Heraclitus : a LFSR-based stream cipher with key dependent structure

We describe Heraclitus as an example of a stream cipher that uses a 128 bit index string to specify the structure of each instance in real time: each instance of Heraclitus will be a stream cipher based on mutually clocked shift registers. Ciphers with key-dependent structures have been investigated and are generally based on Feistel networks. Heraclitus, however, is based on mutually clocked shift registers. Ciphers of this type have been extensively analysed, and published attacks on them will be infeasible against any instance of Heraclitus. The speed and security of Heraclitus makes it suitable as a session cipher, that is, an instance is generated at key exchange and used for one session.

An efficient least common subgraph algorithm for video indexing

Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a priori knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.

Algebraic attacks on clock-controlled stream ciphers

We present an algebraic attack approach to a family of irregularly clock-controlled bit-based linear feedback shift register systems. In the general set-up, we assume that the output bit of one shift register controls the clocking of other registers in the system and produces a family of equations relating the output bits to the internal state bits. We then apply this general theory to four specific stream ciphers: the (strengthened) stop-and-go generator, the alternating step generator, the self-decimated generator and the step1/step2 generator. In the case of the strengthened stop-and-go generator and of the self-decimated generator, we obtain the initial state of the registers in a significantly faster time than any other known attack. In the other two situations, we do better than or as well as all attacks but the correlation attack. In all cases, we demonstrate that the degree of a functional relationship between the registers can be bounded by two. Finally, we determine the effective key length of all four systems.

Mutually clock-controlled feedback shift registers provide resistance to algebraic attacks

Algebraic attacks have been applied to several types of clock-controlled stream ciphers. However, to date there are no such attacks in the literature on mutually clock-controlled ciphers. In this paper, we present a preliminary step in this direction by giving the first algebraic analysis of mutually clock-controlled feedback shift register stream ciphers: the bilateral stop-and-go generator, A5/1, Alpha 1 and the MICKEY cipher. We show that, if there are no regularly clocked shift registers included in the system, mutually clock-controlled feedback shift register ciphers appear to be highly resistant to algebraic attacks. As a demonstration of the weakness inherent in the presence of a regularly clocked shift register, we present a simple algebraic attack on Alpha 1 based on only 29 keystream bits.

CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams

Continuous sensor stream data are often recorded as a series of discrete points in a database from which knowledge can be retrieved through queries. Two classes of uncertainties inevitably happen in sensor streams that we present as follows. The first is Uncertainty due to Discrete Sampling (DS Uncertainty); even if every discrete point is correct, the discrete sensor stream is uncertain – that is, it is not exactly like the continuous stream – since some critical points are missing due to the limited capabilities of the sensing equipment and the database server. The second is Uncertainty due to Sampling Error (SE Uncertainty); sensor readings for the same situation cannot be repeated exactly when we record them at different times or use different sensors since different sampling errors exist. These two uncertainties reduce the efficiency and accuracy of querying common patterns. However, already known algorithms generally only resolve SE Uncertainty. In this paper, we propose a novel method of Correcting Imprecise Readings and Compressing Excrescent (CIRCE) points. Particularly, to resolve DS Uncertainty, a novel CIRCE core algorithm is developed in the CIRCE method to correct the missing critical points while compressing the original sensor streams. The experimental study based on various sizes of sensor stream datasets validates that the CIRCE core algorithm is more efficient and more accurate than a counterpart algorithm to compress sensor streams. We also resolve the SE Uncertainty problem in the CIRCE method. The application for querying longest common route patterns validates the effectiveness of our CIRCE method.

SCLOPE: an algorithm for clustering data streams of categorical attributes

Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorithm based on CLOPErsquos intuitive observation about cluster histograms. Unlike CLOPE however, our algo- rithm is very fast and operates within the constraints of a data stream environment. In particular, we designed SCLOPE according to the recent CluStream framework. Our evaluation of SCLOPE shows very promising results. It consistently outperforms CLOPE in speed and scalability tests on our data sets while maintaining high cluster purity; it also supports cluster analysis that other algorithms in its class do not.

An extended negative selection algorithm for anomaly detection

This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which do not make use of non-self data, the extended negative selection algorithm first acquires prior knowledge about the characteristics of the Problem space from the historial sample data by using machine learning techniques. Such data consists of both self data and non-self data. The acquired prior knowledge is represented in the form of production rules and thus viewed as common schemata which characterise the two subspaces: self-subspace and non-self-subspace, and provide important information to the generation of detection rules. One advantage of our approach is that it does not rely on the structured representation of the data and can be applied to general anomaly detection. To test the effectiveness, we test our approach through experiments with the public data set iris and KDDrsquo99 published data set.

Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization

The Operations Research (OR) community have defined many deterministic manufacturing control problems mainly focused on scheduling. Well-defined benchmark problems provide a mechanism for communication of the effectiveness of different optimization algorithms. Manufacturing problems within industry are stochastic and complex. Common features of these problems include: variable demand, machine part specific breakdown patterns, part machine specific process durations, continuous production, Finished Goods Inventory (FGI) buffers, bottleneck machines and limited production capacity. Discrete Event Simulation (DES) is a commonly used tool for studying manufacturing systems of realistic complexity. There are few reports of detail-rich benchmark problems for use within the simulation optimization community that are as complex as those faced by production managers. This work details an algorithm that can be used to create single and multistage production control problems. The reported software implementation of the algorithm generates text files in eXtensible Markup Language (XML) format that are easily edited and understood as well as being cross-platform compatible. The distribution and acceptance of benchmark problems generated with the algorithm would enable researchers working on simulation and optimization of manufacturing problems to effectively communicate results to benefit the field in general.

Finite-time consensus algorithm of multi-agent networks

This paper is concerned with leader-follower finite-time consensus control of multi-agent networks with input disturbances. Terminal sliding mode control scheme is used to design the distributed control law. A new terminal sliding mode surface is proposed to guarantee finite-time consensus under fixed topology, with the common assumption that the position and the velocity of the active leader is known to its neighbors only. By using the finite-time Lyapunov stability theorem, it is shown that if the directed graph of the network has a directed spanning tree, then the terminal sliding mode control law can guarantee finite-time consensus even under the assumption that the time-varying control input of the active leader is unknown to any follower.

An EM-based algorithm for clustering data streams in sliding windows

Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since the data arrive at a mining system in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model which requiring the elimination of outdated data must be dealt with properly. We propose SWEM algorithm that exploits the Expectation Maximization technique to address these challenges. SWEM is not only able to process the stream in an incremental manner, but also capable to adapt to changes happened in the underlying stream distribution.

Incremental and adaptive clustering stream data over sliding window

Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution’s changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.

Extracting common subtrees from decision trees

This paper explores an efficient technique for the extraction of common subtrees in decision trees. The method is based on a Suffix Tree string matching process and the algorithm is applied to the problem of finding common decision rules in path planning.

Video indexing and similarity retrieval by largest common subgraph detection using decision trees

While the largest common subgraph (LCSG) between a query and a database of models can provide an elegant and intuitive measure of similarity for many applications, it is computationally expensive to compute. Recently developed algorithms for subgraph isomorphism detection take advantage of prior knowledge of a database of models to improve the speed of on-line matching. This paper presents a new algorithm based on similar principles to solve the largest common subgraph problem. The new algorithm significantly reduces the computational complexity of detection of the LCSG between a known database of models, and a query given on-line.

A globally convergent MC algorithm with an adaptive learning rate

This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

An adaptive algorithm for finding frequent sets in landmark windows

We consider a CPU constrained environment for finding approximation of frequent sets in data streams using the landmark window. Our algorithm can detect overload situations, i.e., breaching the CPU capacity, and sheds data in the stream to “keep up”. This is done within a controlled error threshold by exploiting the Chernoff-bound. Empirical evaluation of the algorithm confirms the feasibility.