end users, as well as additional threats posed by potential technical vulnerabilities and zero-day attacks. This paper proposes a pro-active policy-based approach, enforcing the principle of least privilege, through hardware Security Policy Engine (SPE) that actively monitors communication of applications and system resources on the system communication bus (ARM AMBA-AXI4). Upon detecting a policy violation, for example, a malicious application accessing protected storage, it counteracts with predefined mitigations to limit the attack. The proposed SPE approach widely complements existing embedded hardware and software security technologies, targeting the mitigation of risks imposed by unknown vulnerabilities of embedded applications and protocols.
It is well known that distributed cyber attacks simultaneously launched from many hosts have caused the most serious problems in recent years including problems of privacy leakage and denial of services. Thus, how to detect those attacks at early stage has become an important and urgent topic in the cyber security community. For this purpose, recognizing C&C (Command & Control) communication between compromised bots and the C&C server becomes a crucially important issue, because C&C communication is in the preparation phase of distributed attacks. Although attack detection based on signature has been practically applied since long ago, it is well-known that it cannot efficiently deal with new kinds of attacks. In recent years, ML(Machine learning)-based detection methods have been studied widely. In those methods, feature selection is obviously very important to the detection performance. We once utilized up to 55 features to pick out C&C traffic in order to accomplish early detection of DDoS attacks. In this work, we try to answer the question that "Are all of those features really necessary?" We mainly investigate how the detection performance moves as the features are removed from those having lowest importance and we try to make it clear that what features should be payed attention for early detection of distributed attacks. We use honeypot data collected during the period from 2008 to 2013. SVM(Support Vector Machine) and PCA(Principal Component Analysis) are utilized for feature selection and SVM and RF(Random Forest) are for building the classifier. We find that the detection performance is generally getting better if more features are utilized. However, after the number of features has reached around 40, the detection performance will not change much even more features are used. It is also verified that, in some specific cases, more features do not always means a better detection performance. We also discuss 10 important features which have the biggest influence on classification.
The recently proposed Oblivious Cross-Tags (OXT) protocol (CRYPTO 2013) has broken new ground in designing efficient searchable symmetric encryption (SSE) protocol with support for conjunctive keyword search in a single-writer single-reader framework. While the OXT protocol offers high performance by adopting a number of specialised data-structures, it also trades-off security by leaking 'partial' database information to the server. Recent attacks have exploited similar partial information leakage to breach database confidentiality. Consequently, it is an open problem to design SSE protocols that plug such leakages while retaining similar efficiency. In this paper, we propose a new SSE protocol, called Hidden Cross-Tags (HXT), that removes 'Keyword Pair Result Pattern' (KPRP) leakage for conjunctive keyword search. We avoid this leakage by adopting two additional cryptographic primitives - Hidden Vector Encryption (HVE) and probabilistic (Bloom filter) indexing into the HXT protocol. We propose a 'lightweight' HVE scheme that only uses efficient symmetric-key building blocks, and entirely avoids elliptic curve-based operations. At the same time, it affords selective simulation-security against an unbounded number of secret-key queries. Adopting this efficient HVE scheme, the overall practical storage and computational overheads of HXT over OXT are relatively small (no more than 10% for two keywords query, and 21% for six keywords query), while providing a higher level of security.
In modern societies, critical services such as transportation, power supply, water treatment and distribution are strongly dependent on Industrial Control Systems (ICS). As technology moves along, new features improve services provided by such ICS. On the other hand, this progress also introduces new risks of cyber attacks due to the multiple direct and indirect dependencies between cyber and physical components of such systems. Performing rigorous security tests and risk analysis in these critical systems is thus a challenging task, because of the non-trivial interactions between digital and physical assets and the domain-specific knowledge necessary to analyse a particular system. In this work, we propose a methodology to model and analyse a System Under Test (SUT) as a data flow graph that highlights interactions among internal entities throughout the SUT. This model is automatically extracted from production code available in Programmable Logic Controllers (PLCs). We also propose a reachability algorithm and an attack diagram that will emphasize the dependencies between cyber and physical domains, thus enabling a human analyst to gauge various attack vectors that arise from subtle dependencies in data and information propagation. We test our methodology in a functional water treatment testbed and demonstrate how an analyst could make use of our designed attack diagrams to reason on possible threats to various targets of the SUT.
Legacy software, outdated applications and fast changing technologies pose a serious threat to information security. Several domains, such as long-life industrial control systems and Internet of Things devices, suffer from it. In many cases, system updates and new acquisitions are not an option. In this paper, a framework that combines a reverse proxy with various deception-based defense mechanisms is presented. It is designed to autonomously provide deception methods to web applications. Context-awareness and minimal configuration overhead make it perfectly suited to work as a service. The framework is built modularly to provide flexibility and adaptability to the application use case. It is evaluated with common web-based applications such as content management systems and several frequent attack vectors against them. Furthermore, the security and performance implications of the additional security layer are quantified and discussed. It is found that, given sound implementation, no further attack vectors are introduced to the web application. The performance of the prototypical framework increases the delay of communication with the underlying web application. This delay is within tolerable boundaries and can be further reduced by a more efficient implementation.
High-end vehicles incorporate about one hundred computers; physical and virtualized ones; self-driving vehicles even more. This allows a plethora of attack combinations. This paper demonstrates how to assess exploitability risks of vehicular on-board networks via automatically generated and analyzed attack graphs. Our stochastic model and algorithm combine all possible attack vectors and consider attacker resources more efficiently than Bayesian networks. We designed and implemented an algorithm that assesses a compilation of real vehicle development documents within only two CPU minutes, using an average of about 100 MB RAM. Our proof of concept "Security Analyzer for Exploitability Risks" (SAlfER) is 200 to 5 000 times faster and 40 to 200 times more memory-efficient than an implementation with UnBBayes1. Our approach aids vehicle development by automatically re-checking the architecture for attack combinations that may have been enabled by mistake and which are not trivial to spot by the human developer. Our approach is intended for and relevant for industrial application. Our research is part of a collaboration with a globally operating automotive manufacturer and is aimed at supporting the security of autonomous, connected, electrified, and shared vehicles.