Biblio

Information security deals with a large number of subjects like spoofed message detection, audio processing, video surveillance and cyber-attack detections. However the biggest threat for the homeland security is cyber-attacks. Distributed Denial of Service attack is one among them. Interconnected systems such as database server, web server, cloud computing servers etc., are now under threads from network attackers. Denial of service is common attack in the internet which causes problem for both the user and the service providers. Distributed attack sources can be used to enlarge the attack in case of Distributed Denial of Service so that the effect of the attack will be high. Distributed Denial of Service attacks aims at exhausting the communication and computational power of the network by flooding the packets through the network and making malicious traffic in the network. In order to be an effective service the DDoS attack must be detected and mitigated quickly before the legitimate user access the attacker's target. The group of systems that is used to perform the DoS attack is known as the botnets. This paper introduces the overview of the state of art in DDoS attack detection strategies.

This paper presents a framework for privacy-preserving video delivery system to fulfill users' privacy demands. The proposed framework leverages the inference channels in sensitive behavior prediction and object tracking in a video surveillance system for the sequence privacy protection. For such a goal, we need to capture different pieces of evidence which are used to infer the identity. The temporal, spatial and context features are extracted from the surveillance video as the observations to perceive the privacy demands and their correlations. Taking advantage of quantifying various evidence and utility, we let users subscribe videos with a viewer-dependent pattern. We implement a prototype system for off-line and on-line requirements in two typical monitoring scenarios to construct extensive experiments. The evaluation results show that our system can efficiently satisfy users' privacy demands while saving over 25% more video information compared to traditional video privacy protection schemes.

In part I of a three-part series on active surveillance using depth-sensing technology, this paper proposes an algorithm to identify outdoor intrusion activities by monitoring skeletal positions from Microsoft Kinect sensor in real-time. This algorithm implements three techniques to identify a premise intrusion. The first technique observes a boundary line along the wall (or fence) of a surveilled premise for skeletal trespassing detection. The second technique observes the duration of a skeletal object within a region of a surveilled premise for loitering detection. The third technique analyzes the differences in skeletal height to identify wall climbing. Experiment results suggest that the proposed algorithm is able to detect trespassing, loitering and wall climbing at a rate of 70%, 85% and 80% respectively.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

Multimedia authentication is an integral part of multimedia signal processing in many real-time and security sensitive applications, such as video surveillance. In such applications, a full-fledged video digital rights management (DRM) mechanism is not applicable due to the real time requirement and the difficulties in incorporating complicated license/key management strategies. This paper investigates the potential of multimedia authentication from a brand new angle by employing hardware-based security primitives, such as physical unclonable functions (PUFs). We show that the hardware security approach is not only capable of accomplishing the authentication for both the hardware device and the multimedia stream but, more importantly, introduce minimum performance, resource, and power overhead. We justify our approach using a prototype PUF implementation on Xilinx FPGA boards. Our experimental results on the real hardware demonstrate the high security and low overhead in multimedia authentication obtained by using hardware security approaches.

This paper address the problem of shadow detection and removal in traffic vision analysis. Basically, the presence of the shadow in the traffic sequences is imminent, and therefore leads to errors at segmentation stage and often misclassified as an object region or as a moving object. This paper presents a shadow removal method, based on both color and texture features, aiming to contribute to retrieve efficiently the moving objects whose detection are usually under the influence of cast-shadows. Additionally, in order to get a shadow-free foreground segmentation image, a morphology reconstruction algorithm is used to recover the foreground disturbed by shadow removal. Once shadows are detected, an automatic shadow removal model is proposed based on the information retrieved from the histogram shape. Experimental results on a real traffic sequence is presented to test the proposed approach and to validate the algorithm's performance.

H.264/SVC (Scalable Video Coding) codestreams, which consist of a single base layer and multiple enhancement layers, are designed for quality, spatial, and temporal scalabilities. They can be transmitted over networks of different bandwidths and seamlessly accessed by various terminal devices. With a huge amount of video surveillance and various devices becoming an integral part of the security infrastructure, the industry is currently starting to use the SVC standard to process digital video for surveillance applications such that clients with different network bandwidth connections and display capabilities can seamlessly access various SVC surveillance (sub)codestreams. In order to guarantee the trustworthiness and integrity of received SVC codestreams, engineers and researchers have proposed several authentication schemes to protect video data. However, existing algorithms cannot simultaneously satisfy both efficiency and robustness for SVC surveillance codestreams. Hence, in this article, a highly efficient and robust authentication scheme, named TrustSSV (Trust Scalable Surveillance Video), is proposed. Based on quality/spatial scalable characteristics of SVC codestreams, TrustSSV combines cryptographic and content-based authentication techniques to authenticate the base layer and enhancement layers, respectively. Based on temporal scalable characteristics of surveillance codestreams, TrustSSV extracts, updates, and authenticates foreground features for each access unit dynamically with background model support. Using SVC test sequences, our experimental results indicate that the scheme is able to distinguish between content-preserving and content-changing manipulations and to pinpoint tampered locations. Compared with existing schemes, the proposed scheme incurs very small computation and communication costs.

Video surveillance, closed-circuit TV and IP-camera systems became virtually omnipresent and indispensable for many organizations, businesses, and users. Their main purpose is to provide physical security, increase safety, and prevent crime. They also became increasingly complex, comprising many communication means, embedded hardware and non-trivial firmware. However, most research to date focused mainly on the privacy aspects of such systems, and did not fully address their issues related to cyber-security in general, and visual layer (i.e., imagery semantics) attacks in particular. In this paper, we conduct a systematic review of existing and novel threats in video surveillance, closed-circuit TV and IP-camera systems based on publicly available data. The insights can then be used to better understand and identify the security and the privacy risks associated with the development, deployment and use of these systems. We study existing and novel threats, along with their existing or possible countermeasures, and summarize this knowledge into a comprehensive table that can be used in a practical way as a security checklist when assessing cyber-security level of existing or new CCTV designs and deployments. We also provide a set of recommendations and mitigations that can help improve the security and privacy levels provided by the hardware, the firmware, the network communications and the operation of video surveillance systems. We hope the findings in this paper will provide a valuable knowledge of the threat landscape that such systems are exposed to, as well as promote further research and widen the scope of this field beyond its current boundaries.

With recent advances in consumer electronics and the increasingly urgent need for public security, camera networks have evolved from their early role of providing simple and static monitoring to current complex systems capable of obtaining extensive video information for intelligent processing, such as target localization, identification, and tracking. In all cases, it is of vital importance that the optimal camera configuration (i.e., optimal location, orientation, etc.) is determined before cameras are deployed as a suboptimal placement solution will adversely affect intelligent video surveillance and video analytic algorithms. The optimal configuration may also provide substantial savings on the total number of cameras required to achieve the same level of utility. In this article, we examine most, if not all, of the recent approaches (post 2000) addressing camera placement in a structured manner. We believe that our work can serve as a first point of entry for readers wishing to start researching into this area or engineers who need to design a camera system in practice. To this end, we attempt to provide a complete study of relevant formulation strategies and brief introductions to most commonly used optimization techniques by researchers in this field. We hope our work to be inspirational to spark new ideas in the field.

This paper evaluates a new video surveillance platform presented in a previous study, through an abandoned object detection task. The proposed platform has a function of automated detection and alerting, which is still a big challenge for a machine algorithm due to its recall-precision tradeoff problem. To achieve both high recall and high precision simultaneously, a hybrid approach using crowdsourcing after image analysis is proposed. This approach, however, is still not clear about what extent it can improve detection accuracy and raise quicker alerts. In this paper, the experiment is conducted for abandoned object detection, as one of the most common surveillance tasks. The results show that detection accuracy was improved from 50% (without crowdsourcing) to stable 95-100% (with crowdsourcing) by majority vote of 7 crowdworkers for each task. In contrast, alert time issue still remains open to further discussion since at least 7+ minutes are required to get the best performance.

This paper presents the foundations of secured and trusted architecture for the Internet of Things platforms, based on Secure Elements (SE). Some IoT networks could be managed by service providers, dealing with smart grids or healthcare. Many platforms are using DTLS or TLS protocols. Therefore SEs running such stacks could provide strong mutual authentication and secure communications. Three future research directions are illustrated by previous experiments. TLS/DTLS SE servers for objects, CoAP DTLS clients for SIM modules, and RACS authorization servers based on SE TLS servers.

Cyber-physical system integrity requires both hardware and software security. Many of the cyber attacks are successful as they are designed to selectively target a specific hardware or software component in an embedded system and trigger its failure. Existing security measures also use attack vector models and isolate the malicious component as a counter-measure. Isolated security primitives do not provide the overall trust required in an embedded system. Trust enhancements are proposed to a hardware security platform, where the trust specifications are implemented in both software and hardware. This distribution of trust makes it difficult for a hardware-only or software-only attack to cripple the system. The proposed approach is applied to a smart grid application consisting of third-party soft IP cores, where an attack on this module can result in a blackout. System integrity is preserved in the event of an attack and the anomalous behavior of the IP core is recorded by a supervisory module. The IP core also provides a snapshot of its trust metric, which is logged for further diagnostics.

The incorporation of security mechanisms to protect spacecraft's TT&c; payload links is becoming a constant requirement in many space missions. More advanced mission concepts will allow spacecrafts to have higher levels of autonomy, which includes performing key management operations independently of control centers. This is especially beneficial to support missions operating distantly from Earth. In order to support such levels of autonomy, key agreement is one approach that allows spacecrafts to establish new cryptographic keys as they deem necessary. This work introduces an approach based on a trusted platform module that allows for key agreement to be performed with minimal computational efforts and protocol iterations. Besides, it allows for opportunistic control center reporting while avoiding man-in-the-middle and replay attacks.

Ensuring security in the military applications of IoT is a big challenge. The main reasons for this state of affairs is that the sensor nodes of the network are usually mobile, use wireless links, have a small processing power and have a little energy resources. The paper presents the solution for cryptographic protection of transmission between sensor nodes in the data link layer and for cryptographic protection of data stored in the sensor node resources. For this purpose, the Trusted Platform Module (TPM) was used. The proposed solution makes it possible to build secure and fault tolerant sensor network. The following aspects were presented in the paper: the model of such a network, applied security solutions, analysis of the security in the network and selected investigation results of such a network were presented.

Cloud platforms can leverage Trusted Platform Modules to help provide assurance to clients that cloud-based Web services are trustworthy and behave as expected. We discuss a variety of approaches to providing this assurance, and we implement one approach based on the concept of a trustworthy certificate authority. TaoCA, our prototype implementation, links cryptographic attestations from a cloud platform, including a Trusted Platform Module, with existing TLS-based authentication mechanisms. TaoCA is designed to enable certificate authorities, browser vendors, system administrators, and end users to define and enforce a range of trust policies for web services. Evaluation of the prototype implementation demonstrates the feasibility of the design, illustrates performance tradeoffs, and serves as an end-to-end, proof-of-concept evaluation of underlying trustworthy computing abstractions. The proposed approach can be deployed incrementally and provides new benefits while retaining compatibility with the existing public key infrastructure used for TLS. 

Notions like security, trust, and privacy are crucial in the digital environment and in the future, with the advent of technologies like the Internet of Things (IoT) and Cyber-Physical Systems (CPS), their importance is only going to increase. Trust has different definitions, some situations rely on real-world relationships between entities while others depend on robust technologies to gain trust after deployment. In this paper we focus on these robust technologies, their evolution in past decades and their scope in the near future. The evolution of robust trust technologies has involved diverse approaches, as a consequence trust is defined, understood and ascertained differently across heterogeneous domains and technologies. In this paper we look at digital trust technologies from the point of view of security and examine how they are making secure computing an attainable reality. The paper also revisits and analyses the Trusted Platform Module (TPM), Secure Elements (SE), Hypervisors and Virtualisation, Intel TXT, Trusted Execution Environments (TEE) like GlobalPlatform TEE, Intel SGX, along with Host Card Emulation, and Encrypted Execution Environment (E3). In our analysis we focus on these technologies and their application to the emerging domains of the IoT and CPS.

This paper has presented an approach of vTPM (virtual Trusted Platform Module) Dynamic Trust Extension (DTE) to satisfy the requirements of frequent migrations. With DTE, vTPM is a delegation of the capability of signing attestation data from the underlying pTPM (physical TPM), with one valid time token issued by an Authentication Server (AS). DTE maintains a strong association between vTPM and its underlying pTPM, and has clear distinguishability between vTPM and pTPM because of the different security strength of the two types of TPM. In DTE, there is no need for vTPM to re-acquire Identity Key (IK) certificate(s) after migration, and pTPM can have a trust revocation in real time. Furthermore, DTE can provide forward security. Seen from the performance measurements of its prototype, DTE is feasible.

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel's software guard extensions (SGX) [15]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user's input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, heath analysis, image processing and machine translation.

In the universal Android system, each application runs in its own sandbox, and the permission mechanism is used to enforce access control to the system APIs and applications. However, permission leak could happen when an application without certain permission illegally gain access to protected resources through other privileged applications. In order to address permission leak in a trusted execution environment, this paper designs security architecture which contains sandbox module, middleware module, usage and access control module, and proposes an effective usage and access control scheme that can prevent permission leak in a trusted execution environment. Security architecture based on the scheme has been implemented on an ARM-Android platform, and the evaluation of the proposed scheme demonstrates its effectiveness in mitigating permission leak vulnerabilities.

Cloud computing is a wide-spread technology that enables the enterprises to provide services to their customers with a lower cost, higher performance, better availability and scalability. However, privacy and security in cloud computing has always been a major challenge to service providers and a concern to its users. Trusted computing has led its way in securing the cloud computing and virtualized environment, during the past decades. In this paper, first we study virtualized trusted platform modules and integration of vTPM in hypervisor-based virtualization. Then we propose two architectural solutions for integrating the vTPM in container-based virtualization model.

This paper explores fully integrated inductive voltage regulators (FIVR) as a technique to improve the side channel resistance of encryption engines. We propose security aware design modes for low passive FIVR to improve robustness of an encryption-engine against statistical power attacks in time and frequency domain. A Correlation Power Analysis is used to attack a 128-bit AES engine synthesized in 130nm CMOS. The original design requires \textasciitilde250 Measurements to Disclose (MTD) the 1st byte of key; but with security-aware FIVR, the CPA was unsuccessful even after 20,000 traces. We present a reversibility based threat model for the FIVR-based protection improvement and show the robustness of security aware FIVR against such threat.

Coverage-based Greybox Fuzzing (CGF) is a random testing approach that requires no program analysis. A new test is generated by slightly mutating a seed input. If the test exercises a new and interesting path, it is added to the set of seeds; otherwise, it is discarded. We observe that most tests exercise the same few "high-frequency" paths and develop strategies to explore significantly more paths with the same number of tests by gravitating towards low-frequency paths. We explain the challenges and opportunities of CGF using a Markov chain model which specifies the probability that fuzzing the seed that exercises path i generates an input that exercises path j. Each state (i.e., seed) has an energy that specifies the number of inputs to be generated from that seed. We show that CGF is considerably more efficient if energy is inversely proportional to the density of the stationary distribution and increases monotonically every time that seed is chosen. Energy is controlled with a power schedule. We implemented the exponential schedule by extending AFL. In 24 hours, AFLFAST exposes 3 previously unreported CVEs that are not exposed by AFL and exposes 6 previously unreported CVEs 7x faster than AFL. AFLFAST produces at least an order of magnitude more unique crashes than AFL.

This paper proposes a method of distinguishing stock market states, classifying them based on price variations of securities, and using an evolutionary algorithm for improving the quality of classification. The data represents buy/sell order queues obtained from rebuild order book, given as price-volume pairs. In order to put more emphasis on certain features before the classifier is used, we use a weighting scheme, further optimized by an evolutionary algorithm.

Radio Frequency Identification (RFID) systems are widely used today because of their low price, usability and being wireless. As RFID systems use wireless communication, they may encounter challenging security problems. Several lightweight encryption algorithms have been proposed so far to solve these problems. The RBS block cipher is one of these algorithms. In designing RBS, conventional block cipher elements such as S-box and P-box are not used. RBS is based on inserting redundant bits between altered plaintext bits using an encryption key Kenc. In this paper, considering not having a proper diffusion as the main defect of RBS, we propose a chosen ciphertext attack against this algorithm. The data complexity of this attack equals to N pairs of text and its time complexity equals to N decryptions, where N is the size of the encryption key Kenc.

This paper formulates a power system related optimal control problem, motivated by potential cyber-attacks on grid control systems, and ensuing defensive response to such attacks. The problem is formulated as a standard nonlinear program in the GAMS optimization environment, with system dynamics discretized over a short time horizon providing constraint equations, which are then treated via waveform relaxation. Selection of objective function and additional decision variables is explored first for identifying grid vulnerability to cyber-attacks that act by modifying feedback control system parameters. The resulting decisions for the attacker are then fixed, and the optimization problem is modified with a new objective function and decision variables, to explore a defender's possible response to such attacks.