Biblio

Mobile ad hoc network (MANET) is a self-created and self organized network of wireless mobile nodes. Due to special characteristics of these networks, security issue is a difficult task to achieve. Hence, applying current intrusion detection techniques developed for fixed networks is not sufficient for MANETs. In this paper, we proposed an approach based on genetic algorithm (GA) and artificial immune system (AIS), called GAAIS, for dynamic intrusion detection in AODV-based MANETs. GAAIS is able to adapting itself to network topology changes using two updating methods: partial and total. Each normal feature vector extracted from network traffic is represented by a hypersphere with fix radius. A set of spherical detector is generated using NicheMGA algorithm for covering the nonself space. Spherical detectors are used for detecting anomaly in network traffic. The performance of GAAIS is evaluated for detecting several types of routing attacks simulated using the NS2 simulator, such as Flooding, Blackhole, Neighbor, Rushing, and Wormhole. Experimental results show that GAAIS is more efficient in comparison with similar approaches.

Many common cyberdefenses (like firewalls and intrusion-detection systems) are static, giving attackers the freedom to probe them at will. Moving-target defense (MTD) adds dynamism, putting the systems to be defended in motion, potentially at great cost to the defender. An alternative approach is a mobile resilient defense that removes attackers' ability to rely on prior experience without requiring motion in the protected infrastructure. The defensive technology absorbs most of the cost of motion, is resilient to attack, and is unpredictable to attackers. The authors' mobile resilient defense, Ant-Based Cyber Defense (ABCD), is a set of roaming, bio-inspired, digital-ant agents working with stationary agents in a hierarchy headed by a human supervisor. ABCD provides a resilient, extensible, and flexible defense that can scale to large, multi-enterprise infrastructures such as the smart electric grid.

Multiple Inductive Loop Detectors are advanced Inductive Loop Sensors that can measure traffic flow parameters in even conditions where the traffic is heterogeneous and does not conform to lanes. This sensor consists of many inductive loops in series, with each loop having a parallel capacitor across it. These inductive and capacitive elements of the sensor may undergo open or short circuit faults during operation. Such faults lead to erroneous interpretation of data acquired from the loops. Conventional methods used for fault diagnosis in inductive loop detectors consume time and effort as they require experienced technicians and involve extraction of loops from the saw-cut slots on the road. This also means that the traffic flow parameters cannot be measured until the sensor system becomes functional again. The repair activities would also disturb traffic flow. This paper presents a method for automating fault diagnosis for series-connected Multiple Inductive Loop Detectors, based on an impulse test. The system helps in the diagnosis of open/short faults associated with the inductive and capacitive elements of the sensor structure by displaying the fault status conveniently. Since the fault location as well as the fault type can be precisely identified using this method, the repair actions are also localised. The proposed system thereby results in significant savings in both repair time and repair costs. An embedded system was developed to realize this scheme and the same was tested on a loop prototype.
 

Intrusion response is a new generation of technology basing on active defence idea, which has very prominent significance on the protection of network security. However, the existing automatic intrusion response systems are difficult to judge the real "danger" of invasion or attack. In this study, an immune-inspired adaptive automated intrusion response system model, named as AIAIM, was given. With the descriptions of self, non-self, memory detector, mature detector and immature detector of the network transactions, the real-time network danger evaluation equations of host and network are built up. Then, the automated response polices are taken or adjusted according to the real-time danger and attack intensity, which not only solve the problem that the current automated response system models could not detect the true intrusions or attack actions, but also greatly reduce the response times and response costs. Theory analysis and experimental results prove that AIAIM provides a positive and active network security method, which will help to overcome the limitations of traditional passive network security system.
 

The statistical fingerprints left by median filtering can be a valuable clue for image forensics. However, these fingerprints may be maliciously erased by a forger. Recently, a tricky anti-forensic method has been proposed to remove median filtering traces by restoring images' pixel difference distribution. In this paper, we analyze the traces of this anti-forensic technique and propose a novel counter method. The experimental results show that our method could reveal this anti-forensics effectively at low computation load. According to our best knowledge, it's the first work on countering anti-forensics of median filtering.

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

We propose a novel phishing detection architecture based on transparent virtualization technologies and isolation of the own components. The architecture can be deployed as a security extension for virtual machines (VMs) running in the cloud. It uses fine-grained VM introspection (VMI) to extract, filter and scale a color-based fingerprint of web pages which are processed by a browser from the VM's memory. By analyzing the human perceptual similarity between the fingerprints, the architecture can reveal and mitigate phishing attacks which are based on redirection to spoofed web pages and it can also detect “Man-in-the-Browser” (MitB) attacks. To the best of our knowledge, the architecture is the first anti-phishing solution leveraging virtualization technologies. We explain details about the design and the implementation and we show results of an evaluation with real-world data.

The popularity of mobile devices and the enormous number of third party mobile applications in the market have naturally lead to several vulnerabilities being identified and abused. This is coupled with the immaturity of intrusion detection system (IDS) technology targeting mobile devices. In this paper we propose a modular host-based IDS framework for mobile devices that uses behavior analysis to profile applications on the Android platform. Anomaly detection can then be used to categorize malicious behavior and alert users. The proposed system accommodates different detection algorithms, and is being tested at a major telecom operator in North America. This paper highlights the architecture, findings, and lessons learned.

Dual Energy CT (DECT) has recently gained significant research interest owing to its ability to discriminate materials, and hence is widely applied in the field of nuclear safety and security inspection. With the current technological developments, DECT can be typically realized by using two sets of detectors, one for detecting lower energy X-rays and another for detecting higher energy X-rays. This makes the imaging system expensive, limiting its practical implementation. In 2009, our group performed a preliminary study on a new low-cost system design, using only a complete data set for lower energy level and a sparse data set for the higher energy level. This could significantly reduce the cost of the system, as it contained much smaller number of detector elements. Reconstruction method is the key point of this system. In the present study, we further validated this system and proposed a robust method, involving three main steps: (1) estimation of the missing data iteratively with TV constraints; (2) use the reconstruction from the complete lower energy CT data set to form an initial estimation of the projection data for higher energy level; (3) use ordered views to accelerate the computation. Numerical simulations with different number of detector elements have also been examined. The results obtained in this study demonstrate that 1 + 14% CT data is sufficient enough to provide a rather good reconstruction of both the effective atomic number and electron density distributions of the scanned object, instead of 2 sets CT data.

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the χ2-detector or the proposed Euclidean detector. The χ2-detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the χ2-detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.

The security of Smart Grid, being one of the very important aspects of the Smart Grid system, is studied in this paper. We first discuss different pitfalls in the security of the Smart Grid system considering the communication infrastructure among the sensors, actuators, and control systems. Following that, we derive a mathematical model of the system and propose a robust security framework for power grid. To effectively estimate the variables of a wide range of state processes in the model, we adopt Kalman Filter in the framework. The Kalman Filter estimates and system readings are then fed into the χ2-square detectors and the proposed Euclidean detectors, which can detect various attacks and faults in the power system including False Data Injection Attacks. The χ2-detector is a proven-effective exploratory method used with Kalman Filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks such as replay and DoS attacks. However, the study shows that the χ2-detector detectors are unable to detect statistically derived False Data Injection Attacks while the Euclidean distance metrics can identify such sophisticated injection attacks.