Biblio
With the recent advances in information and communication technology, Web and Mobile Internet applications have become a part of our daily lives. These developments have also emerged Information Security concept due to the necessity of protecting information of institutions from Internet attackers. There are many security approaches to provide information security in Enterprise applications. However, using only one of these approaches may not be efficient enough to obtain security. This paper describes a Multi-Layered Framework of implementing two-factor and single sign-on authentication together. The proposed framework generates unique one-time passwords (OTP), which are used to authenticate application data. Nevertheless, using only OTP mechanism does not meet security requirements. Therefore, implementing a separate authentication application which has single sign-on capability is necessary.
Attack simulations may be used to assess the cyber security of systems. In such simulations, the steps taken by an attacker in order to compromise sensitive system assets are traced, and a time estimate may be computed from the initial step to the compromise of assets of interest. Attack graphs constitute a suitable formalism for the modeling of attack steps and their dependencies, allowing the subsequent simulation. To avoid the costly proposition of building new attack graphs for each system of a given type, domain-specific attack languages may be used. These languages codify the generic attack logic of the considered domain, thus facilitating the modeling, or instantiation, of a specific system in the domain. Examples of possible cyber security domains suitable for domain-specific attack languages are generic types such as cloud systems or embedded systems but may also be highly specialized kinds, e.g. Ubuntu installations; the objects of interest as well as the attack logic will differ significantly between such domains. In this paper, we present the Meta Attack Language (MAL), which may be used to design domain-specific attack languages such as the aforementioned. The MAL provides a formalism that allows the semi-automated generation as well as the efficient computation of very large attack graphs. We declare the formal background to MAL, define its syntax and semantics, exemplify its use with a small domain-specific language and instance model, and report on the computational performance.
Public key cryptography or asymmetric keys are widely used in the implementation of data security on information and communication systems. The RSA algorithm (Rivest, Shamir, and Adleman) is one of the most popular and widely used public key cryptography because of its less complexity. RSA has two main functions namely the process of encryption and decryption process. Digital Signature Algorithm (DSA) is a digital signature algorithm that serves as the standard of Digital Signature Standard (DSS). DSA is also included in the public key cryptography system. DSA has two main functions of creating digital signatures and checking the validity of digital signatures. In this paper, the authors compare the computational times of RSA and DSA with some bits and choose which bits are better used. Then combine both RSA and DSA algorithms to improve data security. From the simulation results, the authors chose RSA 1024 for the encryption process and added digital signatures using DSA 512, so the messages sent are not only encrypted but also have digital signatures for the data authentication process.
ARM has become the leading processor architecture for mobile and IoT devices, while it has recently started claiming a bigger slice of the server market pie as well. As such, it will not be long before malware more regularly target the ARM architecture. Therefore, the stealthy operation of Virtual Machine Introspection (VMI) is an obligation to successfully analyze and proactively mitigate this growing threat. Stealthy VMI has proven itself perfectly suitable for malware analysis on Intel's architecture, yet, it often lacks the foundation required to be equally effective on ARM.
Smart meters provide fine-grained electricity consumption reporting to electricity providers. This constitutes an invasive factor into the privacy of the consumers, which has raised many privacy concerns. Although billing requires attributable consumption reporting, consumption reporting for operational monitoring and control measures can be non-attributable. However, the privacy-preserving AMS schemes in the literature tend to address these two categories disjointly — possibly due to their somewhat contradictory characteristics. In this paper, we propose an efficient two-party privacy-preserving cryptographic scheme that addresses operational control measures and billing jointly. It is computationally efficient as it is based on symmetric cryptographic primitives. No online trusted third party (TTP) is required.
An air-gapped network is a type of IT network that is separated from the Internet - physically - due to the sensitive information it stores. Even if such a network is compromised with a malware, the hermetic isolation from the Internet prevents an attacker from leaking out any data - thanks to the lack of connectivity. In this paper we show how attackers can covertly leak sensitive data from air-gapped networks via the row of status LEDs on networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device (‘side-channel'), malware controlling the status LEDs to carry any type of data (‘covert-channel') has never studied before. Sensitive data can be covertly encoded over the blinking of the LEDs and received by remote cameras and optical sensors. A malicious code is executed in a compromised LAN switch or router allowing the attacker direct, low-level control of the LEDs. We provide the technical background on the internal architecture of switches and routers at both the hardware and software level which enables these attacks. We present different modulation and encoding schemas, along with a transmission protocol. We implement prototypes of the malware and discuss its design and implementation. We tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and discuss detection and prevention countermeasures. Our experiments show that sensitive data can be covertly leaked via the status LEDs of switches and routers at bit rates of 1 bit/sec to more than 2000 bit/sec per LED.
A mobile ad hoc network (MANET) is vulnerable to many types of attacks. Thus, security has turned out to be an important factor to facilitate secured communication between mobile nodes in a wireless environment. In this paper we propose a new approach to provide reliable and secure data transmission in MANETs under possible blackhole attacks based on ad hoc on-demand multipath distance vector (AOMDV) protocol and homomorphic encryption scheme for security. The performance of the proposed scheme is stable but that of AOMDV is found to be degrading with the intrusion of malicious nodes in the network. Simulation results show the improvement of packet delivery ratio and network throughput in the presence of blackhole nodes in our proposed scheme.
Nowadays, Vehicular ad hoc network confronts many challenges in terms of security and privacy, due to the fact that data transmitted are diffused in an open access environment. However, highest of drivers want to maintain their information discreet and protected, and they do not want to share their confidential information. So, the private information of drivers who are distributed in this network must be protected against various threats that may damage their privacy. That is why, confidentiality, integrity and availability are the important security requirements in VANET. This paper focus on security threat in vehicle network especially on the availability of this network. Then we regard the rational attacker who decides to lead an attack based on its adversary's strategy to maximize its own attack interests. Our aim is to provide reliability and privacy of VANET system, by preventing attackers from violating and endangering the network. to ensure this objective, we adopt a tree structure called attack tree to model the attacker's potential attack strategies. Also, we join the countermeasures to the attack tree in order to build attack-defense tree for defending these attacks.
Cyber-Physical Systems (CPS), such as Water Distribution Networks (WDNs), deploy digital devices to monitor and control the behavior of physical processes. These digital devices, however, are susceptible to cyber and physical attacks, that may alter their functionality, and therefore the integrity of their measurements/actions. In practice, industrial control systems utilize simple control laws, which rely on various sensor measurements and algorithms which are expected to operate normally. To reduce the impact of a potential failure, operators may deploy redundant components; this however may not be useful, e.g., when a cyber attack at a PLC component occurs. In this work, we address the problem of reducing vulnerability to cyber-physical attacks in water distribution networks. This is achieved by augmenting the graph which describes the information flow from sensors to actuators, by adding new connections and algorithms, to increase the number of redundant cyber components. These, in turn, increase the \textitcyber-physical security level, which is defined in the present paper as the number of malicious attacks a CPS may sustain before becoming unable to satisfy the control requirements. A proof-of-concept of the approach is demonstrated over a simple WDN, with intuition on how this can be used to increase the cyber-physical security level of the system.
Additive manufacturing (AM, or 3D printing) is a novel manufacturing technology that has been adopted in industrial and consumer settings. However, the reliance of this technology on computerization has raised various security concerns. In this paper, we address issues associated with sabotage via tampering during the 3D printing process by presenting an approach that can verify the integrity of a 3D printed object. Our approach operates on acoustic side-channel emanations generated by the 3D printer’s stepper motors, which results in a non-intrusive and real-time validation process that is difficult to compromise. The proposed approach constitutes two algorithms. The first algorithm is used to generate a master audio fingerprint for the verifiable unaltered printing process. The second algorithm is applied when the same 3D object is printed again, and this algorithm validates the monitored 3D printing process by assessing the similarity of its audio signature with the master audio fingerprint. To evaluate the quality of the proposed thresholds, we identify the detectability thresholds for the following minimal tampering primitives: insertion, deletion, replacement, and modification of a single tool path command. By detecting the deviation at the time of occurrence, we can stop the printing process for compromised objects, thus saving time and preventing material waste. We discuss various factors that impact the method, such as background noise, audio device changes and different audio recorder positions.
The Business Intelligence (BI) paradigm is challenged by emerging use cases such as news and social media analytics in which the source data are unstructured, the analysis metrics are unspecified, and the appropriate visual representations are unsupported by mainstream tools. This case study documents the work undertaken in Microsoft Research to enable these use cases in the Microsoft Power BI product. Our approach comprises: (a) back-end pipelines that use AI to infer navigable data structures from streams of unstructured text, media and metadata; and (b) front-end representations of these structures grounded in the Visual Analytics literature. Through our creation of multiple end-to-end data applications, we learned that representing the varying quality of inferred data structures was crucial for making the use and limitations of AI transparent to users. We conclude with reflections on BI in the age of AI, big data, and democratized access to data analytics.
Human face detection plays an essential role in the first stage of face processing applications. In this study, an enhanced face detection framework is proposed to improve detection rate based on skin color and provide a validation process. A preliminary segmentation of the input images based on skin color can significantly reduce search space and accelerate the process of human face detection. The primary detection is based on Haar-like features and the Adaboost algorithm. A validation process is introduced to reject non-face objects, which might occur during the face detection process. The validation process is based on two-stage Extended Local Binary Patterns. The experimental results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate.
Cloud computing is a wide architecture based on diverse models for providing different services of software and hardware. Cloud computing paradigm attracts different users because of its several benefits such as high resource elasticity, expense reduction, scalability and simplicity which provide significant preserving in terms of investment and work force. However, the new approaches introduced by the cloud, related to computation outsourcing, distributed resources, multi-tenancy concept, high dynamism of the model, data warehousing and the nontransparent style of cloud increase the security and privacy concerns and makes building and handling trust among cloud service providers and consumers a critical security challenge. This paper proposes a new approach to improve security of data in cloud computing. It suggests a classification model to categorize data before being introduced into a suitable encryption system according to the category. Since data in cloud has not the same sensitivity level, encrypting it with the same algorithms can lead to a lack of security or of resources. By this method we try to optimize the resources consumption and the computation cost while ensuring data confidentiality.
Return-Oriented Programming (ROP) has emerged as one of the most widely used techniques to exploit software vulnerabilities. Unfortunately, existing ROP protections suffer from a number of shortcomings: they require access to source code and compiler support, focus on specific types of gadgets, depend on accurate disassembly and construction of Control Flow Graphs, or use hardware-dependent (microarchitectural) characteristics. In this paper, we propose EigenROP, a novel system to detect ROP payloads based on unsupervised statistical learning of program characteristics. We study, for the first time, the feasibility and effectiveness of using microarchitecture-independent program characteristics – namely, memory locality, register traffic, and memory reuse distance – for detecting ROP. We propose a novel directional statistics based algorithm to identify deviations from the expected program characteristics during execution. EigenROP works transparently to the protected program, without requiring debug information, source code or disassembly. We implemented a dynamic instrumentation prototype of EigenROP using Intel Pin and measured it against in-the-wild ROP exploits and on payloads generated by the ROP compiler ROPC. Overall, EigenROP achieved significantly higher accuracy than prior anomaly-based solutions. It detected the execution of the ROP gadget chains with 81% accuracy, 80% true positive rate, only 0.8% false positive rate, and incurred comparable overhead to similar Pin-based solutions. This article is summarized in: the morning paper an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer
During the recent years there has been an increased focus on preventing and detecting insider attacks and data thefts. A promising approach has been the construction of data loss prevention systems (DLP) that scan outgoing traffic for sensitive data. However, these automated systems are plagued with a high false positive rate. In this paper we introduce the concept of a meta-score that uses the aggregated output from DLP systems to detect and flag behavior indicative of data leakage. The proposed internal/insider threat score is built on the idea of detecting discrepancies between the userassigned sensitivity level and the sensitivity level inferred by the DLP system, and captures the likelihood that a given entity is leaking data. The practical usefulness of the proposed score is demonstrated on the task of identifying likely internal threats.
We present DECANTeR, a system to detect anomalous outbound HTTP communication, which passively extracts fingerprints for each application running on a monitored host. The goal of our system is to detect unknown malware and backdoor communication indicated by unknown fingerprints extracted from a host's network traffic. We evaluate a prototype with realistic data from an international organization and datasets composed of malicious traffic. We show that our system achieves a false positive rate of 0.9% for 441 monitored host machines, an average detection rate of 97.7%, and that it cannot be evaded by malware using simple evasion techniques such as using known browser user agent values. We compare our solution with DUMONT [24], the current state-of-the-art IDS which detects HTTP covert communication channels by focusing on benign HTTP traffic. The results show that DECANTeR outperforms DUMONT in terms of detection rate, false positive rate, and even evasion-resistance. Finally, DECANTeR detects 96.8% of information stealers in our dataset, which shows its potential to detect data exfiltration.
The exponential increase of Internet of Things (IoT) devices have resulted in a range of new and unanticipated vulnerabilities associated with their use. IoT devices from smart homes to smart enterprises can easily be compromised. One of the major problems associated with the IoT is maintaining security; the vulnerable nature of IoT devices poses a challenge to many aspects of security, including security testing and analysis. It is trivial to perform the security analysis for IoT devices to understand the loop holes and very nature of the devices itself. Given these issues, there has been less emphasis on security testing and analysis of the IoT. In this paper, we show our preliminary efforts in the area of security analysis for IoT devices and introduce a security IoT testbed for performing security analysis. We also discuss the necessary design, requirements and the architecture to support our security analysis conducted via the proposed testbed.
Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler (KL) divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.