Biblio

Bluetooth Low Energy (BLE) Beacons introduced a novel technology that enables devices to advertise their presence in an area by constantly broadcasting a static unique identifier. The aim was to enhance services with location and context awareness. Although the hardware components of typical BLE Beacons systems are able to support adequate cryptography, the design and implementation of most publicly available BLE Beacon protocols appears to render them vulnerable to a plethora of attacks. Indeed, in this paper, we were able to perform user tracking, user behavior monitoring, spoofing as well as denial of service (DoS) of many supported services. Our aim is to show that these attacks stem from design flaws of the underlying protocols and assumptions made for the BLE beacons protocols. Using a clearly defined threat model, we provide a formal analysis of the adversarial capabilities and requirements and the attack impact on security and privacy for the end-user. Contrary to popular belief, BLE technology can be exploited even by low-skilled adversaries leading to exposure of user information. To demonstrate our attacks in practice, we selected Apple's iBeacon technology, as a case study. However, our analysis can be easily generalized to other BLE Beacon technologies.

Bluetooth Low Energy device is increasing in popularity due to its lower energy consumption and reliable connectivity compared to the classic Bluetooth. Some of these BLE devices collects and transmits health care data like the heart rate as in a Fitbit smart band. This paper will demonstrate that Bluetooth Low Energy devices that relies on BLE security has weak communication security and how to solve that problem using a private-key encryption algorithm.

At present, Bluetooth Low Energy (BLE) is dominantly used in commercially available Internet of Things (IoT) devices – such as smart watches, fitness trackers, and smart appliances. Compared to classic Bluetooth, BLE has been simplified in many ways that include its connection establishment, data exchange, and encryption processes. Unfortunately, this simplification comes at a cost. For example, only a star topology is supported in BLE environments and a peripheral (an IoT device) can communicate with only one gateway (e.g. a smartphone, or a BLE hub) at a set time. When a peripheral goes out of range, it loses connectivity to a gateway, and cannot connect and seamlessly communicate with another gateway without user interventions. In other words, BLE connections do not get automatically migrated or handed-off to another gateway. In this paper, we propose a system which brings seamless connectivity to BLE-capable mobile IoT devices in an environment that consists of a network of gateways. Our framework ensures that unmodified, commercial off-the-shelf BLE devices seamlessly and securely connect to a nearby gateway without any user intervention.

We all are very much aware of IoT that is Internet of Things which is emerging technology in today's world. The new and advanced field of technology and inventions make use of IoT for better facility. The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Our project is based on IoT and other supporting techniques which can bring out required output. Security issues are everywhere now-a-days which we are trying to deal with by our project. Our security throwbot (a throwable device) will be tossed into a room after activating it and it will capture 360 degree panaromic video from a single IP camera, by using two end connectivity that is, robot end and another is user end, will bring more features to this project. Shape of the robot will be shperical so that problem of retrieving back can be solved. Easy to use and cheap to buy is one of our goal which will be helpful to police and soldiers who get stuck in situations where they have to question oneself before entering to dangerous condition/room. Our project will help them to handle and verify any area before entering by just throwing this robot and getting the sufficient results.

In this paper, we present the design of Intelligent Security Lock prototype which acts as a smart electronic/digital door locking system. The design of lock device and software system including app is discussed. The paper presents idea to control the lock using mobile app via Bluetooth. The lock satisfies comprehensive security requirements using state of the art technologies. It provides strong authentication using face recognition on app. It stores records of all lock/unlock operations with date and time. It also provides intrusion detection notification and real time camera surveillance on app. Hence, the lock is a unique combination of various aforementioned security features providing absolute solution to problem of security.

We present an approach to tracking the behaviour of an attacker on a decoy system, where the decoy communicates with the real system only through low energy bluetooth. The result is a low-cost solution that does not interrupt the live system, while limiting potential damage. The attacker has no way to detect that they are being monitored, while their actions are being logged for further investigation. The system has been physically implemented using Raspberry PI and Arduino boards to replicate practical performance.

With the continuous development of mobile based Wireless technologies, Bluetooth plays a vital role in smart-phone Era. In such scenario, the security measures are needed to be enhanced for Bluetooth. We propose a Node Energy Based Virus Propagation Model (NBV) for Bluetooth. The algorithm works with key features of node capacity and node energy in Bluetooth network. This proposed NBV model works along with E-mail worm Propagation model. Finally, this work simulates and compares the virus propagation with respect to Node Energy and network traffic.

The need for security in today's world has become a mandatory issue to look after. With the increase in a number of thefts, it has become a necessity to implement a smart security system. Due to the high cost of the existing smart security systems which use conventional Bluetooth and other wireless technologies and their relatively high energy consumption, implementing a security system with low energy consumption at a low cost has become the need of the hour. The objective of the paper is to build a cost effective and low energy consumption security system using the Bluetooth Low Energy (BLE) technology. This system will help the user to monitor and manage the security of the house even when the user is outside the house with the help of webpage. This paper presents the design and implementation of a security system using PSoC 4 BLE which can automatically lock and unlock the door when the user in the vicinity and leaving the vicinity of the door respectively by establishing a wireless connection between the physical lock and the smartphone. The system also captures an image of a person arriving at the house and transmits it wirelessly to a webpage. The system also notifies the user of any intrusion by sending a message and the image of the intruder to the webpage. The user can also access the door remotely on the go from the website.

The detection of cyber-attacks has become a crucial task for highly sophisticated systems like industrial control systems (ICS). These systems are an essential part of critical information infrastructure. Therefore, we can highlight their vital role in contemporary society. The effective and reliable ICS cyber defense is a significant challenge for the cyber security community. Thus, intrusion detection is one of the demanding tasks for the cyber security researchers. In this article, we examine classification problem. The proposed detection system is based on supervised anomaly detection techniques. Moreover, we utilized classifiers algorithms in order to increase intrusion detection capabilities. The fusion of the classifiers is the way how to achieve the predefined goal.

Modbus over TCP/IP is one of the most popular industrial network protocol that are widely used in critical infrastructures. However, vulnerability of Modbus TCP protocol has attracted widely concern in the public. The traditional intrusion detection methods can identify some intrusion behaviors, but there are still some problems. In this paper, we present an innovative approach, SD-IDS (Stereo Depth IDS), which is designed for perform real-time deep inspection for Modbus TCP traffic. SD-IDS algorithm is composed of two parts: rule extraction and deep inspection. The rule extraction module not only analyzes the characteristics of industrial traffic, but also explores the semantic relationship among the key field in the Modbus TCP protocol. The deep inspection module is based on rule-based anomaly intrusion detection. Furthermore, we use the online test to evaluate the performance of our SD-IDS system. Our approach get a low rate of false positive and false negative.

Industrial control systems (ICS) used in industrial plants are vulnerable to cyber-attacks that can cause fatal damage to the plants. Intrusion detection systems (IDSs) monitor ICS network traffic and detect suspicious activities. However, many IDSs overlook sophisticated cyber-attacks because it is hard to make a complete database of cyber-attacks and distinguish operational anomalies when compared to an established baseline. In this paper, a discriminant model between normal and anomalous packets was constructed with a support vector machine (SVM) based on an ICS communication profile, which represents only packet intervals and length, and an IDS with the applied model is proposed. Furthermore, the proposed IDS was evaluated using penetration tests on our cyber security test bed. Although the IDS was constructed by the limited features (intervals and length) of packets, the IDS successfully detected cyber-attacks by monitoring the rate of predicted attacking packets.

We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques.

Microservice architectures are steadily gaining adoption in industrial practice. At the same time, performance and resilience are important properties that need to be ensured. Even though approaches for performance and resilience have been developed (e.g., for anomaly detection and fault tolerance), there are no benchmarking environments for their evaluation under controlled conditions. In this paper, we propose a generative platform for benchmarking performance and resilience engineering approaches in microservice architectures, comprising an underlying metamodel, a generation platform, and supporting services for workload generation, problem injection, and monitoring.

The Modbus/TCP protocol is commonly used in the industrial control systems for communications between the human-machine interface and the industrial controllers. This paper proposes a real-time intrusion detection method based on bidirectional access of the Modbus/TCP protocol. The method doesnt require key observation that Modbus/TCP traffic to and from master device or slave device is periodic. Anomaly detection can be realized in time by the method after checking only two packets. And even though invader modifies the legal function code to another legal one in the packet from master device to slave device, the method can also figure it out. The test results show that the presented method has traits of timeliness, low false positive rate and low false negative rate.

Traffic of Industrial Control System (ICS) between the Human Machine Interface (HMI) and the Programmable Logic Controller (PLC) is known to be highly periodic. However, it is sometimes multiplexed, due to asynchronous scheduling. Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata (DFA) for anomaly detection typically produces a very large DFA and a high false-alarm rate. In this article, we introduce a new modeling approach that addresses this gap. Our Statechart DFA modeling includes multiple DFAs, one per cyclic pattern, together with a DFA-selector that de-multiplexes the incoming traffic into sub-channels and sends them to their respective DFAs. We demonstrate how to automatically construct the statechart from a captured traffic stream. Our unsupervised learning algorithms first build a Discrete-Time Markov Chain (DTMC) from the stream. Next, we split the symbols into sets, one per multiplexed cycle, based on symbol frequencies and node degrees in the DTMC graph. Then, we create a sub-graph for each cycle and extract Euler cycles for each sub-graph. The final statechart is comprised of one DFA per Euler cycle. The algorithms allow for non-unique symbols, which appear in more than one cycle, and also for symbols that appear more than once in a cycle. We evaluated our solution on traces from a production ICS using the Siemens S7-0x72 protocol. We also stress-tested our algorithms on a collection of synthetically-generated traces that simulated multiplexed ICS traces with varying levels of symbol uniqueness and time overlap. The algorithms were able to split the symbols into sets with 99.6% accuracy. The resulting statechart modeled the traces with a median false-alarm rate of as low as 0.483%. In all but the most extreme scenarios, the Statechart model drastically reduced both the false-alarm rate and the learned model size in comparison with the naive single-DFA model.

Cyber-physical systems are found in industrial and production systems, as well as critical infrastructures. Due to the increasing integration of IP-based technology and standard computing devices, the threat of cyber-attacks on cyber-physical systems has vastly increased. Furthermore, traditional intrusion defense strategies for IT systems are often not applicable in operational environments. In this paper we present an anomaly-based approach for detection and classification of attacks in cyber-physical systems. To test our approach, we set up a test environment with sensors, actuators and controllers widely used in industry, thus, providing system data as close as possible to reality. First, anomaly detection is used to define a model of normal system behavior by calculating outlier scores from normal system operations. This valid behavior model is then compared with new data in order to detect anomalies. Further, we trained an attack model, based on supervised attacks against the test setup, using the naive Bayes classifier. If an anomaly is detected, the classification process tries to classify the anomaly by applying the attack model and calculating prediction confidences for trained classes. To evaluate the statistical performance of our approach, we tested the model by applying an unlabeled dataset, which contains valid and anomalous data. The results show that this approach was able to detect and classify such attacks with satisfactory accuracy.

Industrial control systems have stringent safety and security demands. High safety assurance can be obtained by specifying the system with possible faults and monitoring it to ensure these faults are properly addressed. Addressing security requires considering unpredictable attacker behavior. Anomaly detection, with its data driven approach, can detect simple unusual behavior and system-based attacks like the propagation of malware; on the other hand, anomaly detection is less suitable to detect more complex \textbackslashtextbackslashemph\process-based\ attacks and it provides little actionability in presence of an alert. The alternative to anomaly detection is to use specification-based intrusion detection, which is more suitable to detect process-based attacks, but is typically expensive to set up and less scalable. We propose to combine a lightweight formal system specification with anomaly detection, providing data-driven monitoring. The combination is based on mapping elements of the specification to elements of the network traffic. This allows extracting locations to monitor and relevant context information from the formal specification, thus semantically enriching the raised alerts and making them actionable. On the other hand, it also allows under-specification of data-based properties in the formal model; some predicates can be left uninterpreted and the monitoring can be used to learn a model for them. We demonstrate our methodology on a smart manufacturing use case.

Many of the emerging wide-area monitoring protection and control (WAMPAC) applications in modern electrical grids rely heavily on the availability and integrity of widespread phasor measurement unit (PMU) data. Therefore, it is critical to protect PMU networks against growing cyber-attacks and system faults. In this paper, we present a self-healing PMU network design that considers both power system observability and communication network characteristics. Our design utilizes centralized network control, such as the emerging software-defined networking (SDN) technology, to design resilient network self-healing algorithms against cyber-attacks. Upon detection of a cyber-attack, the PMU network can reconfigure itself to isolate compromised devices and re-route measurement
data with the goal of preserving the power system observability. We have developed a proof-of-concept system in a container-based network testbed using integer linear programming to solve a graphbased PMU system model.We also evaluate the system performance regarding the self-healing plan generation and installation using the IEEE 30-bus system.
 

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). In such cases, learning algorithms may have to cope with manipulated data aimed at hampering decision making. Although some previous work addressed the issue of handling malicious data in the context of supervised learning, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method–online centroid anomaly detection–in the presence of adversarial noise. Our analysis addresses the following security-related issues: formalization of learning and attack processes, derivation of an optimal attack, and analysis of attack efficiency and limitations. We derive bounds on the effectiveness of a poisoning attack against centroid anomaly detection under different conditions: attacker's full or limited control over the traffic and bounded false positive rate. Our bounds show that whereas a poisoning attack can be effectively staged in the unconstrained case, it can be made arbitrarily difficult (a strict upper bound on the attacker's gain) if external constraints are properly used. Our experimental evaluation, carried out on real traces of HTTP and exploit traffic, confirms the tightness of our theoretical bounds and the practicality of our protection mechanisms.

The use of machine learning models has become ubiquitous. Their predictions are used to make decisions about healthcare, security, investments and many other critical applications. Given this pervasiveness, it is not surprising that adversaries have an incentive to manipulate machine learning models to their advantage. One way of manipulating a model is through a poisoning or causative attack in which the adversary feeds carefully crafted poisonous data points into the training set. Taking advantage of recently developed tamper-free provenance frameworks, we present a methodology that uses contextual information about the origin and transformation of data points in the training set to identify poisonous data, thereby enabling online and regularly re-trained machine learning applications to consume data sources in potentially adversarial environments. To the best of our knowledge, this is the first approach to incorporate provenance information as part of a filtering algorithm to detect causative attacks. We present two variations of the methodology - one tailored to partially trusted data sets and the other to fully untrusted data sets. Finally, we evaluate our methodology against existing methods to detect poison data and show an improvement in the detection rate.

The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensionality reduction to improve performance and identify the most important factors in predicting outcomes. However, the economic importance of learning has made it a natural target for adversarial manipulation of training data, which we term poisoning attacks. Prior approaches to dealing with robust supervised learning rely on strong assumptions about the nature of the feature matrix, such as feature independence and sub-Gaussian noise with low variance. We propose an integrated method for robust regression that relaxes these assumptions, assuming only that the feature matrix can be well approximated by a low-rank matrix. Our techniques integrate improved robust low-rank matrix approximation and robust principle component regression, and yield strong performance guarantees. Moreover, we experimentally show that our methods significantly outperform state of the art both in running time and prediction error.

Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems. However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data. In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior. To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.

Deep Neural Network (DNN) has recently become the “de facto” technique to drive the artificial intelligence (AI) industry. However, there also emerges many security issues as the DNN based intelligent systems are being increasingly prevalent. Existing DNN security studies, such as adversarial attacks and poisoning attacks, are usually narrowly conducted at the software algorithm level, with the misclassification as their primary goal. The more realistic system-level attacks introduced by the emerging intelligent service supply chain, e.g. the third-party cloud based machine learning as a service (MLaaS) along with the portable DNN computing engine, have never been discussed. In this work, we propose a low-cost modular methodology-Stealth Infection on Neural Network, namely “SIN2”, to demonstrate the novel and practical intelligent supply chain triggered neural Trojan attacks. Our “SIN2” well leverages the attacking opportunities built upon the static neural network model and the underlying dynamic runtime system of neural computing framework through a bunch of neural Trojaning techniques. We implement a variety of neural Trojan attacks in Linux sandbox by following proposed “SIN2”. Experimental results show that our modular design can rapidly produce and trigger various Trojan attacks that can easily evade the existing defenses.

Poison message failure is a mechanism that has been responsible for large scale failures in both telecommunications and IP networks. The poison message failure can propagate in the network and cause an unstable network. We apply a machine learning, data mining technique in the network fault management area. We use the k-nearest neighbor method to identity the poison message failure. We also propose a "probabilistic" k-nearest neighbor method which outputs a probability distribution about the poison message. Through extensive simulations, we show that the k-nearest neighbor method is very effective in identifying the responsible message type.

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.