Biblio

Publicly available blacklists are popular tools to capture and spread information about misbehaving entities on the Internet. In some cases, their straight-forward utilization leads to many false positives. In this work, we propose a system that combines blacklists with network flow data while introducing automated evaluation techniques to avoid reporting unreliable alerts. The core of the system is formed by an Adaptive Filter together with an Evaluator module. The assessment of the system was performed on data obtained from a national backbone network. The results show the contribution of such a system to the reduction of unreliable alerts.

The detection of malicious HTTP(S) requests is a pressing concern in cyber security, in particular given the proliferation of HTTP-based (micro-)service architectures. In addition to rule-based systems for known attacks, anomaly detection has been shown to be a promising approach for unknown (zero-day) attacks. This article extends existing work by integrating outlier explanations for individual requests into an end-to-end pipeline. These end-to-end explanations reflect the internal working of the pipeline. Empirically, we show that found explanations coincide with manually labelled explanations for identified outliers, allowing security professionals to quickly identify and understand malicious requests.

Lightweight cryptography concept has been a very hot topic for the last few years and considered as a new domain of encryption suitable for big data networks, small devices, phones, cards and embedded systems. These systems require low latency security and low power consuming [1]. An improved lightweight encryption algorithm ILEA is proposed in this paper. ILEA is based on PRINCE lightweight algorithm as his main core with two defacing balanced mixing layers added. ILEA presented in two programming languages: PYTHON, C++ with a comparative study with original PRINCE results and some of another lightweight algorithms.

Cybercrime is growing dramatically in the technological world nowadays. World Wide Web criminals exploit the personal information of internet users and use them to their advantage. Unethical users leverage the dark web to buy and sell illegal products or services and sometimes they manage to gain access to classified government information. A number of illegal activities that can be found in the dark web include selling or buying hacking tools, stolen data, digital fraud, terrorists activities, drugs, weapons, and more. The aim of this project is to collect evidence of any malicious activity in the dark web by using computer security mechanisms as traps called honeypots.

Nowadays, the emerging Internet-of-Things (IoT) emphasize the need for the security of network-connected devices. Additionally, there are two types of services in IoT devices that are easily exploited by attackers, weak authentication services (e.g., SSH/Telnet) and exploited services using command injection. Based on this observation, we propose IoTCMal, a hybrid IoT honeypot framework for capturing more comprehensive malicious samples aiming at IoT devices. The key novelty of IoTC-MAL is three-fold: (i) it provides a high-interactive component with common vulnerable service in real IoT device by utilizing traffic forwarding technique; (ii) it also contains a low-interactive component with Telnet/SSH service by running in virtual environment. (iii) Distinct from traditional low-interactive IoT honeypots[1], which only analyze family categories of malicious samples, IoTCMal primarily focuses on homology analysis of malicious samples. We deployed IoTCMal on 36 VPS1 instances distributed in 13 cities of 6 countries. By analyzing the malware binaries captured from IoTCMal, we discover 8 malware families controlled by at least 11 groups of attackers, which mainly launched DDoS attacks and digital currency mining. Among them, about 60% of the captured malicious samples ran in ARM or MIPs architectures, which are widely used in IoT devices.

Return-oriented programming (ROP)is a technique used to break data execution protection(DEP). Existing ROP chain automatic construction technology cannot effectively use program controllable memory area. In order to improve the utilization of memory space, this paper proposes a method of ROP chain fragmentation layout. By searching the controllable memory area of the program, a set of layoutable space is formed, and the overall ROP chain is segmented to add jump instructions at the end of each segment, thereby achieving a fragmented layout of the ROP chain. The prototype system ROP-chip based on S2E proved the effectiveness of the fragmented layout of the ROP chain.

Language-based information flow control (IFC) aims to provide guarantees about information propagation in computer systems having multiple security levels. Existing IFC systems extend the lattice model of Denning's, enforcing transitive security policies by tracking information flows along with a partially ordered set of security levels. They yield a transitive noninterference property of either confidentiality or integrity. In this paper, we explore IFC for security policies that are not necessarily transitive. Such nontransitive security policies avoid unwanted or unexpected information flows implied by transitive policies and naturally accommodate high-level coarse-grained security requirements in modern component-based software. We present a novel security type system for enforcing nontransitive security policies. Unlike traditional security type systems that verify information propagation by subtyping security levels of a transitive policy, our type system relaxes strong transitivity by inferring information flow history through security levels and ensuring that they respect the nontransitive policy in effect. Such a type system yields a new nontransitive noninterference property that offers more flexible information flow relations induced by security policies that do not have to be transitive, therefore generalizing the conventional transitive noninterference. This enables us to directly reason about the extent of information flows in the program and restrict interactions between security-sensitive and untrusted components.

A quantum high secure direct communication with authentication protocol is proposed by using single photons. The high security of the protocol is achieved on levels. The first level involves the verification of the quantum channel security by using fake photons. The authentication process is also ensured by the fake photons. The second level of security is given by the use of multiple polarization bases. The secret message is encoded in groups of photons; each single character of the message is associated with m (m≥7) photons. Thus, at least 27 (128) characters will be encoded. In order to defeat the quantum teleportation attack, the string of bits associated to the secret message is encrypted with a secret string of bits by using XOR operator. Encryption of the sender's identity string and the receiver's identity string by the XOR operator with a random string of fake photons defends quantum man-in-the-middle attack efficiently. Quantum memory is required to implement our protocol. Storage of quantum information is a key element in quantum information processing and provides a more flexible, effective and efficient communication. Our protocol is feasible with current technologies.

The construction of power Internet of things is an important development direction of power grid enterprises in the future. Big data not only brings economic and social benefits to the power system industry, but also brings many information security problems. Therefore, in the case of accelerating the construction of ubiquitous electric Internet of things, it is urgent to standardize the data security protection in the ubiquitous electric Internet of things environment. By analyzing the characteristics of big data in power system, this paper discusses the security risks faced by big data in power system. Finally, we propose some methods of data security protection based on the defects of big data security in current power system. By building a data security intelligent management and control platform, it can automatically discover and identify the types and levels of data assets, and build a classification and grading information base of dynamic data assets. And through the detection and identification of data labels and data content characteristics, tracking the use of data flow process. So as to realize the monitoring of data security state. By protecting sensitive data against leakage based on the whole life cycle of data, the big data security of power grid informatization can be effectively guaranteed and the safety immunity of power information system can be improved.

Passive radio frequency identification (RFID) systems raise new transmission secrecy protection challenges against the special proactive eavesdropper, since it is able to both enhance the information wiretap and interfere with the information detection at the RFID reader simultaneously by broadcasting its own continuous wave (CW) signal. To defend against proactive eavesdropping attacks, we propose an artificial noise (AN) aided secure transmission scheme for the RFID reader, which superimposes an AN signal on the CW signal to confuse the proactive eavesdropper. The power allocation between the AN signal and the CW signal are optimized to maximize the secrecy rate. Furthermore, we model the attack and defense process between the proactive eavesdropper and the RFID reader as a hierarchical security game, and prove it can achieve the equilibrium. Simulation results show the superiority of our proposed scheme in terms of the secrecy rate and the interactions between the RFID reader and the proactive eavesdropper.

The increasing vehicles have brought convenience to people as well as many traffic problems. The Internet of Vehicles (IoV) is an extension of the intelligent transportation system based on the Internet of Things (IoT), which is the omnibearing network connection among “Vehicles, Loads, Clouds”. However, IoV also faces threats from various known and unknown security vulnerabilities. Traditional security defense methods can only deal with known attacks, while there is no effective way to deal with unknown attacks. In this paper, we show an IoV system deployed on a Mimic Common Operating Environment (MCOE). At the sensing layer, we introduce a lightweight cryptographic algorithm, LBlock, to encrypt the data collected by the hardware. Thus, we can prevent malicious tampering of information such as vehicle conditions. At the application layer, we firstly put the IoV system platform into MCOE to make it dynamic, heterogeneous and redundant. Extensive experiments prove that the sensing layer can encrypt data reliably and energy-efficiently. And we prove the feasibility and security of the Internet of Vehicles system platform on MCOE.

In order to solve the statistical fluctuation problem caused by the finite data length in the practical quantum key distribution system, four commonly used statistical methods, DeMoivre-Laplace theorem, Chebyshev inequality, Chernoff boundary and Hoeffding boundary, are used to analyze. The application conditions of each method are discussed, and the effects of data length and confidence level on quantum key distribution security performance are simulated and analyzed. The simulation results show that the applicable conditions of Chernoff boundary are most consistent with the reality of the practical quantum key distribution system with finite-length data. Under the same experimental conditions, the secure key generation rate and secure transmission distance obtained by Chernoff boundary are better than those of the other three methods. When the data length and confidence level change, the stability of the security performance obtained by the Chernoff boundary is the best.

The algorithm of causal anomaly detection in industrial control physics is proposed to determine the normal cloud line of industrial control system so as to accurately detect the anomaly. In this paper, The causal modeling algorithm combining Maximum Information Coefficient and Transfer Entropy was used to construct the causal network among nodes in the system. Then, the abnormal nodes and the propagation path of the anomaly are deduced from the structural changes of the causal network before and after the attack. Finally, an anomaly detection algorithm based on hybrid differential cumulative is used to identify the specific anomaly data in the anomaly node. The stability of causality mining algorithm and the validity of locating causality anomalies are verified by using the data of classical chemical process. Experimental results show that the anomaly detection algorithm is better than the comparison algorithm in accuracy, false negative rate and recall rate, and the anomaly location strategy makes the anomaly source traceable.

True Random number Generator (TRNG) is critical for secure communications. In this work, we explain in details regarding our recent solution on TRNG using random telegraph noise (RTN) including the benefits and the disadvantages. Security check is performed using the NIST randomness tests for both the RTN-based TRNG and various conventional pseudo random umber generator. The newly-proposed design shows excellent randomness, power consumption, low design complexity, small area and high speed, making it a suitable candidate for future cryptographically secured applications within the internet of things.

Internet of Things (IoT) have offered great opportunities for the growth of smart objects in the last decade. Smart devices are deployed in many fields such as smart cities, healthcare and agriculture. One of the applications of IoT is Wireless Sensor Networks (WSN) that require inexpensive and space-economic design for remote sensing and communication capabilities. This, unfortunately, lead to their inherent security vulnerabilities. Lightweight cryptography schemes are designed to counter many attacks in low-powered devices such as the IoT and WSN. These schemes can provide support for data encryption and key management while maintaining some level of efficiency. Most of these block ciphers provide good security. However, due to the complex cryptographic scheme's efficiency and optimisation is an issue. In this work, we focus on a new lightweight encryption scheme called the Klein block cipher. The algorithms of Klein block cipher are analysed for performance and security optimisations. A new algorithm which consists of 3-layer substitute box is proposed to reduce the need for resource consumption but maintain the security.

In order to improve the concealment of image steganography, a new method is proposed. The algorithm firstly adopted GM (1, 1) model to detect texture and edge points of carrier image, then embedded secret information in them. GM (1, 1) model of optimized parameters can make full use of pixels information. These pixels are the nearest to the detected point, so it improves the detection accuracy. The method is a kind of steganography based on human visual system. By testing the stegano images with different embedding capacities, the result indicates concealment and image quality of the proposed algorithm are better than BPCS (Bit-plane Complexity Segmentation) and PVD (Pixel-value Differencing), which are also based on visual characteristics.

Ciphertext policy Attribute-based encryption (CPABE) plays an increasingly important role in the field of fine-grained access control for cloud storage. However, The exiting solution can not balance the issue of user identity tracking and user revocation. In this paper, we propose a CP-ABE scheme that supports association revocation and traceability. This scheme uses identity directory technology to realize single user revocation and associated user revocation, and the ciphertext re-encryption technology guarantees the forward security of revocation without updating the private key. In addition, we can accurately trace the identity of the user according to the decryption private key and effectively solve the problem of key abuse. This scheme is proved to be safe and traceable under the standard model, and can effectively control the computational and storage costs while maintaining functional advantages. It is suitable for the practical scenarios of tracking audit and user revocation.

The paper discusses the use of virtual (VR) and augmented (AR) reality for visual analytics in information security. Paper answers two questions: “In which areas of information security visualization VR/AR can be useful?” and “What is the difference of the VR/AR from similar methods of visualization at the level of perception of information?”. The first answer is based on the investigation of information security areas and visualization models that can be used in VR/AR security visualization. The second answer is based on experiments that evaluate perception of visual components in VR.

The wireless technology has knocked the door of tremendous usage and popularity in the last few years along with a high growth rate for new applications in the networking domain. Mobile Ad hoc Networks (MANETs) is solitary most appealing, alluring and challenging field where in the participating nodes do not require any active, existing and centralized system or rigid infrastructure for execution purpose and thus nodes have the moving capability on arbitrary basis. Radio range nodes directly communicate with each other through the wireless links whereas outside range nodes uses relay principle for communication. Though it is a rigid infrastructure less environment and has high growth rate but security is a major concern and becomes vital part of providing hostile free environment for communication. The MANET imposes several prominent challenges such as limited energy reserve, resource constraints, highly dynamic topology, sharing of wireless medium, energy inefficiency, recharging of the batteries etc. These challenges bound to make MANET more susceptible, more close to attacks and weak unlike the wired line networks. Theresearch paperismainly focused on two aspects, one is computation termination of cluster head algorithm and another is use of finite state machine for attacks identification.

The goal of this project is to create a realistic VR experience of solitary confinement and study its impact on users. Although there have been active debates and studies on this subject, very few people have personal experience of solitary confinement. Our first aim is to create such an experience in VR to raise the awareness of solitary confinement. We also want to conduct user studies to compare the VR solitary confinement experience with other types of media experiences, such as films or personal narrations. Finally, we want to study people’s sense of time in such a VR environment.

The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.

Current physical layer authentication (PLA) mechanisms are mostly designed for static communications, and the accuracy degrades significantly when used in dynamic scenarios, where the network environments and wireless channels change frequently. To improve the authentication performance, it is necessary to update the hypothesis test models and parameters in time, which however brings high computational complexity and authentication delay. In this paper, we propose a lightweight cross-layer authentication scheme for dynamic communication scenarios. We use multiple characteristics based PLA to guarantee the reliability and accuracy of authentication, and propose an upper layer assisted method to ensure the performance stability. Specifically, upper layer authentication (ULA) helps to update the PLA models and parameters. By properly choosing the period of triggering ULA, a balance between complexity and performance can be easily obtained. Simulation results show that our scheme can achieve pretty good authentication performance with reduced complexity.

Data races occur when two threads fail to use proper synchronization when accessing shared data. In kernel file systems, which are highly concurrent by design, data races are common mistakes and often wreak havoc on the users, causing inconsistent states or data losses. Prior fuzzing practices on file systems have been effective in uncovering hundreds of bugs, but they mostly focus on the sequential aspect of file system execution and do not comprehensively explore the concurrency dimension and hence, forgo the opportunity to catch data races.In this paper, we bring coverage-guided fuzzing to the concurrency dimension with three new constructs: 1) a new coverage tracking metric, alias coverage, specially designed to capture the exploration progress in the concurrency dimension; 2) an evolution algorithm for generating, mutating, and merging multi-threaded syscall sequences as inputs for concurrency fuzzing; and 3) a comprehensive lockset and happens-before modeling for kernel synchronization primitives for precise data race detection. These components are integrated into Krace, an end-to-end fuzzing framework that has discovered 23 data races in ext4, btrfs, and the VFS layer so far, and 9 are confirmed to be harmful.

Supply chain security threats pose new challenges to security risk modeling techniques for complex ICT systems such as the IoT. With established techniques drawn from attack trees and reliability analysis providing needed points of reference, graph-based analysis can provide a framework for considering the role of suppliers in such systems. We present such a framework here while highlighting the need for a component-centered model. Given resource limitations when applying this model to existing systems, we study various classes of uncertainties in model development, including structural uncertainties and uncertainties in the magnitude of estimated event probabilities. Using case studies, we find that structural uncertainties constitute a greater challenge to model utility and as such should receive particular attention. Best practices in the face of these uncertainties are proposed.

Compressive sensing (CS) is regarded as one of the promising solutions for IoT data encryption as it achieves simultaneous sampling, compression, and encryption. Theoretical work in the literature has proved that CS provides computational secrecy. It also provides asymptotic perfect secrecy for Gaussian sensing matrix with constraints on input signal. In this paper, we design an attack decoding algorithm based on block compressed sensing decoding algorithm to perform ciphertext-only attack on real-life time series IoT data. It shows that it is possible to retrieve vital information in the plaintext under some conditions. Furthermore, it is also applied to a State-of-the Art CS-based encryption scheme for smart grid, and the power profile is reconstructed using ciphertext-only attack. Additionally, the statistical analysis of Gaussian and Binomial measurements is conducted to investigate the randomness provided by them.